Si può desiderare di provare un trattamento naturale disfunzione erettile come un diverso per i problemi di costruzione. Al giorno d oggi ci sono diverse terapie sul mercato, ma un trattamento naturale disfunzione erettile è stato confermato qualche ora e ora di nuovo per dare risultati efficienti e permanenti. Cos è la disfunzione sessuale? L incapacità di sviluppare o sostenere una costruzione abbastanza lungo per fare l amore è chiamato disfunzione erettile, ED https://farmacia-senzaricetta.it/ o (maschio) problemi di erezione. Tutti gli uomini possono avere problemi di costruzione di volta in volta e gli scienziati considerano ED essere presenti se si verificano problemi di costruzione almeno il 25% del tempo. Alcuni fatti duri: ED Può essere dovuto a problemi emotivi. Stress, pressione, giltiness, depressione, bassa autostima e ansia prestazioni può essere la causa dei vostri problemi di costruzione. La ricerca ha confermato che il 90 per cento della disfunzione erettile è fisica in origine, non emotiva. L impotenza colpisce la maggior parte degli uomini durante la loro vita e può essere dovuto a troppo colesterolo, problemi cardiaci, diabete, ipertensione, fumo o alcol. Alcuni rimedi possono essere la ragione. Le questioni legate al movimento sono collegate. Se ti occupi dei tuoi problemi di movimento, hai piu possibilita di risolvere questo problema. Qui ci sono 5 consigli facili su come aumentare la circolazione: 1. Mangia i pasti giusti. Questo ti rendera il flusso sanguigno ovvio. Una grande parte di rimanere sani e anche mantenere il flusso sanguigno ovvio è legato al vostro piano di alimentazione quotidiana e quello che si mangia. Una buona cura per la disfunzione erettile è mangiare un piano a basso contenuto di grassi e grande alimentazione di fibre. Mangiare fibre tutti i giorni e questo viene scoperto in prodotti cerealicoli cereali integrali, frutta e verdura. Evitare il più possibile pasti pronti o pasti non sani. 2. Wonder herbal rimedi. Molti rimedi vegetali per ED eseguire bene come possono migliorare il movimento. Hanno molto meno reazioni avverse rispetto ai farmaci convenzionali e si svolgono in modo efficiente per migliorare hardons e la forza, troppo. Erbe naturali come Ginkgo Biloba sono utilizzati come una strategia per ED. Gli specialisti di erboristeria credono anche che le spezie o le erbe come noce moscata, portano al movimento intorno al corpo, tra cui il pene. 3. Vitamine naturali vitali. Gli scienziati sanitari hanno scoperto che una mancanza di supplemento è tipico tra gli uomini con ED in particolare vitamina A. Se si ha una mancanza del nutriente ossido di zinco, Questo è stato confermato per portare alla disfunzione erettile. Queste inadeguatezze derivano dal fatto che molti valori nutrizionali in quello che mangiamo piano non sono sufficienti. Aggiungere al vostro fabbisogno di nutrienti aumenterà la circolazione del sistema e migliorare questa condizione. Gli integratori alimentari sono completamente naturali, quindi non dovrete preoccuparvi dei rischi di reazioni avverse. Inoltre, queste vitamine naturali sono utili per il vostro benessere over-all. Oltre a questi vantaggi benessere, disfunzione erettile vitamine naturali e integratori costano molto meno di farmaci rimedi. 4. Esercitare. Fai una mossa e non un tablet vibrante. Camminare farà di più per migliorare e sostenere hardons di qualsiasi altra compressa chimica nel lungo periodo. Il fitness fisico manterrà bassi livelli di pressione e mantenere grandi stadi di movimento. Andando per un 20-30 minuti di movimento rapido ogni giorno, può affrontare questo problema e può sostenere la vostra libido senza l uso di qualsiasi farmaco. 5. Sottolineare. Questo è il peggior attaccante per problemi di erezione. Scopri diversi metodi per riposare. Alcuni metodi tipici per riposare includono la lettura di un libro, la meditazione, un bagno rilassante o allenamenti di respirazione. Sto solo imparando alcuni semplici allenamenti di respirazione che possono migliorare significativamente il movimento nel reparto pantaloni. Una naturale disfunzione erettile soluzioni di trattamento stanno diventando sempre più popolare con gli uomini. Questi rimedi a base di erbe sono preferiti perché non hanno reazioni avverse e sono confermati essere efficiente come il farmaco. La maggior parte degli uomini combattere parlano dei loro problemi, in particolare la disfunzione erettile come c è poca discussione sui problemi di erezione. La verita e che ED ha un impatto su piu di dieci milioni di uomini solo negli Stati Uniti. Non siete soli e l aiuto è disponibile.
Microsoft word - intentionalqueryexpansion_0501_cr-copy_changes_off_ms.doc
Intentional Query Suggestion: Making User Goals More Explicit During Search ABSTRACT
~25% of queries have a clear navigational intent, and up to ~75%
The degree to which users’ make their search intent explicit can
of queries need to be understood as informational or transactional
be assumed to represent an upper bound on the level of service
queries, meaning they are not directed towards a specific set of
that search engines can provide. In a departure from traditional
expected documents. Recent studies even estimate more drastic
query expansion mechanisms, we introduce
ratios [13]. While users crafting informational or transactional
search queries often have a high level search intent (“plan a trip to
Suggestion as a novel idea that is attempting to make users’ intent
Europe”), in many situations they have no clear idea or knowledge
more explicit during search. In this paper, we present a prototypical algorithm for Intentional Query Suggestion and we
about the specific documents they expect to retrieve. This makes
discuss corresponding data from comparative experiments with
it difficult for users to craft successful queries and makes query
traditional query suggestion mechanisms. Our preliminary results
suggestion a particularly important and challenging problem.
indicate that intentional query suggestions 1) diversify search
In this paper we are interested in exploring the following question:
result sets (i.e. it reduces result set overlap) and 2) have the
What if search engines would, rather than letting users guess
potential to yield higher click-through rates than traditional query
arbitrary words from the set of documents they are expected to
retrieve, encourage users to tell them their original search intent in a more unambiguous and natural way? In other words, what if
Categories and Subject Descriptors
search engines would encourage users to make their search intent
H.1.2 [User/Machine Systems]: Human Factors; H.3.3
more explicit (e.g. “buy a car”) rather than formulating their query
[Information Storage and Retrieval]: Query Formulation,
in a rather artificial manner (“car dealership”)? In future search
interfaces (such as audio search interfaces for cell phones or natural language search interfaces), current mechanisms for query
General Terms
suggestion might become inadequate and natural language search
Algorithms, Human Factors, Experimentation
queries might play a more important role. This work is interested
Keywords
in understanding how current search methods would cope with such a development.
For this purpose, we introduce and study a novel approach to
query suggestion: Intentional Query Suggestion or query suggestion by user intent. While traditional query suggestion often
1. INTRODUCTION
aims to make a query resemble more closely the documents a user
In IR literature, the purpose of query suggestion has often been
is expected to retrieve (which might be unknown to the user), we
described as the process of making a user query resemble more
want to study an alternative: expanding queries to make searchers’
closely the documents it is expected to retrieve ([26]). In other
words, the goal of query suggestion is commonly understood as maximizing the similarity between query terms and expected
To give an example: In traditional query suggestion, a query “car”
documents. The task of a searcher then is to envision the expected
might receive the following suggestions: “car rental”, “car
documents, and craft queries that reflect their contents.
insurance”, “enterprise car rental”, “car games” (actual suggestions produced by Yahoo.com on Nov 27th 2008).In query suggestion
However, research on query log analysis suggests that many
based on explicit user intent, the suggestions could be “buy a car”,
queries exhibit a lack of user understanding about the specific
“rent a car”, “sel your car”, “repair your car” (see Table 1 for
documents users expect to retrieve. Broder [6] found that only
examples). We can speculate that in innovative search interfaces (such as audio search interfaces), such suggestions would be
easier to verify with a user than verifying traditional query
Permission to make digital or hard copies of all or part of this work for
suggestions (e.g. “Do you want to: buy a car OR sell a car OR …?”).
personal or classroom use is granted without fee provided that copies are
not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. WSCD’09 at WSDM’09, February 9, 2009, Barcelona, Spain. Copyright 2009 ACM 1-58113-000-0/00/0004…$5.00.
Table 1: Comparison of suggested queries provided by
query and expand it based on a better understanding of the
Yahoo!, MSN and Intentional Query Suggestion.
corresponding information need – thereby aiming to make user intent more explicit.
We define Intentional Query Suggestion as the incremental
process of transforming a query into a new query based on
intentional structures found in a given domain, in our case: a
search query log. An initial query is replaced by the most probable
intentions that underlay the query. To give an example: for the
query “playground mat”, an Intentional Query Suggestion
mechanism might suggest the following 5 user intentions: “buy
full tilt poker, free learn to play poker,
playground equipment”, “build a swing set”, “covering dirt in a
of poker, party poker games, free buy poker table,
playground”, “buy children plastic slides”, “raise money for our
In our case, we extract the proposed intentions from search query
logs, but they could potentially be extracted from other knowledge
house plans, white house TV show, insure my house,
bases containing common human goals as well, such as
house, house of houses for sale, sell your house,
ConceptNet [19] or others. To the best of our knowledge, the
fraser, columbia houses for rent, make offer on house, house of house plans, house house, buy house
application of explicit search intent [24] to query suggestion
represents a novel idea that has not been studied yet.
3. EXPERIMENTAL SETUP
We are interested in studying the effects of this idea on the search
In traditional query suggestion, an initial query formulation is
result sets obtained from experiments with a current search engine
replaced by some other query that refines, disambiguates or
provider. In particular, we are interested in seeking answers to the
clarifies the original query. In our approach, the initial query is
following questions: How do today’s search engines deal with
replaced by a query that exhibits a higher degree of intentional
queries that contain explicit user goals? How would queries
explicitness, meaning that it makes user intent more explicit [24].
expanded by user intent influence search results and click Definition: We define this replacement as query suggestion based on user intent. The suggested queries can be considered to
This paper introduces Intentional Query Suggestion as a novel
represent Intentional Query Suggestions whenever they 1) contain
type of query suggestion. Specifically, this paper 1) introduces a
at least one verb and 2) describe a plausible state of affairs that
definition of Intentional Query Suggestion 2) presents a
the user may want to achieve or avoid (cf.) in 3) a recognizable
preliminary algorithm to perform intentional query suggestion
based on historic query log data and 3) discusses experimental
We developed a parametric algorithm that executes the function
results and potential implications for future research on search
f(q) → RQE = {qe,1, qe,2 … qe,k}, mapping implicit intentional
queries (length <= 2) to a set of potential explicit intentional query
suggestions (e.g. “car” → “buy a car”, “rent a car”, “repair my car”).
2. QUERY SUGGESTION The general idea of query suggestion is to support the searcher in 3.1 Datasets
formulating queries that have a better chance to retrieve relevant
The MSN Search query log excerpt contains about 15 million
documents [21], [3]. Methods offered to expand queries can be
queries (from US users) that were sampled over one month in
divided into two major categories. Global methods employ entire
May, 2006. The search query log data is split into two files, one
document collections or external sources such as thesauri as
file containing attributes Time, Query, QueryID and ResultCount, the
corpora for producing suggestions. Local methods reformulate the
other one attributes QueryID, Query, Time, URL and Position
initial query based on the result set it has retrieved. Relevance
providing click-through data. The queries were modified via the
feedback represents another query reformulation strategy in which
following normalization steps (i) trimming of each query, and (ii)
a searcher is involved by marking retrieved documents as relevant
space sequence reduction to one space character. Queries and
or not. Global as well as local methods aim to eventually move
corresponding click-through data containing adult content were
the initial query closer to the entire cluster of relevant documents.
filtered out (and were not taken into account in our study).
A set of ~46.000 explicit intentional queries was extracted from the MSN Search Asset Data Spring 2006 applying the algorithm
2.1 Intentional Query Suggestion
described in [24]. The resulting set has an estimated precision of
While traditional query suggestion techniques aim at narrowing
77% of explicit intentional queries (based on the evaluations
the gap between the initial query and the set of relevant
reported in [25]) and represents our knowledge base for
documents, we seek to approximate the user’s intentions behind a
Intentional Query Suggestion. We call this subset of queries the
Explicit Intentional Query Dataset from here on.
1 Related query suggestion results from Yahoo!
Our parametric algorithm for Intentional Query Suggestion approximates the searcher’s intent by combining two different yet
2 Related query suggestion results from MSN
complementary approaches, i.e. text-based Intentional Query
Suggestion (see Section 3.2) and neighborhood-based Intentional
We define the neighborhood of an explicit intentional query q as
Query Suggestion (see Section 3.3). The two approaches can be
N(qe, Pd), where the parameter Pd determines the number of
combined yielding a ranked list of potential intentional query
queries that are considered before and after the query q . The
neighborhood N(qe, Pd) contains 2 * Pd queries where q ϵ QU
holds. Queries qi ϵ N(qe, Pd) are processed to serve as tags
(dimensions of the characteristic vector describing explicit
3.2 Text-Based Intentional Query Suggestion
intentional queries) for the corresponding intentional query q .
In the text-based approach, the tokens of input queries are
After stop words have been removed, the remaining tokens are
textually compared to all query tokens in the Explicit Intentional
combined into a set of words and form a tag set T(qe)={t1, t2 … tm}
Query Dataset. We experimented with several text-based
of the explicit intentional query q . In addition to parameter P
similarity measures including Cosine Similarity, Dice Similarity,
introduce the parameter Pi that denotes the intersection size
Jaccard Similarity and Overlap Similarity [11], [3]. Because the
between explicit intentional queries and neighboring queries. This
similarity measures did not exhibit significant differences, we
parameter can be considered as a quality filter. Tokens of one
decided on using Jaccard Similarity throughout our experiments
query are only admitted to the tag set T(qe) if the query shares at
for reasons of simplicity. In text-based intentional query
least Pi tokens with qe. Let qe be “lose weight fast”, qu be “weight
suggestion, we calculate Jaccard Similarity in the following way:
loss supplements” and Pi = 1: qe and qu share one common term
(“weight”). Consequently, the tokens of q
qe, i.e. T(qe) = {“weight”, “loss”, “supplement”}.We suspect this
parameter to be related to the quality of the tags admitted to the
tag set and consequently related to the quality of the entire model.
A and qB are the respective token sets representing two
This yields a characteristic vector of tags for each explicit intentional query based on session-neighborhood.
Figure 1 shows a bipartite graph that was partly generated from
3.3 Neighborhood-Based Intentional Query
the query log excerpt in Table 2 with a parameter setting Pd = 3
Suggestion
and Pi = 1. The graph illustrates relations between explicit
In addition to Intentional Query Suggestion based on text, we are
intentional queries and meaningful terms in the session
using a similarity construct based on query log session
neighborhood, representing characteristic term vectors for explicit
neighborhood. This has the potential to include behavioural
intentional queries. The example also shows that the
intentional structures in our algorithm. For that purpose, we are
neighborhood-based approach is agnostic to misspellings. The
conceptualizing query logs as consisting of two types of nodes (a
bipartite graph is useful in at least two ways: Bottom-up, it can
bipartite graph), where nodes of one type correspond to
help to produce intentional query suggestions based on co-
intentional queries and nodes of the other type correspond to
occurrence (e.g. “upplements” → “lose weight fast”). Top-down, the graph can help to transform explicit intentional queries into
implicit intentional queries. We construct a bipartite graph based on session proximity between these two types of nodes. Thereby,
implicit ones (which is not further pursued in this paper). Note
we use neighboring queries to further describe and characterize
that qu,3 and qu,4 both represent explicit intentional queries and are
explicit intentional queries, building characteristic term vectors
therefore neglected in the graph generation process.
for explicit intentional queries. In the following, we introduce the parametric algorithm for intentional query expansion in a more formal way.
Table 2: Search query log excerpt illustrating the explicit intentional query qe,1 and its neighborhood N(qe,1, 3). Figure 1: Bipartite graph partly generated from search query log excerpt in Table 2 with parameter setting P d=3 and Pi=1.
Similarity between an input query (“upplements”) and a number of
explicit intentional queries (“lose weight fast”) can now be
calculated with traditional similarity metrics. Again, we
experimented with different similarity measures and opted for the
Jaccard similarity measure due to insignificant differences
between the measures. In neighbourhood-based intentional query
suggestion, we calculate Jaccard similarity in the following way:
T (q ) ∩ T (q )
T (q ) ∪ T (q )
1, q2 … qn} denote the set of n queries in a search query
log. Q consists of two disjoint sets QE={qe,1, qe,2 … qe,s} and
where T(qA) and T(qB) are the respective token sets representing
U={qu,1, qu,2 … qu,t } so that Q = QE ∪ QU
represents the set of explicit intentional queries, such as “lose weight fast”, and QU the neighboring implicit intentional queries
such as “weight loss supplements” as illustrated in Table 2.
3.4 Query Suggestion based on User Intent
suggestions, i.e. query suggestions that were assigned to relevance
When input queries are processed by our algorithm, both
class 1. Achieved precision values are illustrated in Table 3.
similarity measures are calculated. In our approach, a linear
Table 3: Precision values of our algorithm as rated by three
combination determines the overall similarity between an input
human annotators (X, Y and Z).
query and every explicit intentional query in our dataset yielding a ranked list of potential user intentions. The parameter α defines
S (q , q ) = α * S (q , q ) + 1( − α ) * S (q , q )
The average precision amounts to 0.71, i.e. in seven out of ten cases the algorithm returns a potential user intention.
In this work we do not intend to identify an optimized parameter set to generate the model. We rather chose a simple parameter set
In addition, we calculated the inter-rater agreement κ [8] between
for the purpose of seeking answers to the exploratory questions of
all pairs of human subjects X, Y, and Z. Cohen’s κ measures the
this paper. Future work might explore the utility of parameter
average pair-wise agreement corrected for chance agreement
when classifying N items into C mutually exclusive categories. Cohen’s κ formula reads:
The parametric algorithm for Intentional Query Suggestion can be
P(O) − P(C)
d = 3, Pi = 1 and α = 0.5 in our experiments.
where P(O) is the proportion of times that a hypothesis agrees
An evaluation of the selected model is provided in Section 3.5.
with a standard (or another rater), and P(C) is the proportion of
times that a hypothesis and a standard would be expected to agree by chance. The κ value is constrained to the interval [-1,1]. A κ-
3.5 Evaluation
value of 1 indicates total agreement, 0 indicates agreement by
We conducted a user study to learn more about the quality of
chance and -1 indicates total disagreement. Table 4 shows the
intentions that were suggested by our algorithm. Annotators were
achieved κ-values in our human subject study.
asked to categorize the 10 top-ranked suggested explicit
intentional queries for 30 queries into one of the following two
Table 4: Kappa values amongst three annotators (X, Y and Z) for the two relevance classes. Relevance Classes: (1) Potential User Intention: the suggested query represents
Cohen’s Kappa (κ) 0.6416 0.5125 0.6703
a plausible intention behind a short query.
The κ-values (see Table 4) range from 0.51 to 0.67 (0.61 on
“anime” “draw anime”, “draw manga”
average) containing two values above 0.6 indicating some level of
“playground mat” “buy playground equipment”, “build a swing set”
or the suggested query represents an unlikely yet still related user intention as illustrated by following examples:
4. PRELIMINARY RESULTS
In this section we discuss two potential implications of Intentional Query Suggestion for web search: First, diversity of search results
“Boston herald” “getting around Boston”, “sightseeing in
has recently gained importance in web search [9]. For example in
“ginseng coffee” “moving coffee stains”, “fix my keyboard”
informational queries, web search results should not provide monolithic search result sets but rather cover as many different
aspects (topics) as possible. We are interested in exploring the
(2) Clear Misinterpretation: the suggested query has no
influence of explicit intentional queries on the diversity of search
relation with the initial query. Suggestions that do not
result sets. If result sets of explicit intentional queries would be
conform to our definition (see Section 3) are assigned this
more diverse, Intentional Query Suggestion could help to better
focus and guide searchers’ intent in exploratory searches.
Second, click through rates have been frequently used as a proxy
“Boston herald” “care for Boston fern”, “flying to Nantucket”
for measuring relevance in large document collections (cf. [10]).
“playground mat” “raise money for our playground”, “weave a
We are interested in studying whether explicit intentional queries
would yield other/better click-through rates than implicit
intentional queries. If explicit intentional queries would yield higher click-through rates, making user intent more explicit would
30 queries of length 1 or 2 were randomly drawn from the MSN
represent an interesting new mechanism to improve search engine
search query log. The prospective queries were filtered with
regard to (i) reasonableness, i.e. discarding queries such as “wiseco” or “drinkingmate” and to (ii) non American raters, i.e.
4.1 Influence on Diversity of Search Results
discarding queries such as “target” or “espn”.
We examine the diversity within search results by calculating the
In order to evaluate intentional query suggestions that are
intersection size between different URL result sets produced by
provided by our algorithm, we calculated the percentage of correct
different/same query suggestion mechanisms. Two experiments were conducted, seeking answers to the following questions:
Table 6: Average intersection sizes for URL sets expanded by
(i) Intersection between different Query Suggestion Yahoo! Suggestions and Intentional Query Suggestion. Mechanisms: How many URLs (top level domains only) intersect between URL result sets retrieved by 1) the
Compared URL result sets Average Intersection
original queries, 2) the corresponding Yahoo! expanded
queries and 3) the corresponding intentional query suggestions?
(ii) Intersection within same Query Suggestion Mechanisms: How many URLs (top level domains only) intersect between result sets that were retrieved by
Considering the presented results, we can speculate that search
different query suggestions (produced by the same
processes could be made more focused if the searchers’ intention
query suggestion mechanism) regarding one original
is explicitly included in the search process. It appears that
intentional query suggestions diversify search results and cover a
wider range of topics than Yahoo!’s suggestions.
400 queries of length 1 or 2 were randomly drawn from the MSR search query log. Following constraints were made: original
queries (i) should yield at least 10 suggestions by our algorithm,
4.2 Influence on Click-Through
(ii) should not contain misspellings and (iii) must not be ‘adult’
To study the influence of explicit intentional queries on click
phrases. For each selected query, the top 10 suggestions were
through, we analyzed the number of click-through events for
produced by using the Yahoo! API and by the Intentional Query
different token lengths. We obtained the click-through numbers
Suggestion algorithm. We processed the top 50 result URLs for
for different token lengths in the MSR query dataset and created
each suggestion, totalling 500 URLs per selected query. Searches
the following token length bins: one token queries, two token
were conducted by applying the Yahoo! BOSS API3. In order to
queries, three to four token queries, five token queries, six to ten
compare the original query results with both expanded results sets,
token queries and queries consisting of more than ten tokens
500 resulting URLs are retrieved for every original query. For
(excluding explicit intentional queries). Five token queries were
each query, we calculated how many URLs are shared on average
of particular interest, since the average length of queries in our
between the URL result sets taking into account only unique
Explicit Intentional Query Dataset amounts to 5.33 tokens. For
URLs as well as only top level domains of the resulting set.
each category, a random sample of 5,000 queries was drawn from
Again, we used Jaccard as a metric for intersection/similarity. The
the MSN search query log and all corresponding click-through
averaged results over all candidate queries are shown in Table 5.
events were registered and counted. Table 7 shows the number of
Table 5: Average intersection sizes for URL sets of original
click through events for each bin and also for the set of explicit
queries and their corresponding suggestions. Compared URL result sets Avg.Inter- Table 7: Click-through distribution for different query lengths and explicit intentional queries
Original Queries vs. Intentional Suggestions
Yahoo! Suggestions vs. Intentional Suggestions
855,649 358,327 64,313 5,559 2,728 960 7,236
The results in Table 5 imply that original query results share more
URLs with results from Yahoo! expanded queries than with
It can be observed that explicit intentional queries appear to have
results yielded by queries that reflect potential user intent. This
a ~ 30% higher number of click through events (#click-through =
suggests that if queries are expanded by user intent more diverse
7,236) than implicit intentional queries of comparable length
result sets can be achieved. In addition, we calculated the inner
(length 5, #click-through = 5,559). The higher click-through
intersection size of the result sets, i.e. the overlap between
numbers of explicit intentional queries suggest that such queries
different result sets produced by the same suggestion mechanism.
retrieve more relevant results, which appears to be an interesting
The results were again averaged over all queries and are shown in
finding and preliminary evidence for the potential utility of
The results in Table 6 suggest that queries expanded by Yahoo! yield more overlapping URLs than queries expanded by user
5. RELATED WORK
intent. These results suggest that queries that express a specific
Two areas of research are particularly relevant to our work:
intention lead to more diverse results than queries that attempt to
Studies of search intent in query logs and query suggestion.
approximate the expected document content to retrieve.
Studies of search intent in query logs: Peter Norvig discussed4 search intent as one of the outstanding problems in the future of search. One interpretation of understanding the users’ needs is to
3 http://developer.yahoo.com/search/boss/
4 Interview in the Technology Review (Monday, July 16, 2007)
understand the intentions behind search queries. Intentional query
6. CONCLUSIONS
suggestions could be regarded as a first step in this direction by
While there is a significant body of research on understanding
helping users to make their search intent more explicit. In
user intent during search ([6], [23], [13], [5], [18], [10], [7]), to
previous years, several different definitions of user intent emerged
the best of our knowledge, the application of user intent to query
[6], [10], [12],[25]. Broder [6] for example introduced a high level
suggestion is a novel idea which has not been studied yet. In this
taxonomy of search intent by categorizing search queries into
paper, we introduce and define the concept of Intentional Query
three categories: navigational, informational and transactional.
Suggestion and present a prototypical algorithm as first evidence
This has stimulated a series of follow up research on automatic
for the feasibility of this idea. In a number of experiments,
query categorization by [18], [13], [15], [12] and [23]. Evolutions
we.could highlight interesting differences to traditional query
of Broder’s taxonomy include collapsing categories, adding
suggestion mechanisms: 1) Differences in the diversity of search
categories [5] and/or focusing on subsets only [18]. In contrast to
results. Our results suggest that intentional query expansions can
Broder, we do not incorporate high-level categories of search
be used to diversify result sets. One implication of this finding is
intent but rather focus on instances of user intentions
that search engine vendors might be able to make search processes
(informational vs. “things to consider when buying a car”).
more focused if the searchers’ intention is explicitly included in
He et al. [12] used syntactic structures, i.e. verb-object pairs, to
the search process. 2) Different click-through distributions for
classify queries into Broder’s categories. In a similar way,
explicit intentional queries. Our experiments showed a higher
Strohmaier et al. [25] employed part-of-speech trigrams as
click-through ratio for explicit intentional queries compared to
features to extract instances of user intentions in search query
implicit intentional queries of similar length. The higher click-
logs. In this paper, user intent is understood as a certain type of
through numbers suggest that such queries retrieve more relevant
verb phrases that explicitly state the user’s goal. Downey et al
results. This interesting finding might inspire novel ways to
[10] view the information seeking process differently: Actions
approach query suggestion in the future.
that follow a search query are proposed as characterizations of the
Our results could be relevant for a number of currently open
searcher’s information goal. The last URL visited in a search
research problems. 1) Query disambiguation: Similar to Allan [2],
session serves as a proxy for the user intent. While their approach
where the problem of query disambiguation was approached by
is useful to study user behavior during search sessions, it can not
posing questions, Intentional Query Suggestion could provide a
easily be used in an interactive way - to enable users to make their
mechanism to identify the original user goal during search. 2)
Search intent: A better understanding of the user’s intent could
In addition to studies of user intent, research on query suggestion
give search engine vendors a better picture of users’ needs. In the
is related to our work as well. Query expansion [27], query
long run, approximating user intent could help making search
substitution [14], query recommendation [4] and query refinement
more focused and prevent topic drift. 3) Search session: Along
[17] are different concepts that share a similar objective:
with a better understanding of users’ search intent, new, more
transforming an initial query into a ‘better’ query that is capable
useful definitions of search sessions might be necessary. New
of satisfying the searcher’s information need by retrieving more
definitions could differ from existing definitions by, for example,
relevant documents. We deviate from these traditional approaches
putting emphasis on a set of coherent, goal-related queries rather
that focus on query vs. expected documents by focusing on
than time-based notions, where multi-tasking behavior of users is
queries and potential user intentions. Xu et al. [27] for example
hard to capture. 4) Evaluation: Kinney et al. [16] point out the
employed local and global documents in query expansion by
difficulty of finding expert annotators when it comes to annotating
applying the measure of global analysis to the selection of query
web search results for evaluation purposes. In order to alleviate
terms in local feedback. Query suggestion is closely related to
the annotation task, the authors proposed statements that
query substitution as well where the original query is extended by
described the user intent behind a query. Intentional query
new search terms to narrow the search scope. Jones et al. [14]
suggestion might serve as a link between plain queries and the
investigated a query substitution mechanism that does not exhibit
intent statements by offering a list of empirically-grounded,
query drift which represents a common drawback of query
expansion techniques. The authors make use of search query
sessions to infer relations between queries. Baeza-Yates et al. [4] proposed an approach that suggests related
7. ACKNOWLEDGMENTS
queries based on query log data and clustering. Former queries
We would like to thank Microsoft Research for providing the
were transformed into a new term-vector representation by taking
search query log and Peter Prettenhofer for his support in
into account the content of the clicked URLs. Another approach
extracting the Explicit Intentional Query Dataset. This work is
reported in [17] employed anchor texts for the purpose of query
funded by the FWF Austrian Science Fund Grant P20269
refinement. It is based on the observation that queries and anchor
TransAgere. The Know-Center is funded within the Austrian
texts are highly similar. Query transformation techniques have
COMET Program under the auspices of the Austrian Ministry of
already spread to other areas such as question answering [1].
Transport, Innovation and Technology, the Austrian Ministry of Economics and Labor and by the State of Styria. COMET is
Work on query suggestion has recently been done by [20], [22].
managed by the Austrian Research Promotion Agency FFG.
Both papers apply their algorithms on bipartite graphs (user - query and/or query - URL) that were generated from search query
logs. In a similar way, our work generates a bipartite graph from a search query log. However, our approach focuses on explicit
8. REFERENCES
intentional queries and their implicit intentional query
[1] Agichtein E., Lawrence S. and Gravano L. Learning search
neighborhood, thereby focusing on explicit user intent rather than
engine specific query transformations for question
the generation of syntactic or semantic query suggestions.
answering. In'WWW '01: Proceedings of the 10th
International Conference on World Wide Web', ACM, New
[15] Kang, I. and Kim, G. Query type classification for web
document retrieval. In'SIGIR '03: Proceedings of the 26th
Annual International ACM SIGIR Conference on Research
Allan J. and Raghavan H. Using part-of-speech patterns to
and Development in Information Retrieval', ACM, New
International ACM SIGIR Conference on Research and
Development in Information Retrieval', ACM Press New
[16] Kinney, K. A., Huffman, S. B. and Zhai, J. How evaluator
domain expertise affects search result relevance judgments.
Baeza-Yates R. and Ribeiro-Neto B. Modern Information 'CIKM '08: Proceedings of the 17th ACM Conference on
Information and Knowledge Management', ACM, New
[4] Baeza-Yates R., Hurtado C.A. and Mendoza M. Query
recommendation using query logs in search engines. In
[17] Kraft, R. and Zien, J. Mining anchor text for query
Lindner W., Mesiti M., Türker C., Tzitzikas Y. and Vakali
refinement. In'WWW '04: Proceedings of the 13th
A., 'EDBT Workshops', Springer, pp. 588--596, 2004.
International Conference on World Wide Web', ACM, New York, NY, USA, pp. 666--674, 2004.
[5] Baeza-Yates R., Calderón-Benavides L. and González-Caro
[18] Lee, U., Liu, Z. and Cho, J. Automatic identification of user
and Information Retrieval, pp. 98--109, 2006.
'WWW '05: Proceedings of the 14th
International Conference on World Wide Web', ACM, New
[6] Broder A. A taxonomy of web search. In ACM SIGIR Forum
[19] Liu, H. and Singh, P. 'ConceptNet — A practical
[7] Chang Y., He, K., Yu S. and Lu, W. Identifying user goals
commonsense reasoning tool-kit'. In BT Technology Journal
from web search results. In 'WI '06: Proceedings of the 2006
IEEE/WIC/ACM International Conference on Web Intelligence', IEEE Computer Society, Washington, DC,
[20] Ma, H., Yang, H., King, I. and Lyu, M. R. Learning latent
semantic relations from clickthrough data for query suggestion. In'CIKM '08: Proceedings of the 17th ACM
[8] Cohen, J. A coefficient of agreement for nominal scales. In
Conference on Information and Knowledge Management',
Educational and Psychological Measurement 20(1), 37,
ACM, New York, NY, USA, pp. 709--718, 2008.
[21] Manning, C. D., Raghavan, P. and Schütze, H. Introduction
[9] Crabtree, D. W., Andreae, P. and Gao, X. Exploiting
to Information Retrieval, Cambridge University Press, 2008.
underrepresented query aspects for automatic query expansion. In
[22] Mei, Q., Zhou, D. and Church, K. Query suggestion using
'KDD '07: Proceedings of the 13th ACM
SIGKDD international conference on Knowledge Discovery
hitting time. In'CIKM '08: Proceedings of the 17th ACM
and Data Mining', ACM, New York, NY, USA, pp. 191--
Conference on Information and Knowledge Management',
ACM, New York, NY, USA, pp. 469--478, 2008.
Downey, D., Liebling, D. and Dumais, S. Understanding the
Rose, D. E. and Levinson, D. Understanding user goals in
relationship between searchers, queries and information
web search. In'WWW '04: Proceedings of the 13th
goals. 'CIKM '08: Proceedings of the 17th ACM Conference
International Conference on World Wide Web', ACM, New
on Information and Knowledge Management', ACM, New
[24] Strohmaier, M., Prettenhofer, P. and Lux, M. Different
degrees of explicitness in intentional artifacts - studying user
Ferber, R. Information Retrieval, Dpunkt.Verlag, ISBN 978-
goals in a large search query log. In 'CSKGOI'08: Proceedings of the Workshop on Commonsense Knowledge
[12] He, K., Chang, Y. and Lu, W. Improving identification of
and Goal Oriented Interfaces, in conjunction with IUI'08',
latent user goals through search-result snippet classification.
In'WI '07: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence', IEEE Computer Society,
[25] Strohmaier, M., Prettenhofer, P. and Kröll, M. Acquiring
Washington, DC, USA, pp. 683--686, 2007.
explicit user goals from search query logs. In'International Workshop on Agents and Data Mining Interaction ADMI'
[13] Jansen, B. J., Booth, D. L. and Spink, A. Determining the
informational, navigational, and transactional intent of web queries. In
[26] Strzalkowski, T. and Carballo, J. Natural Language
Inf. Process. Manage. 44(3), pp. 1251--1266,
Information Retrieval: TREC-5 Report. In'Text REtrieval Conference', pp. 164--173, 1998.
[14] Jones, R., Rey, B., Madani, O. and Greiner, W. Generating
[27] Xu, J. and Croft, W. B. Query expansion using local and
'WWW '06: Proceedings of the 15th
International Conference on World Wide Web', ACM, New
global document analysis. In'SIGIR '96: Proceedings of the
19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval', ACM, New York, NY, USA, pp. 4--1, 1996
Se dice que “ las depresiones son un conjunto de enfermedades psíquicas hereditarias o adquiridas, con una sintomatología determinada, a la que se asocian cambios negativos de tipo somático, psicológico, conductual, cognitivo La Organización Mundial de la Salud (OMS), con base en el aumento de la depresión expone que ésta afecta hasta un 7.5% de los varones frente al 16% de las
SELECTION OF SUSCEPTIBILITY TEST BROTH Use Sensititre CAMHBT for rapid growing mycobacteria, Nocardia and other aerobic Actinomycetes or Mueller Hinton broth with OADC for slow growing mycobacteria. Sensititre broths are performance tested for use with SENSITITRE® Broth Microdilution (MIC) Method: INOCULATION AND INCUBATION For Rapidly Growing Mycobacteria (RGM), Slowly