Personalized click model through collaborative filtering

Si, Shubin; Hu, Botao; Chen, Weizhu; Yang, Qiang

doi:10.1145/2124295.2124336

Cited by 51 publications

(34 citation statements)

References 21 publications

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“…These include the personalized click model [30], the task-centric click model [37], intent-aware modifications of UBM and DBN [9], the federated click models [6], the vertical-aware click model [33], the content-aware click model [34], and noise-aware modifications of UBM and DBN [7].…”

Section: Click Modelsmentioning

confidence: 99%

A Neural Click Model for Web Search

Borisov

Markov

Rijke

et al. 2016

Proceedings of the 25th International Conference on World Wide Web

131

121

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A neural click model for web searchBorisov, A.; Markov, I.; de Rijke, M.; Serdyukov, P. General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. ABSTRACTUnderstanding user browsing behavior in web search is key to improving web search effectiveness. Many click models have been proposed to explain or predict user clicks on search engine results. They are based on the probabilistic graphical model (PGM) framework, in which user behavior is represented as a sequence of observable and hidden events. The PGM framework provides a mathematically solid way to reason about a set of events given some information about other events. But the structure of the dependencies between the events has to be set manually. Different click models use different hand-crafted sets of dependencies.We propose an alternative based on the idea of distributed representations: to represent the user's information need and the information available to the user with a vector state. The components of the vector state are learned to represent concepts that are useful for modeling user behavior. And user behavior is modeled as a sequence of vector states associated with a query session: the vector state is initialized with a query, and then iteratively updated based on information about interactions with the search engine results. This approach allows us to directly understand user browsing behavior from click-through data, i.e., without the need for a predefined set of rules as is customary for PGM-based click models.We illustrate our approach using a set of neural click models. Our experimental results show that the neural click model that uses the same training data as traditional PGM-based click models, has better performance on the click prediction task (i.e., predicting user click on search engine results) and the relevance prediction task (i.e., ranking documents by their relevance to a query). An analysis of the best performing neural click model shows that it learns similar concepts to those used in traditional click models, and that it also learns other concepts that cannot be designed manually.

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Section: Click Modelsmentioning

confidence: 99%

A Neural Click Model for Web Search

Borisov

Markov

Rijke

et al. 2016

Proceedings of the 25th International Conference on World Wide Web

131

121

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“…We adopt a user browsing model very similar to [7,17,33]. The user starts by looking at the first result in the list and gives feedback on it.…”

Section: Browsing and Feedback Modelmentioning

confidence: 99%

Actively soliciting feedback for query answers in keyword search-based data integration

et al. 2013

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The problem of scaling up data integration, such that new sources can be quickly utilized as they are discovered, remains elusive: global schemas for integrated data are difficult to develop and expand, and schema and record matching techniques are limited by the fact that data and metadata are often under-specified and must be disambiguated by data experts. One promising approach is to avoid using a global schema, and instead to develop keyword searchbased data integration -where the system lazily discovers associations enabling it to join together matches to keywords, and return ranked results. The user is expected to understand the data domain and provide feedback about answers' quality. The system generalizes such feedback to learn how to correctly integrate data.A major open challenge is that under this model, the user only sees and offers feedback on a few "top-k" results: this result set must be carefully selected to include answers of high relevance and answers that are highly informative when feedback is given on them. Existing systems merely focus on predicting relevance, by composing the scores of various schema and record matching algorithms. In this paper we show how to predict the uncertainty associated with a query result's score, as well as how informative feedback is on a given result. We build upon these foundations to develop an active learning approach to keyword search-based data integration, and we validate the effectiveness of our solution over real data from several very different domains.

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“…Recently, it has been shown that patterns of user click behaviour can be learned automatically from interaction data [6]. In addition to position bias, recent work examines other types of bias including(i) vertical bias driven by visually salient vertical results (e.g., image results, video results, news results) [11,12,43]; (ii) query bias, which occurs if a query does not match the user's information need [45], (iii) duplicate bias, which occurs if a result has been examined earlier in the search task [45]; and (iv) bias driven by individual differences between users [40].…”

Section: Models Of User Behaviormentioning

confidence: 99%

A Context-aware Time Model for Web Search

Borisov

Markov

Rijke

et al. 2016

Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval

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A context-aware time model for web search Borisov, A.; Markov, I.; de Rijke, M.; Serdyukov, P. General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. ABSTRACTIn web search, information about times between user actions has been shown to be a good indicator of users' satisfaction with the search results. Existing work uses the mean values of the observed times, or fits probability distributions to the observed times. This implies a context-independence assumption that the time elapsed between a pair of user actions does not depend on the context, in which the first action takes place. We validate this assumption using logs of a commercial web search engine and discover that it does not always hold. For between 37% to 80% of queryresult pairs, depending on the number of observations, the distributions of click dwell times have statistically significant differences in query sessions for which a given result (i) is the first item to be clicked and (ii) is not the first. To account for this context bias effect, we propose a context-aware time model (CATM). The CATM allows us(i) to predict times between user actions in contexts, in which these actions were not observed, and (ii) to compute contextindependent estimates of the times by predicting them in predefined contexts. Our experimental results show that the CATM provides better means than existing methods to predict and interpret times between user actions.

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Personalized click model through collaborative filtering

Cited by 51 publications

References 21 publications

A Neural Click Model for Web Search

A Neural Click Model for Web Search

Actively soliciting feedback for query answers in keyword search-based data integration

A Context-aware Time Model for Web Search

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