Roberto Konow, G. Navarro, C. Clarke, A. López-Ortiz
We introduce a new representation of the inverted index that performs faster ranked unions and intersections while using less space. Our index is based on the treap data structure, which allows us to intersect/merge the document identifiers while simultaneously thresholding by frequency, instead of the costlier two-step classical processing methods. To achieve compression we represent the treap topology using compact data structures. Further, the treap invariants allow us to elegantly encode differentially both document identifiers and frequencies. Results show that our index uses about 20% less space, and performs queries up to three times faster, than state-of-the-art compact representations.
{"title":"Faster and smaller inverted indices with treaps","authors":"Roberto Konow, G. Navarro, C. Clarke, A. López-Ortiz","doi":"10.1145/2484028.2484088","DOIUrl":"https://doi.org/10.1145/2484028.2484088","url":null,"abstract":"We introduce a new representation of the inverted index that performs faster ranked unions and intersections while using less space. Our index is based on the treap data structure, which allows us to intersect/merge the document identifiers while simultaneously thresholding by frequency, instead of the costlier two-step classical processing methods. To achieve compression we represent the treap topology using compact data structures. Further, the treap invariants allow us to elegantly encode differentially both document identifiers and frequencies. Results show that our index uses about 20% less space, and performs queries up to three times faster, than state-of-the-art compact representations.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115504624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rishabh Mehrotra, S. Sanner, Wray L. Buntine, Lexing Xie
Twitter, or the world of 140 characters poses serious challenges to the efficacy of topic models on short, messy text. While topic models such as Latent Dirichlet Allocation (LDA) have a long history of successful application to news articles and academic abstracts, they are often less coherent when applied to microblog content like Twitter. In this paper, we investigate methods to improve topics learned from Twitter content without modifying the basic machinery of LDA; we achieve this through various pooling schemes that aggregate tweets in a data preprocessing step for LDA. We empirically establish that a novel method of tweet pooling by hashtags leads to a vast improvement in a variety of measures for topic coherence across three diverse Twitter datasets in comparison to an unmodified LDA baseline and a variety of pooling schemes. An additional contribution of automatic hashtag labeling further improves on the hashtag pooling results for a subset of metrics. Overall, these two novel schemes lead to significantly improved LDA topic models on Twitter content.
{"title":"Improving LDA topic models for microblogs via tweet pooling and automatic labeling","authors":"Rishabh Mehrotra, S. Sanner, Wray L. Buntine, Lexing Xie","doi":"10.1145/2484028.2484166","DOIUrl":"https://doi.org/10.1145/2484028.2484166","url":null,"abstract":"Twitter, or the world of 140 characters poses serious challenges to the efficacy of topic models on short, messy text. While topic models such as Latent Dirichlet Allocation (LDA) have a long history of successful application to news articles and academic abstracts, they are often less coherent when applied to microblog content like Twitter. In this paper, we investigate methods to improve topics learned from Twitter content without modifying the basic machinery of LDA; we achieve this through various pooling schemes that aggregate tweets in a data preprocessing step for LDA. We empirically establish that a novel method of tweet pooling by hashtags leads to a vast improvement in a variety of measures for topic coherence across three diverse Twitter datasets in comparison to an unmodified LDA baseline and a variety of pooling schemes. An additional contribution of automatic hashtag labeling further improves on the hashtag pooling results for a subset of metrics. Overall, these two novel schemes lead to significantly improved LDA topic models on Twitter content.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124888841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chaosheng Fan, Yanyan Lan, J. Guo, Zuoquan Lin, Xueqi Cheng
Recommender system has become an effective tool for information filtering, which usually provides the most useful items to users by a top-k ranking list. Traditional recommendation techniques such as Nearest Neighbors (NN) and Matrix Factorization (MF) have been widely used in real recommender systems. However, neither approaches can well accomplish recommendation task since that: (1) most NN methods leverage the neighbor's behaviors for prediction, which may suffer the severe data sparsity problem; (2) MF methods are less sensitive to sparsity, but neighbors' influences on latent factors are not fully explored, since the latent factors are often used independently. To overcome the above problems, we propose a new framework for recommender systems, called collaborative factorization. It expresses the user as the combination of his own factors and those of the neighbors', called collaborative latent factors, and a ranking loss is then utilized for optimization. The advantage of our approach is that it can both enjoy the merits of NN and MF methods. In this paper, we take the logistic loss in RankNet and the likelihood loss in ListMLE as examples, and the corresponding collaborative factorization methods are called CoF-Net and CoF-MLE. Our experimental results on three benchmark datasets show that they are more effective than several state-of-the-art recommendation methods.
{"title":"Collaborative factorization for recommender systems","authors":"Chaosheng Fan, Yanyan Lan, J. Guo, Zuoquan Lin, Xueqi Cheng","doi":"10.1145/2484028.2484176","DOIUrl":"https://doi.org/10.1145/2484028.2484176","url":null,"abstract":"Recommender system has become an effective tool for information filtering, which usually provides the most useful items to users by a top-k ranking list. Traditional recommendation techniques such as Nearest Neighbors (NN) and Matrix Factorization (MF) have been widely used in real recommender systems. However, neither approaches can well accomplish recommendation task since that: (1) most NN methods leverage the neighbor's behaviors for prediction, which may suffer the severe data sparsity problem; (2) MF methods are less sensitive to sparsity, but neighbors' influences on latent factors are not fully explored, since the latent factors are often used independently. To overcome the above problems, we propose a new framework for recommender systems, called collaborative factorization. It expresses the user as the combination of his own factors and those of the neighbors', called collaborative latent factors, and a ranking loss is then utilized for optimization. The advantage of our approach is that it can both enjoy the merits of NN and MF methods. In this paper, we take the logistic loss in RankNet and the likelihood loss in ListMLE as examples, and the corresponding collaborative factorization methods are called CoF-Net and CoF-MLE. Our experimental results on three benchmark datasets show that they are more effective than several state-of-the-art recommendation methods.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121558533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The textual context of an element, structurally, contains traces of evidences. Utilizing this context in scoring is called contextualization. In this study we hypothesize that the context of an XML-element originated from its textit{preceding} and textit{following} elements in the sequential ordering of a document improves the quality of retrieval. In the tree form of the document's structure, textit{kinship} contextualization means, contextualization based on the horizontal and vertical elements in the textit{kinship tree,} or elements in closer to a wider structural kinship. We have tested several variants of kinship contextualization and verified notable improvements in comparison with the baseline system and gold standards in the retrieval of focused elements.
{"title":"Kinship contextualization: utilizing the preceding and following structural elements","authors":"Muhammad Ali Norozi, Paavo Arvola","doi":"10.1145/2484028.2484111","DOIUrl":"https://doi.org/10.1145/2484028.2484111","url":null,"abstract":"The textual context of an element, structurally, contains traces of evidences. Utilizing this context in scoring is called contextualization. In this study we hypothesize that the context of an XML-element originated from its textit{preceding} and textit{following} elements in the sequential ordering of a document improves the quality of retrieval. In the tree form of the document's structure, textit{kinship} contextualization means, contextualization based on the horizontal and vertical elements in the textit{kinship tree,} or elements in closer to a wider structural kinship. We have tested several variants of kinship contextualization and verified notable improvements in comparison with the baseline system and gold standards in the retrieval of focused elements.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125816443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Matthew Ekstrand-Abueg, Virgil Pavlu, Makoto P. Kato, T. Sakai, Takehiro Yamamoto, Mayu Iwata
Building test collections based on nuggets is useful evaluating systems that return documents, answers, or summaries. However, nugget construction requires a lot of manual work and is not feasible for large query sets. Towards an efficient and scalable nugget-based evaluation, we study the applicability of semi-automatic nugget extraction in the context of the ongoing NTCIR One Click Access (1CLICK) task. We compare manually-extracted and semi-automatically-extracted Japanese nuggets to demonstrate the coverage and efficiency of the semi-automatic nugget extraction. Our findings suggest that the manual nugget extraction can be replaced with a direct adaptation of the English semi-automatic nugget extraction system, especially for queries for which the user desires broad answers from free-form text.
{"title":"Exploring semi-automatic nugget extraction for Japanese one click access evaluation","authors":"Matthew Ekstrand-Abueg, Virgil Pavlu, Makoto P. Kato, T. Sakai, Takehiro Yamamoto, Mayu Iwata","doi":"10.1145/2484028.2484153","DOIUrl":"https://doi.org/10.1145/2484028.2484153","url":null,"abstract":"Building test collections based on nuggets is useful evaluating systems that return documents, answers, or summaries. However, nugget construction requires a lot of manual work and is not feasible for large query sets. Towards an efficient and scalable nugget-based evaluation, we study the applicability of semi-automatic nugget extraction in the context of the ongoing NTCIR One Click Access (1CLICK) task. We compare manually-extracted and semi-automatically-extracted Japanese nuggets to demonstrate the coverage and efficiency of the semi-automatic nugget extraction. Our findings suggest that the manual nugget extraction can be replaced with a direct adaptation of the English semi-automatic nugget extraction system, especially for queries for which the user desires broad answers from free-form text.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129463492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Shou, Kuang Mao, Xinyuan Luo, Ke Chen, Gang Chen, Tianlei Hu
Singing is a popular social activity and a good way of expressing one's feelings. One important reason for unsuccessful singing performance is because the singer fails to choose a suitable song. In this paper, we propose a novel singing competence-based song recommendation framework. It is distinguished from most existing music recommendation systems which rely on the computation of listeners' interests or similarity. We model a singer's vocal competence as singer profile, which takes voice pitch, intensity, and quality into consideration. Then we propose techniques to acquire singer profiles. We also present a song profile model which is used to construct a human annotated song database. Finally, we propose a learning-to-rank scheme for recommending songs by singer profile. The experimental study on real singers demonstrates the effectiveness of our approach and its advantages over two baseline methods. To the best of our knowledge, our work is the first to study competence-based song recommendation.
{"title":"Competence-based song recommendation","authors":"L. Shou, Kuang Mao, Xinyuan Luo, Ke Chen, Gang Chen, Tianlei Hu","doi":"10.1145/2484028.2484048","DOIUrl":"https://doi.org/10.1145/2484028.2484048","url":null,"abstract":"Singing is a popular social activity and a good way of expressing one's feelings. One important reason for unsuccessful singing performance is because the singer fails to choose a suitable song. In this paper, we propose a novel singing competence-based song recommendation framework. It is distinguished from most existing music recommendation systems which rely on the computation of listeners' interests or similarity. We model a singer's vocal competence as singer profile, which takes voice pitch, intensity, and quality into consideration. Then we propose techniques to acquire singer profiles. We also present a song profile model which is used to construct a human annotated song database. Finally, we propose a learning-to-rank scheme for recommending songs by singer profile. The experimental study on real singers demonstrates the effectiveness of our approach and its advantages over two baseline methods. To the best of our knowledge, our work is the first to study competence-based song recommendation.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126969741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Prior research in resource selection for federated search mainly focused on selecting a small number of information sources that are most relevant to a user query. However, result novelty and diversification are largely unexplored, which does not reflect the various kinds of information needs of users in real world applications. This paper proposes two general approaches to model both result relevance and diversification in selecting sources, in order to provide more comprehensive coverage of multiple aspects of a user query. The first approach focuses on diversifying the document ranking on a centralized sample database before selecting information sources under the framework of Relevant Document Distribution Estimation (ReDDE). The second approach first evaluates the relevance of information sources with respect to each aspect of the query, and then ranks the sources based on the novelty and relevance that they offer. Both approaches can be applied with a wide range of existing resource selection algorithms such as ReDDE, CRCS, CORI and Big Document. Moreover, this paper proposes a learning based approach to combine multiple resource selection algorithms for result diversification, which can further improve the performance. We propose a set of new metrics for resource selection in federated search to evaluate the diversification performance of different approaches. To our best knowledge, this is the first piece of work that addresses the problem of search result diversification in federated search. The effectiveness of the proposed approaches has been demonstrated by an extensive set of experiments on the federated search testbed of the Clueweb dataset.
{"title":"Search result diversification in resource selection for federated search","authors":"Dzung Hong, Luo Si","doi":"10.1145/2484028.2484091","DOIUrl":"https://doi.org/10.1145/2484028.2484091","url":null,"abstract":"Prior research in resource selection for federated search mainly focused on selecting a small number of information sources that are most relevant to a user query. However, result novelty and diversification are largely unexplored, which does not reflect the various kinds of information needs of users in real world applications. This paper proposes two general approaches to model both result relevance and diversification in selecting sources, in order to provide more comprehensive coverage of multiple aspects of a user query. The first approach focuses on diversifying the document ranking on a centralized sample database before selecting information sources under the framework of Relevant Document Distribution Estimation (ReDDE). The second approach first evaluates the relevance of information sources with respect to each aspect of the query, and then ranks the sources based on the novelty and relevance that they offer. Both approaches can be applied with a wide range of existing resource selection algorithms such as ReDDE, CRCS, CORI and Big Document. Moreover, this paper proposes a learning based approach to combine multiple resource selection algorithms for result diversification, which can further improve the performance. We propose a set of new metrics for resource selection in federated search to evaluate the diversification performance of different approaches. To our best knowledge, this is the first piece of work that addresses the problem of search result diversification in federated search. The effectiveness of the proposed approaches has been demonstrated by an extensive set of experiments on the federated search testbed of the Clueweb dataset.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121999153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Query-biased search result summaries, or "snippets", help users decide whether a result is relevant for their information need, and have become increasingly important for helping searchers with difficult or ambiguous search tasks. Previously published snippet generation algorithms have been primarily based on selecting document fragments most similar to the query, which does not take into account which parts of the document the searchers actually found useful. We present a new approach to improving result summaries by incorporating post-click searcher behavior data, such as mouse cursor movements and scrolling over the result documents. To achieve this aim, we develop a method for collecting behavioral data with precise association between searcher intent, document examination behavior, and the corresponding document fragments. In turn, this allows us to incorporate page examination behavior signals into a novel Behavior-Biased Snippet generation system (BeBS). By mining searcher examination data, BeBS infers document fragments of most interest to users, and combines this evidence with text-based features to select the most promising fragments for inclusion in the result summary. Our extensive experiments and analysis demonstrate that our method improves the quality of result summaries compared to existing state-of-the-art methods. We believe that this work opens a new direction for improving search result presentation, and we make available the code and the search behavior data used in this study to encourage further research in this area.
{"title":"Improving search result summaries by using searcher behavior data","authors":"Mikhail S. Ageev, Dmitry Lagun, Eugene Agichtein","doi":"10.1145/2484028.2484093","DOIUrl":"https://doi.org/10.1145/2484028.2484093","url":null,"abstract":"Query-biased search result summaries, or \"snippets\", help users decide whether a result is relevant for their information need, and have become increasingly important for helping searchers with difficult or ambiguous search tasks. Previously published snippet generation algorithms have been primarily based on selecting document fragments most similar to the query, which does not take into account which parts of the document the searchers actually found useful. We present a new approach to improving result summaries by incorporating post-click searcher behavior data, such as mouse cursor movements and scrolling over the result documents. To achieve this aim, we develop a method for collecting behavioral data with precise association between searcher intent, document examination behavior, and the corresponding document fragments. In turn, this allows us to incorporate page examination behavior signals into a novel Behavior-Biased Snippet generation system (BeBS). By mining searcher examination data, BeBS infers document fragments of most interest to users, and combines this evidence with text-based features to select the most promising fragments for inclusion in the result summary. Our extensive experiments and analysis demonstrate that our method improves the quality of result summaries compared to existing state-of-the-art methods. We believe that this work opens a new direction for improving search result presentation, and we make available the code and the search behavior data used in this study to encourage further research in this area.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120945552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Search systems use context to effectively satisfy a user's information need as expressed by a query. Tasks are important factors in determining user context during search and many studies have been conducted that identify tasks and task stages through users' interaction behavior with search systems. The type of interaction available to users, however, depends on the type of search interface features available. Queries are the most pervasive input from users to express their information need regardless of the input method, e.g., typing keywords or clicking facets. Instead of characterizing interaction behavior in terms of interface specific components, we propose to characterize users' search behavior in terms of two types of query modification: (i) direct modification, which refers to reformulations of queries; and (ii) indirect modification, which refers to user operations on additional input components provided by various search interfaces. We investigate the utility of characterizing task stages through direct and indirect query reformulations in a case study and find that it is possible to effectively differentiate subsequent stages of the search task. We found that describing user interaction behavior in such a generic form allowed us to relate user actions to search task stages independent from the specific search interface deployed. The next step will then be to validate this idea in a setting with a wider palette of search tasks and tools.
{"title":"Characterizing stages of a multi-session complex search task through direct and indirect query modifications","authors":"Jiyin He, M. Bron, A. D. Vries","doi":"10.1145/2484028.2484178","DOIUrl":"https://doi.org/10.1145/2484028.2484178","url":null,"abstract":"Search systems use context to effectively satisfy a user's information need as expressed by a query. Tasks are important factors in determining user context during search and many studies have been conducted that identify tasks and task stages through users' interaction behavior with search systems. The type of interaction available to users, however, depends on the type of search interface features available. Queries are the most pervasive input from users to express their information need regardless of the input method, e.g., typing keywords or clicking facets. Instead of characterizing interaction behavior in terms of interface specific components, we propose to characterize users' search behavior in terms of two types of query modification: (i) direct modification, which refers to reformulations of queries; and (ii) indirect modification, which refers to user operations on additional input components provided by various search interfaces. We investigate the utility of characterizing task stages through direct and indirect query reformulations in a case study and find that it is possible to effectively differentiate subsequent stages of the search task. We found that describing user interaction behavior in such a generic form allowed us to relate user actions to search task stages independent from the specific search interface deployed. The next step will then be to validate this idea in a setting with a wider palette of search tasks and tools.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121135772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Query auto-completion (QAC) is one of the most prominent features of modern search engines. The list of query candidates is generated according to the prefix entered by the user in the search box and is updated on each new key stroke. Query prefixes tend to be short and ambiguous, and existing models mostly rely on the past popularity of matching candidates for ranking. However, the popularity of certain queries may vary drastically across different demographics and users. For instance, while instagram and imdb have comparable popularities overall and are both legitimate candidates to show for prefix i, the former is noticeably more popular among young female users, and the latter is more likely to be issued by men. In this paper, we present a supervised framework for personalizing auto-completion ranking. We introduce a novel labelling strategy for generating offline training labels that can be used for learning personalized rankers. We compare the effectiveness of several user-specific and demographic-based features and show that among them, the user's long-term search history and location are the most effective for personalizing auto-completion rankers. We perform our experiments on the publicly available AOL query logs, and also on the larger-scale logs of Bing. The results suggest that supervised rankers enhanced by personalization features can significantly outperform the existing popularity-based base-lines, in terms of mean reciprocal rank (MRR) by up to 9%.
{"title":"Learning to personalize query auto-completion","authors":"Milad Shokouhi","doi":"10.1145/2484028.2484076","DOIUrl":"https://doi.org/10.1145/2484028.2484076","url":null,"abstract":"Query auto-completion (QAC) is one of the most prominent features of modern search engines. The list of query candidates is generated according to the prefix entered by the user in the search box and is updated on each new key stroke. Query prefixes tend to be short and ambiguous, and existing models mostly rely on the past popularity of matching candidates for ranking. However, the popularity of certain queries may vary drastically across different demographics and users. For instance, while instagram and imdb have comparable popularities overall and are both legitimate candidates to show for prefix i, the former is noticeably more popular among young female users, and the latter is more likely to be issued by men. In this paper, we present a supervised framework for personalizing auto-completion ranking. We introduce a novel labelling strategy for generating offline training labels that can be used for learning personalized rankers. We compare the effectiveness of several user-specific and demographic-based features and show that among them, the user's long-term search history and location are the most effective for personalizing auto-completion rankers. We perform our experiments on the publicly available AOL query logs, and also on the larger-scale logs of Bing. The results suggest that supervised rankers enhanced by personalization features can significantly outperform the existing popularity-based base-lines, in terms of mean reciprocal rank (MRR) by up to 9%.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125099560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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