Dae Hoon Park, Mengwen Liu, ChengXiang Zhai, Haohong Wang
Smartphones and tablets with their apps pervaded our everyday life, leading to a new demand for search tools to help users find the right apps to satisfy their immediate needs. While there are a few commercial mobile app search engines available, the new task of mobile app retrieval has not yet been rigorously studied. Indeed, there does not yet exist a test collection for quantitatively evaluating this new retrieval task. In this paper, we first study the effectiveness of the state-of-the-art retrieval models for the app retrieval task using a new app retrieval test data we created. We then propose and study a novel approach that generates a new representation for each app. Our key idea is to leverage user reviews to find out important features of apps and bridge vocabulary gap between app developers and users. Specifically, we jointly model app descriptions and user reviews using topic model in order to generate app representations while excluding noise in reviews. Experiment results indicate that the proposed approach is effective and outperforms the state-of-the-art retrieval models for app retrieval.
{"title":"Leveraging User Reviews to Improve Accuracy for Mobile App Retrieval","authors":"Dae Hoon Park, Mengwen Liu, ChengXiang Zhai, Haohong Wang","doi":"10.1145/2766462.2767759","DOIUrl":"https://doi.org/10.1145/2766462.2767759","url":null,"abstract":"Smartphones and tablets with their apps pervaded our everyday life, leading to a new demand for search tools to help users find the right apps to satisfy their immediate needs. While there are a few commercial mobile app search engines available, the new task of mobile app retrieval has not yet been rigorously studied. Indeed, there does not yet exist a test collection for quantitatively evaluating this new retrieval task. In this paper, we first study the effectiveness of the state-of-the-art retrieval models for the app retrieval task using a new app retrieval test data we created. We then propose and study a novel approach that generates a new representation for each app. Our key idea is to leverage user reviews to find out important features of apps and bridge vocabulary gap between app developers and users. Specifically, we jointly model app descriptions and user reviews using topic model in order to generate app representations while excluding noise in reviews. Experiment results indicate that the proposed approach is effective and outperforms the state-of-the-art retrieval models for app retrieval.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133310244","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 task of information retrieval (IR) has traditionally been defined as to rank a collection of documents in response to a query. While this definition has enabled most research progress in IR so far, it does not model accurately the actual retrieval task in a real IR application, where users tend to be engaged in an interactive process with multipe queries, and optimizing the overall performance of an IR system on an entire search session is far more important than its performance on an individual query. In this talk, I will present a new game-theoretic formulation of the IR problem where the key idea is to model information retrieval as a process of a search engine and a user playing a cooperative game, with a shared goal of satisfying the user's information need (or more generally helping the user complete a task) while minimizing the user's effort and the resource overhead on the retrieval system. Such a game-theoretic framework offers several benefits. First, it naturally suggests optimization of the overall utility of an interactive retrieval system over a whole search session, thus breaking the limitation of the traditional formulation that optimizes ranking of documents for a single query. Second, it models the interactions between users and a search engine, and thus can optimize the collaboration of a search engine and its users, maximizing the "combined intelligence" of a system and users. Finally, it can serve as a unified framework for optimizing both interactive information retrieval and active relevance judgment acquisition through crowdsourcing. I will discuss how the new framework can not only cover several emerging directions in current IR research as special cases, but also open up many interesting new research directions in IR.
{"title":"Towards a Game-Theoretic Framework for Information Retrieval","authors":"ChengXiang Zhai","doi":"10.1145/2766462.2767853","DOIUrl":"https://doi.org/10.1145/2766462.2767853","url":null,"abstract":"The task of information retrieval (IR) has traditionally been defined as to rank a collection of documents in response to a query. While this definition has enabled most research progress in IR so far, it does not model accurately the actual retrieval task in a real IR application, where users tend to be engaged in an interactive process with multipe queries, and optimizing the overall performance of an IR system on an entire search session is far more important than its performance on an individual query. In this talk, I will present a new game-theoretic formulation of the IR problem where the key idea is to model information retrieval as a process of a search engine and a user playing a cooperative game, with a shared goal of satisfying the user's information need (or more generally helping the user complete a task) while minimizing the user's effort and the resource overhead on the retrieval system. Such a game-theoretic framework offers several benefits. First, it naturally suggests optimization of the overall utility of an interactive retrieval system over a whole search session, thus breaking the limitation of the traditional formulation that optimizes ranking of documents for a single query. Second, it models the interactions between users and a search engine, and thus can optimize the collaboration of a search engine and its users, maximizing the \"combined intelligence\" of a system and users. Finally, it can serve as a unified framework for optimizing both interactive information retrieval and active relevance judgment acquisition through crowdsourcing. I will discuss how the new framework can not only cover several emerging directions in current IR research as special cases, but also open up many interesting new research directions in IR.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"185 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133888311","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}
Ashraf Bah Rabiou, Praveen Chandar, Ben Carterette
Different users may be attempting to satisfy different information needs while providing the same query to a search engine. Addressing that issue is addressing Novelty and Diversity in information retrieval. Novelty and Diversity search task models the task wherein users are interested in seeing more and more documents that are not only relevant, but also cover more aspects (or subtopics) related to the topic of interest. This is in contrast with the traditional IR task where topical relevance is the only factor in evaluating search results. In this paper, we conduct a user study where users are asked to give a preference between one of two documents B and C given a query and also given that they have already seen a document A. We then test a total of ten hypotheses pertaining to the relationship between the "comprehensiveness" of documents (i.e. the number of subtopics a document is relevant to) and real users' preference judgments. Our results show that users are inclined to prefer documents with higher comprehensiveness, even when the prior document A already covers more aspects than the two documents being compared, and even when the least preferred has a higher relevance grade. In fact, users are inclined to prefer documents with higher overall aspect-coverage even in cases where B and C are relevant to the same number of novel subtopics.
{"title":"Document Comprehensiveness and User Preferences in Novelty Search Tasks","authors":"Ashraf Bah Rabiou, Praveen Chandar, Ben Carterette","doi":"10.1145/2766462.2767820","DOIUrl":"https://doi.org/10.1145/2766462.2767820","url":null,"abstract":"Different users may be attempting to satisfy different information needs while providing the same query to a search engine. Addressing that issue is addressing Novelty and Diversity in information retrieval. Novelty and Diversity search task models the task wherein users are interested in seeing more and more documents that are not only relevant, but also cover more aspects (or subtopics) related to the topic of interest. This is in contrast with the traditional IR task where topical relevance is the only factor in evaluating search results. In this paper, we conduct a user study where users are asked to give a preference between one of two documents B and C given a query and also given that they have already seen a document A. We then test a total of ten hypotheses pertaining to the relationship between the \"comprehensiveness\" of documents (i.e. the number of subtopics a document is relevant to) and real users' preference judgments. Our results show that users are inclined to prefer documents with higher comprehensiveness, even when the prior document A already covers more aspects than the two documents being compared, and even when the least preferred has a higher relevance grade. In fact, users are inclined to prefer documents with higher overall aspect-coverage even in cases where B and C are relevant to the same number of novel subtopics.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"345 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122837049","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}
While watching television, people increasingly consume additional content related to what they are watching. We consider the task of finding video content related to a live television broadcast for which we leverage the textual stream of subtitles associated with the broadcast. We model this task as a Markov decision process and propose a method that uses reinforcement learning to directly optimize the retrieval effectiveness of queries generated from the stream of subtitles. Our dynamic query modeling approach significantly outperforms state-of-the-art baselines for stationary query modeling and for text-based retrieval in a television setting. In particular we find that carefully weighting terms and decaying these weights based on recency significantly improves effectiveness. Moreover, our method is highly efficient and can be used in a live television setting, i.e., in near real time.
{"title":"Dynamic Query Modeling for Related Content Finding","authors":"Daan Odijk, E. Meij, I. Sijaranamual, M. de Rijke","doi":"10.1145/2766462.2767715","DOIUrl":"https://doi.org/10.1145/2766462.2767715","url":null,"abstract":"While watching television, people increasingly consume additional content related to what they are watching. We consider the task of finding video content related to a live television broadcast for which we leverage the textual stream of subtitles associated with the broadcast. We model this task as a Markov decision process and propose a method that uses reinforcement learning to directly optimize the retrieval effectiveness of queries generated from the stream of subtitles. Our dynamic query modeling approach significantly outperforms state-of-the-art baselines for stationary query modeling and for text-based retrieval in a television setting. In particular we find that carefully weighting terms and decaying these weights based on recency significantly improves effectiveness. Moreover, our method is highly efficient and can be used in a live television setting, i.e., in near real time.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123042145","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}
When consuming content in applications such as e-readers, word processors, and Web browsers, users often see mentions to topics (or concepts) that attract their attention. In a scenario of significant practical interest, topics are explored in situ, without leaving the context of the application: The user selects a mention of a topic (in the form of continuous text), and the system subsequently recommends references (e.g., Wikipedia concepts) that are relevant in the context of the application. In order to realize this experience, it is necessary to tackle challenges that include: users may select any continuous text, even potentially noisy text for which there is no corresponding reference in the knowledge base; references must be relevant to both the user selection and the text around it; and the real estate available on the application may be constrained, thus limiting the number of results that can be shown. In this paper, we study this novel recommendation task, that we call in situ insights: recommending reference concepts in response to a text selection and its context in-situ of a document consumption application. We first propose a selection-centric context language model and a selection-centric context semantic model to capture user interest. Based on these models, we then measure the quality of a reference concept across three aspects: selection clarity, context coherence, and concept relevance. By leveraging all these aspects, we put forward a machine learning approach to simultaneously decide if a selection is noisy, and filter out low-quality candidate references. In order to quantitatively evaluate our proposed techniques, we construct a test collection based on the simulation of the in situ insights scenario using crowdsourcing in the context of a real-word e-reader application. Our experimental evaluation demonstrates the effectiveness of the proposed techniques.
{"title":"In Situ Insights","authors":"Yuanhua Lv, A. Fuxman","doi":"10.1145/2766462.2767696","DOIUrl":"https://doi.org/10.1145/2766462.2767696","url":null,"abstract":"When consuming content in applications such as e-readers, word processors, and Web browsers, users often see mentions to topics (or concepts) that attract their attention. In a scenario of significant practical interest, topics are explored in situ, without leaving the context of the application: The user selects a mention of a topic (in the form of continuous text), and the system subsequently recommends references (e.g., Wikipedia concepts) that are relevant in the context of the application. In order to realize this experience, it is necessary to tackle challenges that include: users may select any continuous text, even potentially noisy text for which there is no corresponding reference in the knowledge base; references must be relevant to both the user selection and the text around it; and the real estate available on the application may be constrained, thus limiting the number of results that can be shown. In this paper, we study this novel recommendation task, that we call in situ insights: recommending reference concepts in response to a text selection and its context in-situ of a document consumption application. We first propose a selection-centric context language model and a selection-centric context semantic model to capture user interest. Based on these models, we then measure the quality of a reference concept across three aspects: selection clarity, context coherence, and concept relevance. By leveraging all these aspects, we put forward a machine learning approach to simultaneously decide if a selection is noisy, and filter out low-quality candidate references. In order to quantitatively evaluate our proposed techniques, we construct a test collection based on the simulation of the in situ insights scenario using crowdsourcing in the context of a real-word e-reader application. Our experimental evaluation demonstrates the effectiveness of the proposed techniques.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127862276","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}
Given a topic of interest, a contrastive theme is a group of opposing pairs of viewpoints. We address the task of summarizing contrastive themes: given a set of opinionated documents, select meaningful sentences to represent contrastive themes present in those documents. Several factors make this a challenging problem: unknown numbers of topics, unknown relationships among topics, and the extraction of comparative sentences. Our approach has three core ingredients: contrastive theme modeling, diverse theme extraction, and contrastive theme summarization. Specifically, we present a hierarchical non-parametric model to describe hierarchical relations among topics; this model is used to infer threads of topics as themes from the nested Chinese restaurant process. We enhance the diversity of themes by using structured determinantal point processes for selecting a set of diverse themes with high quality. Finally, we pair contrastive themes and employ an iterative optimization algorithm to select sentences, explicitly considering contrast, relevance, and diversity. Experiments on three datasets demonstrate the effectiveness of our method.
{"title":"Summarizing Contrastive Themes via Hierarchical Non-Parametric Processes","authors":"Z. Ren, M. de Rijke","doi":"10.1145/2766462.2767713","DOIUrl":"https://doi.org/10.1145/2766462.2767713","url":null,"abstract":"Given a topic of interest, a contrastive theme is a group of opposing pairs of viewpoints. We address the task of summarizing contrastive themes: given a set of opinionated documents, select meaningful sentences to represent contrastive themes present in those documents. Several factors make this a challenging problem: unknown numbers of topics, unknown relationships among topics, and the extraction of comparative sentences. Our approach has three core ingredients: contrastive theme modeling, diverse theme extraction, and contrastive theme summarization. Specifically, we present a hierarchical non-parametric model to describe hierarchical relations among topics; this model is used to infer threads of topics as themes from the nested Chinese restaurant process. We enhance the diversity of themes by using structured determinantal point processes for selecting a set of diverse themes with high quality. Finally, we pair contrastive themes and employ an iterative optimization algorithm to select sentences, explicitly considering contrast, relevance, and diversity. Experiments on three datasets demonstrate the effectiveness of our method.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128548986","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}
{"title":"Session details: Session 4A: User Models","authors":"D. Kelly","doi":"10.1145/3255924","DOIUrl":"https://doi.org/10.1145/3255924","url":null,"abstract":"","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115500573","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}
In information retrieval evaluation, when presented with an effectiveness difference between two systems, there are three relevant questions one might ask. First, are the differences statistically significant? Second, is the comparison stable with respect to assessor differences? Finally, is the difference actually meaningful to a user? This paper tackles the last two questions about assessor differences and user preferences in the context of the newly-introduced tweet timeline generation task in the TREC 2014 Microblog track, where the system's goal is to construct an informative summary of non-redundant tweets that addresses the user's information need. Central to the evaluation methodology is human-generated semantic clusters of tweets that contain substantively similar information. We show that the evaluation is stable with respect to assessor differences in clustering and that user preferences generally correlate with effectiveness metrics even though users are not explicitly aware of the semantic clustering being performed by the systems. Although our analyses are limited to this particular task, we believe that lessons learned could generalize to other evaluations based on establishing semantic equivalence between information units, such as nugget-based evaluations in question answering and temporal summarization.
{"title":"Assessor Differences and User Preferences in Tweet Timeline Generation","authors":"Yulu Wang, G. Sherman, Jimmy J. Lin, Miles Efron","doi":"10.1145/2766462.2767699","DOIUrl":"https://doi.org/10.1145/2766462.2767699","url":null,"abstract":"In information retrieval evaluation, when presented with an effectiveness difference between two systems, there are three relevant questions one might ask. First, are the differences statistically significant? Second, is the comparison stable with respect to assessor differences? Finally, is the difference actually meaningful to a user? This paper tackles the last two questions about assessor differences and user preferences in the context of the newly-introduced tweet timeline generation task in the TREC 2014 Microblog track, where the system's goal is to construct an informative summary of non-redundant tweets that addresses the user's information need. Central to the evaluation methodology is human-generated semantic clusters of tweets that contain substantively similar information. We show that the evaluation is stable with respect to assessor differences in clustering and that user preferences generally correlate with effectiveness metrics even though users are not explicitly aware of the semantic clustering being performed by the systems. Although our analyses are limited to this particular task, we believe that lessons learned could generalize to other evaluations based on establishing semantic equivalence between information units, such as nugget-based evaluations in question answering and temporal summarization.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115571940","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}
{"title":"Session details: Session 2A: Diversity and Bias","authors":"Gareth J.F. Jones","doi":"10.1145/3255918","DOIUrl":"https://doi.org/10.1145/3255918","url":null,"abstract":"","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115636296","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}
SimRank is an influential link-based similarity measure that has been used in many fields of Web search and sociometry. The best-of-breed method by Kusumoto et. al., however, does not always deliver high-quality results, since it fails to accurately obtain its diagonal correction matrix D. Besides, SimRank is also limited by an unwanted "connectivity trait": increasing the number of paths between nodes a and b often incurs a decrease in score s(a,b). The best-known solution, SimRank++, cannot resolve this problem, since a revised score will be zero if a and b have no common in-neighbors. In this paper, we consider high-quality similarity search. Our scheme, SR#, is efficient and semantically meaningful: (1) We first formulate the exact D, and devise a "varied-D" method to accurately compute SimRank in linear memory. Moreover, by grouping computation, we also reduce the time of from quadratic to linear in the number of iterations. (2) We design a "kernel-based" model to improve the quality of SimRank, and circumvent the "connectivity trait" issue. (3) We give mathematical insights to the semantic difference between SimRank and its variant, and correct an argument: "if D is replaced by a scaled identity matrix, top-K rankings will not be affected much". The experiments confirm that SR# can accurately extract high-quality scores, and is much faster than the state-of-the-art competitors.
{"title":"High Quality Graph-Based Similarity Search","authors":"Weiren Yu, J. Mccann","doi":"10.1145/2766462.2767720","DOIUrl":"https://doi.org/10.1145/2766462.2767720","url":null,"abstract":"SimRank is an influential link-based similarity measure that has been used in many fields of Web search and sociometry. The best-of-breed method by Kusumoto et. al., however, does not always deliver high-quality results, since it fails to accurately obtain its diagonal correction matrix D. Besides, SimRank is also limited by an unwanted \"connectivity trait\": increasing the number of paths between nodes a and b often incurs a decrease in score s(a,b). The best-known solution, SimRank++, cannot resolve this problem, since a revised score will be zero if a and b have no common in-neighbors. In this paper, we consider high-quality similarity search. Our scheme, SR#, is efficient and semantically meaningful: (1) We first formulate the exact D, and devise a \"varied-D\" method to accurately compute SimRank in linear memory. Moreover, by grouping computation, we also reduce the time of from quadratic to linear in the number of iterations. (2) We design a \"kernel-based\" model to improve the quality of SimRank, and circumvent the \"connectivity trait\" issue. (3) We give mathematical insights to the semantic difference between SimRank and its variant, and correct an argument: \"if D is replaced by a scaled identity matrix, top-K rankings will not be affected much\". The experiments confirm that SR# can accurately extract high-quality scores, and is much faster than the state-of-the-art competitors.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115736601","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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