C. Clarke, Luanne Freund, Mark D. Smucker, Emine Yilmaz
The SIGIR 2013 Workshop on Modeling User Behavior for Information Retrieval Evaluation (MUBE 2013) brings together people to discuss existing and new approaches, ways to collaborate, and other ideas and issues involved in improving information retrieval evaluation through the modeling of user behavior.
{"title":"SIGIR 2013 workshop on modeling user behavior for information retrieval evaluation","authors":"C. Clarke, Luanne Freund, Mark D. Smucker, Emine Yilmaz","doi":"10.1145/2484028.2484222","DOIUrl":"https://doi.org/10.1145/2484028.2484222","url":null,"abstract":"The SIGIR 2013 Workshop on Modeling User Behavior for Information Retrieval Evaluation (MUBE 2013) brings together people to discuss existing and new approaches, ways to collaborate, and other ideas and issues involved in improving information retrieval evaluation through the modeling of user behavior.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"4 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":"133883871","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}
Most of existing e-commerce recommender systems aim to recommend the right product to a user, based on whether the user is likely to purchase or like a product. On the other hand, the effectiveness of recommendations also depends on the time of the recommendation. Let us take a user who just purchased a laptop as an example. She may purchase a replacement battery in 2 years (assuming that the laptop's original battery often fails to work around that time) and purchase a new laptop in another 2 years. In this case, it is not a good idea to recommend a new laptop or a replacement battery right after the user purchased the new laptop. It could hurt the user's satisfaction of the recommender system if she receives a potentially right product recommendation at the wrong time. We argue that a system should not only recommend the most relevant item, but also recommend at the right time. This paper studies the new problem: how to recommend the right product at the right time? We adapt the proportional hazards modeling approach in survival analysis to the recommendation research field and propose a new opportunity model to explicitly incorporate time in an e-commerce recommender system. The new model estimates the joint probability of a user making a follow-up purchase of a particular product at a particular time. This joint purchase probability can be leveraged by recommender systems in various scenarios, including the zero-query pull-based recommendation scenario (e.g. recommendation on an e-commerce web site) and a proactive push-based promotion scenario (e.g. email or text message based marketing). We evaluate the opportunity modeling approach with multiple metrics. Experimental results on a data collected by a real-world e-commerce website(shop.com) show that it can predict a user's follow-up purchase behavior at a particular time with descent accuracy. In addition, the opportunity model significantly improves the conversion rate in pull-based systems and the user satisfaction/utility in push-based systems.
{"title":"Opportunity model for e-commerce recommendation: right product; right time","authors":"Jian Wang, Yi Zhang","doi":"10.1145/2484028.2484067","DOIUrl":"https://doi.org/10.1145/2484028.2484067","url":null,"abstract":"Most of existing e-commerce recommender systems aim to recommend the right product to a user, based on whether the user is likely to purchase or like a product. On the other hand, the effectiveness of recommendations also depends on the time of the recommendation. Let us take a user who just purchased a laptop as an example. She may purchase a replacement battery in 2 years (assuming that the laptop's original battery often fails to work around that time) and purchase a new laptop in another 2 years. In this case, it is not a good idea to recommend a new laptop or a replacement battery right after the user purchased the new laptop. It could hurt the user's satisfaction of the recommender system if she receives a potentially right product recommendation at the wrong time. We argue that a system should not only recommend the most relevant item, but also recommend at the right time. This paper studies the new problem: how to recommend the right product at the right time? We adapt the proportional hazards modeling approach in survival analysis to the recommendation research field and propose a new opportunity model to explicitly incorporate time in an e-commerce recommender system. The new model estimates the joint probability of a user making a follow-up purchase of a particular product at a particular time. This joint purchase probability can be leveraged by recommender systems in various scenarios, including the zero-query pull-based recommendation scenario (e.g. recommendation on an e-commerce web site) and a proactive push-based promotion scenario (e.g. email or text message based marketing). We evaluate the opportunity modeling approach with multiple metrics. Experimental results on a data collected by a real-world e-commerce website(shop.com) show that it can predict a user's follow-up purchase behavior at a particular time with descent accuracy. In addition, the opportunity model significantly improves the conversion rate in pull-based systems and the user satisfaction/utility in push-based systems.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"125 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":"133935672","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}
Cristian Rossi, E. Moura, A. Carvalho, A. D. Silva
In this paper we present two new algorithms designed to reduce the overall time required to process top-k queries. These algorithms are based on the document-at-a-time approach and modify the best baseline we found in the literature, Blockmax WAND (BMW), to take advantage of a two-tiered index, in which the first tier is a small index containing only the higher impact entries of each inverted list. This small index is used to pre-process the query before accessing a larger index in the second tier, resulting in considerable speeding up the whole process. The first algorithm we propose, named BMW-CS, achieves higher performance, but may result in small changes in the top results provided in the final ranking. The second algorithm, named BMW-t, preserves the top results and, while slower than BMW-CS, it is faster than BMW. In our experiments, BMW-CS was more than 40 times faster than BMW when computing top 10 results, and, while it does not guarantee preserving the top results, it preserved all ranking results evaluated at this level.
{"title":"Fast document-at-a-time query processing using two-tier indexes","authors":"Cristian Rossi, E. Moura, A. Carvalho, A. D. Silva","doi":"10.1145/2484028.2484085","DOIUrl":"https://doi.org/10.1145/2484028.2484085","url":null,"abstract":"In this paper we present two new algorithms designed to reduce the overall time required to process top-k queries. These algorithms are based on the document-at-a-time approach and modify the best baseline we found in the literature, Blockmax WAND (BMW), to take advantage of a two-tiered index, in which the first tier is a small index containing only the higher impact entries of each inverted list. This small index is used to pre-process the query before accessing a larger index in the second tier, resulting in considerable speeding up the whole process. The first algorithm we propose, named BMW-CS, achieves higher performance, but may result in small changes in the top results provided in the final ranking. The second algorithm, named BMW-t, preserves the top results and, while slower than BMW-CS, it is faster than BMW. In our experiments, BMW-CS was more than 40 times faster than BMW when computing top 10 results, and, while it does not guarantee preserving the top results, it preserved all ranking results evaluated at this level.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"11 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":"134325487","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 recent years, we have witnessed a rapid growth in the availability of digital multimedia on various application platforms and domains. Consequently, the problem of information overload has become more and more serious. In order to tackle the challenge, various multimedia recommendation technologies have been developed by different research communities (e.g., multimedia systems, information retrieval, machine learning and computer version). Meanwhile, many commercial web systems (e.g., Flick, YouTube, and Last.fm) have successfully applied recommendation techniques to provide users personalized content and services in a convenient and flexible way. When looking back, the information retrieval (IR) community has a long history of studying and contributing recommender system design and related issues. It has been proven that the recommender systems can effectively assist users in handling information overload and provide high-quality personalization. While several courses were dedicated to multimedia retrieval in the recent decade, to the best of our knowledge, the tutorial is the first one specifically focusing on multimedia recommender systems and their applications on various domains and media contents. We plan to summarize the research along this direction and provide an impetus for further research on this important topic
{"title":"Multimedia recommendation: technology and techniques","authors":"Jialie Shen, Meng Wang, Shuicheng Yan, Peng Cui","doi":"10.1145/2484028.2484194","DOIUrl":"https://doi.org/10.1145/2484028.2484194","url":null,"abstract":"In recent years, we have witnessed a rapid growth in the availability of digital multimedia on various application platforms and domains. Consequently, the problem of information overload has become more and more serious. In order to tackle the challenge, various multimedia recommendation technologies have been developed by different research communities (e.g., multimedia systems, information retrieval, machine learning and computer version). Meanwhile, many commercial web systems (e.g., Flick, YouTube, and Last.fm) have successfully applied recommendation techniques to provide users personalized content and services in a convenient and flexible way. When looking back, the information retrieval (IR) community has a long history of studying and contributing recommender system design and related issues. It has been proven that the recommender systems can effectively assist users in handling information overload and provide high-quality personalization. While several courses were dedicated to multimedia retrieval in the recent decade, to the best of our knowledge, the tutorial is the first one specifically focusing on multimedia recommender systems and their applications on various domains and media contents. We plan to summarize the research along this direction and provide an impetus for further research on this important topic","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"23 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":"131703322","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}
M. Hall, Paul D. Clough, Samuel Fernando, Paula Goodale, Mark Stevenson, Eneko Agirre, Arantxa Otegi, Aitor Soroa Etxabe, K. Fernie, Jillian R. Griffiths, Runar Bergheim
Current Information Retrieval systems for digital cultural heritage support only the actual search aspect of the information seeking process. This demonstration presents the second PATHS system which provides the exploration, analysis, and sense-making features to support the full information seeking process.
{"title":"Information seeking in digital cultural heritage with PATHS","authors":"M. Hall, Paul D. Clough, Samuel Fernando, Paula Goodale, Mark Stevenson, Eneko Agirre, Arantxa Otegi, Aitor Soroa Etxabe, K. Fernie, Jillian R. Griffiths, Runar Bergheim","doi":"10.1145/2484028.2484210","DOIUrl":"https://doi.org/10.1145/2484028.2484210","url":null,"abstract":"Current Information Retrieval systems for digital cultural heritage support only the actual search aspect of the information seeking process. This demonstration presents the second PATHS system which provides the exploration, analysis, and sense-making features to support the full information seeking process.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"49 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":"126592005","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}
People's beliefs, and unconscious biases that arise from those beliefs, influence their judgment, decision making, and actions, as is commonly accepted among psychologists. Biases can be observed in information retrieval in situations where searchers seek or are presented with information that significantly deviates from the truth. There is little understanding of the impact of such biases in search. In this paper we study search-related biases via multiple probes: an exploratory retrospective survey, human labeling of the captions and results returned by a Web search engine, and a large-scale log analysis of search behavior on that engine. Targeting yes-no questions in the critical domain of health search, we show that Web searchers exhibit their own biases and are also subject to bias from the search engine. We clearly observe searchers favoring positive information over negative and more than expected given base rates based on consensus answers from physicians. We also show that search engines strongly favor a particular, usually positive, perspective, irrespective of the truth. Importantly, we show that these biases can be counterproductive and affect search outcomes; in our study, around half of the answers that searchers settled on were actually incorrect. Our findings have implications for search engine design, including the development of ranking algorithms that con-sider the desire to satisfy searchers (by validating their beliefs) and providing accurate answers and properly considering base rates. Incorporating likelihood information into search is particularly important for consequential tasks, such as those with a medical focus.
{"title":"Beliefs and biases in web search","authors":"Ryen W. White","doi":"10.1145/2484028.2484053","DOIUrl":"https://doi.org/10.1145/2484028.2484053","url":null,"abstract":"People's beliefs, and unconscious biases that arise from those beliefs, influence their judgment, decision making, and actions, as is commonly accepted among psychologists. Biases can be observed in information retrieval in situations where searchers seek or are presented with information that significantly deviates from the truth. There is little understanding of the impact of such biases in search. In this paper we study search-related biases via multiple probes: an exploratory retrospective survey, human labeling of the captions and results returned by a Web search engine, and a large-scale log analysis of search behavior on that engine. Targeting yes-no questions in the critical domain of health search, we show that Web searchers exhibit their own biases and are also subject to bias from the search engine. We clearly observe searchers favoring positive information over negative and more than expected given base rates based on consensus answers from physicians. We also show that search engines strongly favor a particular, usually positive, perspective, irrespective of the truth. Importantly, we show that these biases can be counterproductive and affect search outcomes; in our study, around half of the answers that searchers settled on were actually incorrect. Our findings have implications for search engine design, including the development of ranking algorithms that con-sider the desire to satisfy searchers (by validating their beliefs) and providing accurate answers and properly considering base rates. Incorporating likelihood information into search is particularly important for consequential tasks, such as those with a medical focus.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"69 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":"131018208","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}
It is an important research problem to design efficient and effective solutions for large scale similarity search. One popular strategy is to represent data examples as compact binary codes through semantic hashing, which has produced promising results with fast search speed and low storage cost. Many existing semantic hashing methods generate binary codes for documents by modeling document relationships based on similarity in a keyword feature space. Two major limitations in existing methods are: (1) Tag information is often associated with documents in many real world applications, but has not been fully exploited yet; (2) The similarity in keyword feature space does not fully reflect semantic relationships that go beyond keyword matching. This paper proposes a novel hashing approach, Semantic Hashing using Tags and Topic Modeling (SHTTM), to incorporate both the tag information and the similarity information from probabilistic topic modeling. In particular, a unified framework is designed for ensuring hashing codes to be consistent with tag information by a formal latent factor model and preserving the document topic/semantic similarity that goes beyond keyword matching. An iterative coordinate descent procedure is proposed for learning the optimal hashing codes. An extensive set of empirical studies on four different datasets has been conducted to demonstrate the advantages of the proposed SHTTM approach against several other state-of-the-art semantic hashing techniques. Furthermore, experimental results indicate that the modeling of tag information and utilizing topic modeling are beneficial for improving the effectiveness of hashing separately, while the combination of these two techniques in the unified framework obtains even better results.
{"title":"Semantic hashing using tags and topic modeling","authors":"Qifan Wang, Dan Zhang, Luo Si","doi":"10.1145/2484028.2484037","DOIUrl":"https://doi.org/10.1145/2484028.2484037","url":null,"abstract":"It is an important research problem to design efficient and effective solutions for large scale similarity search. One popular strategy is to represent data examples as compact binary codes through semantic hashing, which has produced promising results with fast search speed and low storage cost. Many existing semantic hashing methods generate binary codes for documents by modeling document relationships based on similarity in a keyword feature space. Two major limitations in existing methods are: (1) Tag information is often associated with documents in many real world applications, but has not been fully exploited yet; (2) The similarity in keyword feature space does not fully reflect semantic relationships that go beyond keyword matching. This paper proposes a novel hashing approach, Semantic Hashing using Tags and Topic Modeling (SHTTM), to incorporate both the tag information and the similarity information from probabilistic topic modeling. In particular, a unified framework is designed for ensuring hashing codes to be consistent with tag information by a formal latent factor model and preserving the document topic/semantic similarity that goes beyond keyword matching. An iterative coordinate descent procedure is proposed for learning the optimal hashing codes. An extensive set of empirical studies on four different datasets has been conducted to demonstrate the advantages of the proposed SHTTM approach against several other state-of-the-art semantic hashing techniques. Furthermore, experimental results indicate that the modeling of tag information and utilizing topic modeling are beneficial for improving the effectiveness of hashing separately, while the combination of these two techniques in the unified framework obtains even better results.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"28 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":"133615799","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}
Ahmed Hassan Awadallah, Ryen W. White, Yi-Min Wang
Search engines receive queries with a broad range of different search intents. However, they do not perform equally well for all queries. Understanding where search engines perform poorly is critical for improving their performance. In this paper, we present a method for automatically identifying poorly-performing query groups where a search engine may not meet searcher needs. This allows us to create coherent query clusters that help system design-ers generate actionable insights about necessary changes and helps learning-to-rank algorithms better learn relevance signals via spe-cialized rankers. The result is a framework capable of estimating dissatisfaction from Web search logs and learning to improve per-formance for dissatisfied queries. Through experimentation, we show that our method yields good quality groups that align with established retrieval performance metrics. We also show that we can significantly improve retrieval effectiveness via specialized rankers, and that coherent grouping of underperforming queries generated by our method is important in improving each group.
{"title":"Toward self-correcting search engines: using underperforming queries to improve search","authors":"Ahmed Hassan Awadallah, Ryen W. White, Yi-Min Wang","doi":"10.1145/2484028.2484043","DOIUrl":"https://doi.org/10.1145/2484028.2484043","url":null,"abstract":"Search engines receive queries with a broad range of different search intents. However, they do not perform equally well for all queries. Understanding where search engines perform poorly is critical for improving their performance. In this paper, we present a method for automatically identifying poorly-performing query groups where a search engine may not meet searcher needs. This allows us to create coherent query clusters that help system design-ers generate actionable insights about necessary changes and helps learning-to-rank algorithms better learn relevance signals via spe-cialized rankers. The result is a framework capable of estimating dissatisfaction from Web search logs and learning to improve per-formance for dissatisfied queries. Through experimentation, we show that our method yields good quality groups that align with established retrieval performance metrics. We also show that we can significantly improve retrieval effectiveness via specialized rankers, and that coherent grouping of underperforming queries generated by our method is important in improving each group.","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":"115527562","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}
Random Walk with Restart (RWR) has become an appealing measure of node proximities in emerging applications eg recommender systems and automatic image captioning. In practice, a real graph is typically large, and is frequently updated with small changes. It is often cost-inhibitive to recompute proximities from scratch via emph{batch} algorithms when the graph is updated. This paper focuses on the incremental computations of RWR in a dynamic graph, whose edges often change over time. The prior attempt of RWR [1] deploys kdash to find top-$k$ highest proximity nodes for a given query, which involves a strategy to incrementally emph{estimate} upper proximity bounds. However, due to its aim to prune needless calculation, such an incremental strategy is emph{approximate}: in $O(1)$ time for each node. The main contribution of this paper is to devise an emph{exact} and fast incremental algorithm of RWR for edge updates. Our solution, IRWR!, can incrementally compute any node proximity in $O(1)$ time for each edge update without loss of exactness. The empirical evaluations show the high efficiency and exactness of IRWR for computing proximities on dynamic networks against its batch counterparts.
{"title":"IRWR: incremental random walk with restart","authors":"Weiren Yu, Xuemin Lin","doi":"10.1145/2484028.2484114","DOIUrl":"https://doi.org/10.1145/2484028.2484114","url":null,"abstract":"Random Walk with Restart (RWR) has become an appealing measure of node proximities in emerging applications eg recommender systems and automatic image captioning. In practice, a real graph is typically large, and is frequently updated with small changes. It is often cost-inhibitive to recompute proximities from scratch via emph{batch} algorithms when the graph is updated. This paper focuses on the incremental computations of RWR in a dynamic graph, whose edges often change over time. The prior attempt of RWR [1] deploys kdash to find top-$k$ highest proximity nodes for a given query, which involves a strategy to incrementally emph{estimate} upper proximity bounds. However, due to its aim to prune needless calculation, such an incremental strategy is emph{approximate}: in $O(1)$ time for each node. The main contribution of this paper is to devise an emph{exact} and fast incremental algorithm of RWR for edge updates. Our solution, IRWR!, can incrementally compute any node proximity in $O(1)$ time for each edge update without loss of exactness. The empirical evaluations show the high efficiency and exactness of IRWR for computing proximities on dynamic networks against its batch counterparts.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"76 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":"115042001","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":"Task differentiation for personal search evaluation","authors":"S. S. Sadeghi","doi":"10.1145/2484028.2484236","DOIUrl":"https://doi.org/10.1145/2484028.2484236","url":null,"abstract":"","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"50 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":"117059880","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：