Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval最新文献_第10页

Session details: Session 4B: Recommending 会话详情:会话4B:推荐

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/3255925

Paul Benett

引用次数: 0

Subsequence Search in Event-Interval Sequences 事件间隔序列的子序列搜索

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767778

Orestis Kostakis, A. Gionis

引用次数: 10

Modeling Website Topic Cohesion at Scale to Improve Webpage Classification 大规模建模网站主题内聚以改进网页分类

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767834

D. Eswaran, Paul N. Bennett, Joseph J. Pfeiffer

引用次数: 1

Leveraging Procedural Knowledge for Task-oriented Search 利用程序知识进行面向任务的搜索

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767744

Zi Yang, Eric Nyberg

{"title":"Leveraging Procedural Knowledge for Task-oriented Search","authors":"Zi Yang, Eric Nyberg","doi":"10.1145/2766462.2767744","DOIUrl":"https://doi.org/10.1145/2766462.2767744","url":null,"abstract":"Many search engine users attempt to satisfy an information need by issuing multiple queries, with the expectation that each result will contribute some portion of the required information. Previous research has shown that structured or semi-structured descriptive knowledge bases (such as Wikipedia) can be used to improve search quality and experience for general or entity-centric queries. However, such resources do not have sufficient coverage of procedural knowledge, i.e. what actions should be performed and what factors should be considered to achieve some goal; such procedural knowledge is crucial when responding to task-oriented search queries. This paper provides a first attempt to bridge the gap between two evolving research areas: development of procedural knowledge bases (such as wikiHow) and task-oriented search. We investigate whether task-oriented search can benefit from existing procedural knowledge (search task suggestion) and whether automatic procedural knowledge construction can benefit from users' search activities (automatic procedural knowledge base construction). We propose to create a three-way parallel corpus of queries, query contexts, and task descriptions, and reduce both problems to sequence labeling tasks. We propose a set of textual features and structural features to identify key search phrases from task descriptions, and then adapt similar features to extract wikiHow-style procedural knowledge descriptions from search queries and relevant text snippets. We compare our proposed solution with baseline algorithms, commercial search engines, and the (manually-curated) wikiHow procedural knowledge; experimental results show an improvement of +0.28 to +0.41 in terms of Precision@8 and mean average precision (MAP).","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"146 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":"132199745","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}

引用次数: 33

WEMAREC: Accurate and Scalable Recommendation through Weighted and Ensemble Matrix Approximation WEMAREC:基于加权和集合矩阵逼近的精确可扩展推荐

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767718

Chao Chen, Dongsheng Li, Yingying Zhao, Q. Lv, L. Shang

{"title":"WEMAREC: Accurate and Scalable Recommendation through Weighted and Ensemble Matrix Approximation","authors":"Chao Chen, Dongsheng Li, Yingying Zhao, Q. Lv, L. Shang","doi":"10.1145/2766462.2767718","DOIUrl":"https://doi.org/10.1145/2766462.2767718","url":null,"abstract":"Matrix approximation is one of the most effective methods for collaborative filtering-based recommender systems. However, the high computation complexity of matrix factorization on large datasets limits its scalability. Prior solutions have adopted co-clustering methods to partition a large matrix into a set of smaller submatrices, which can then be processed in parallel to improve scalability. The drawback is that the recommendation accuracy is lower as the submatrices only contain subsets of the user-item rating information. This paper presents WEMAREC, a weighted and ensemble matrix approximation method for accurate and scalable recommendation. It builds upon the intuition that (sub)matrices containing more frequent samples of certain user/item/rating tend to make more reliable rating predictions for these specific user/item/rating. WEMAREC consists of two important components: (1) a weighting strategy that is computed based on the rating distribution in each submatrix and applied to approximate a single matrix containing those submatrices; and (2) an ensemble strategy that leverages user-specific and item-specific rating distributions to combine the approximation matrices of multiple sets of co-clustering results. Evaluations using real-world datasets demonstrate that WEMAREC outperforms state-of-the-art matrix approximation methods in recommendation accuracy (0.5?11.9% on the MovieLens dataset and 2.2--13.1% on the Netflix dataset) with 3--10X improvement on scalability.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"68 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":"132210162","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}

引用次数: 43

Differences in Eye-Tracking Measures Between Visits and Revisits to Relevant and Irrelevant Web Pages 访问相关和不相关网页之间的眼动追踪测量差异

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767795

J. Gwizdka, Yinglong Zhang

引用次数: 34

SIGIR 2015 Workshop on Temporal, Social and Spatially-aware Information Access (#TAIA2015) SIGIR 2015时间、社会和空间感知信息访问研讨会(#TAIA2015)

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767860

K. Berberich, James Caverlee, Miles Efron, C. Hauff, Vanessa Murdock, Milad Shokouhi, B. Thomee

引用次数: 1

Large-scale Image Retrieval using Neural Net Descriptors 基于神经网络描述符的大规模图像检索

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767868

David Novak, Michal Batko, P. Zezula

引用次数: 29

A Test Collection for Spoken Gujarati Queries 古吉拉特语口语查询测试集

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767791

Douglas W. Oard, Rashmi Sankepally, Jerome White, A. Jansen, Craig Harman

引用次数: 0

From Web Search Relevance to Vertical Search Relevance 从网络搜索相关性到垂直搜索相关性

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2776787

Yi Chang

引用次数: 1