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dc.contributor.authorYu, Lu
dc.contributor.authorHuang, Junming
dc.contributor.authorZhou, Ge
dc.contributor.authorLiu, Chuang
dc.contributor.authorZhang, Zi-Ke
dc.date.accessioned2017-05-22T06:58:04Z
dc.date.available2017-05-22T06:58:04Z
dc.date.issued2017-05-17
dc.identifier.citationYu L, Huang J, Zhou G, Liu C, Zhang Z-K (2017) TIIREC: A Tensor Approach for Tag-Driven Item Recommendation with Sparse User Generated Content. Information Sciences. Available: http://dx.doi.org/10.1016/j.ins.2017.05.025.
dc.identifier.issn0020-0255
dc.identifier.doi10.1016/j.ins.2017.05.025
dc.identifier.urihttp://hdl.handle.net/10754/623687
dc.description.abstractIn recent years, tagging system has become a building block o summarize the content of items for further functions like retrieval or personalized recommendation in various web applications. One nontrivial requirement is to precisely deliver a list of suitable items when users interact with the systems via inputing a specific tag (i.e. a query term). Different from traditional recommender systems, we need deal with a collaborative retrieval (CR) problem, where both characteristics of retrieval and recommendation should be considered to model a ternary relationship involved with query× user× item. Recently, several works are proposed to study CR task from users’ perspective. However, they miss a significant challenge raising from the sparse content of items. In this work, we argue that items will suffer from the sparsity problem more severely than users, since items are usually observed with fewer features to support a feature-based or content-based algorithm. To tackle this problem, we aim to sufficiently explore the sophisticated relationship of each query× user× item triple from items’ perspective. By integrating item-based collaborative information for this joint task, we present an alternative factorized model that could better evaluate the ranks of those items with sparse information for the given query-user pair. In addition, we suggest to employ a recently proposed bayesian personalized ranking (BPR) algorithm to optimize latent collaborative retrieval problem from pairwise learning perspective. The experimental results on two real-world datasets, (i.e. Last.fm, Yelp), verified the efficiency and effectiveness of our proposed approach at top-k ranking metric.
dc.description.sponsorshipThis work was partially supported by Natural Science Foundation of China (Grant Nos. 61673151 and 61503110), Zhejiang Provincial Natural Science Foundation of China (Grant Nos. LY14A050001 and LQ16F030006).
dc.publisherElsevier BV
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S002002551730734X
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Information Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences, [, , (2017-05-17)] DOI: 10.1016/j.ins.2017.05.025 . © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectRecommender Systems
dc.subjectCollaborative Filtering
dc.subjectMatrix Factorization
dc.subjectLatent Factor Model
dc.subjectCollaborative Retrieval
dc.subjectTop-K Ranking
dc.titleTIIREC: A Tensor Approach for Tag-Driven Item Recommendation with Sparse User Generated Content
dc.typeArticle
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalInformation Sciences
dc.eprint.versionPost-print
dc.contributor.institutionAlibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, PR China
dc.contributor.institutionWeb Sciences Center, University of Electronic Science and Technology of China, China
kaust.personYu, Lu
refterms.dateFOA2019-05-17T00:00:00Z
dc.date.published-online2017-05-17
dc.date.published-print2017-10


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