TIIREC: A Tensor Approach for Tag-Driven Item Recommendation with Sparse User Generated Content

Handle URI:
http://hdl.handle.net/10754/623687
Title:
TIIREC: A Tensor Approach for Tag-Driven Item Recommendation with Sparse User Generated Content
Authors:
Yu, Lu; Huang, Junming; Zhou, Ge; Liu, Chuang; Zhang, Zi-Ke
Abstract:
In 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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Yu 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.
Publisher:
Elsevier BV
Journal:
Information Sciences
Issue Date:
17-May-2017
DOI:
10.1016/j.ins.2017.05.025
Type:
Article
ISSN:
0020-0255
Sponsors:
This 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).
Additional Links:
http://www.sciencedirect.com/science/article/pii/S002002551730734X
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorYu, Luen
dc.contributor.authorHuang, Junmingen
dc.contributor.authorZhou, Geen
dc.contributor.authorLiu, Chuangen
dc.contributor.authorZhang, Zi-Keen
dc.date.accessioned2017-05-22T06:58:04Z-
dc.date.available2017-05-22T06:58:04Z-
dc.date.issued2017-05-17en
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.en
dc.identifier.issn0020-0255en
dc.identifier.doi10.1016/j.ins.2017.05.025en
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.en
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).en
dc.publisherElsevier BVen
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S002002551730734Xen
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/en
dc.subjectRecommender Systemsen
dc.subjectCollaborative Filteringen
dc.subjectMatrix Factorizationen
dc.subjectLatent Factor Modelen
dc.subjectCollaborative Retrievalen
dc.subjectTop-K Rankingen
dc.titleTIIREC: A Tensor Approach for Tag-Driven Item Recommendation with Sparse User Generated Contenten
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalInformation Sciencesen
dc.eprint.versionPost-printen
dc.contributor.institutionAlibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, PR Chinaen
dc.contributor.institutionWeb Sciences Center, University of Electronic Science and Technology of China, Chinaen
kaust.authorYu, Luen
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