Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management 2014
DOI: 10.1145/2661829.2661998
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Leveraging Social Connections to Improve Personalized Ranking for Collaborative Filtering

Abstract: Recommending products to users means estimating their preferences for certain items over others. This can be cast either as a problem of estimating the rating that each user will give to each item, or as a problem of estimating users' relative preferences in the form of a ranking. Although collaborative-filtering approaches can be used to identify users who rate and rank products similarly, another source of data that informs us about users' preferences is their set of social connections. Both rating-and ranki… Show more

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Cited by 348 publications
(209 citation statements)
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“…Previous literature has shown that the geographical information of venues (e.g. [3,7,12,12,23,25,27]) and social correlation [12,15,16,21,23,28,30] as well as textual content of comments (e.g. [17,29]) are important factors to improve the e ectiveness of venue recommendation systems.…”
Section: Related Workmentioning
confidence: 99%
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“…Previous literature has shown that the geographical information of venues (e.g. [3,7,12,12,23,25,27]) and social correlation [12,15,16,21,23,28,30] as well as textual content of comments (e.g. [17,29]) are important factors to improve the e ectiveness of venue recommendation systems.…”
Section: Related Workmentioning
confidence: 99%
“…Bayesian Personalised Ranking (BPR) [19] is a pairwise ranking-based model that is widely implemented and extended to leverage implicit feedback to generate the top-K venue recommendations (e.g. [14,21,25,30]). …”
Section: Introductionmentioning
confidence: 99%
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