Recommending a ranked list of interesting venues to users based on their preferences has become a key functionality in LocationBased Social Networks (LBSNs) such as Yelp and Gowalla. Bayesian Personalised Ranking (BPR) is a popular pairwise recommendation technique that is used to generate the ranked list of venues of interest to a user, by leveraging the user's implicit feedback such as their check-ins as instances of positive feedback, while randomly sampling other venues as negative instances. To alleviate the sparsity that a ects the usefulness of recommendations by BPR for users with few check-ins, various approaches have been proposed in the literature to incorporate additional sources of information such as the social links between users, the textual content of comments, as well as the geographical location of the venues. However, such approaches can only readily leverage one source of additional information for negative sampling. Instead, we propose a novel Personalised Ranking Framework with Multiple sampling Criteria (PRFMC) that leverages both geographical in uence and social correlation to enhance the e ectiveness of BPR. In particular, we apply a multi-centre Gaussian model and a power-law distribution method, to capture geographical in uence and social correlation when sampling negative venues, respectively. Finally, we conduct comprehensive experiments using three large-scale datasets from the Yelp, Gowalla and Brightkite LBSNs. e experimental results demonstrate the e ectiveness of fusing both geographical in uence and social correlation in our proposed PRFMC framework and its superiority in comparison to BPR-based and other similar ranking approaches. Indeed, our PRFMC approach a ains a 37% improvement in MRR over a recently proposed approach that identi es negative venues only from social links.
REF incorporate friends' selections and the geographical proximity of venues in the negative sampling strategy of BPR to produce recommendations.
A Personalised Ranking Framework with Multiple Sampling Criteria for Venue Recommendation
CIKM '17
Computer Science