Neural Embedding Features for Point-of-Interest Recommendation
ASONAM 2019
The focus of point-of-interest recommendation techniques is to suggest a venue to a given user that would match the users' interests and is likely to be adopted by the user. Given the multitude of venues and the sparsity of user check-ins, the problem of recommending venues has shown to be a difficult task. Existing literature has already explored various types of features such as geographical distribution, social structure and temporal behavioral patterns to make a recommendation. In this paper, we propose a new set of features derived based on the neural embeddings of venues and users. We show how the neural embeddings for users and venues can be jointly learnt based on the prior check-in sequence of users and then be used to define three types of features, namely user, venue, and user-venue interaction features. These features are integrated into a feature-based matrix factorization model. Our experiments show that the features defined over the user and venue embeddings are effective for venue recommendation.
Point-of-Interest Recommendation Using Heterogeneous Link Prediction
EDBT 2018
Venue recommendation in location-based social networks is
among the more important tasks that enhances user participation
on the social network. Despite its importance, earlier research
have shown that the accurate recommendation of appropriate
venues for users is a difficult task specially given the highly sparse
nature of user check-in information. In this paper, we show how
a comprehensive set of user and venue related information can
be methodically incorporated into a heterogeneous graph representation
based on which the problem of venue recommendation
can be efficiently formulated as an instance of the heterogeneous
link prediction problem on the graph.We systematically compare
our proposed approach with several strong baselines and show
that our work, which is computationally less-intensive compared
to the baselines, is able to shows improved performance in terms
of precision and f-measure.
