A Factored Relevance Model for Contextual Point-of-Interest Recommendation

Abstract

The challenge of providing personalized and contextually appropriate recommendations to a user is faced in a range of use-cases, e.g., recommendations for movies, places to visit, articles to read etc. In this paper, we focus on one such application, namely that of suggesting 'points of interest' (POIs) to a user given her current location, by leveraging relevant information from her past preferences. An automated contextual recommendation algorithm is likely to work well if it can extract information from the preference history of a user (exploitation) and effectively combine it with information from the user's current context (exploration) to predict an item's 'usefulness' in the new context. To balance this trade-off between exploration and exploitation, we propose a generic unsupervised framework involving a factored relevance model (FRLM), comprising two distinct components, one corresponding to the historical information from past contexts, and the other pertaining to the information from the local context. Our experiments are conducted on the TREC contextual suggestion (TREC-CS) 2016 dataset. The results of our experiments demonstrate the effectiveness of our proposed approach in comparison to a number of standard IR and recommender-based baselines.