A Collaborative Ranking Model with Multiple Location-based Similarities for Venue Suggestion
This work addresses the sparsity issue in venue suggestion for users on location-based social networks, representing an incremental improvement over existing collaborative ranking approaches.
The paper tackles the sparsity problem in collaborative ranking for venue recommendation by integrating multiple similarity measures based on venue contents and locations, and experiments on the TREC Contextual Suggestion dataset show it outperforms state-of-the-art methods.
Recommending venues plays a critical rule in satisfying users' needs on location-based social networks. Recent studies have explored the idea of adopting collaborative ranking (CR) for recommendation, combining the idea of learning to rank and collaborative filtering. However, CR suffers from the sparsity problem, mainly because it associates similar users based on exact matching of the venues in their check-in history. Even though research in collaborative filtering has shown that considering auxiliary information such as geographical influence, helps the model to alleviate the sparsity problem, the same direction still needs to be explored in CR. In this work, we present a CR framework that focuses on the top of the ranked list while integrating an arbitrary number of similarity functions between venues as it learns the model's parameters. We further introduce three example similarity measures based on venues' contents and locations. Incorporating cross-venue similarity measures into the model enhances the latent associations between users as similar venues are also taken into account while associating users with each other. Our experiments on the TREC Contextual Suggestion dataset show that our proposed CR model beats other state-of-the-art venue suggestion methods.