Personalized Ranking for Context-Aware Venue Suggestion
This work addresses the need for improved venue suggestions for users in recommendation systems, though it appears incremental as it builds on existing datasets and tasks.
The paper tackles the problem of personalized and context-aware venue recommendation by introducing a novel user-modeling approach that uses scoring functions based on venue content, reviews, and user context. The method significantly outperforms state-of-the-art approaches on the TREC Contextual Suggestion Track dataset.
Making personalized and context-aware suggestions of venues to the users is very crucial in venue recommendation. These suggestions are often based on matching the venues' features with the users' preferences, which can be collected from previously visited locations. In this paper we present a novel user-modeling approach which relies on a set of scoring functions for making personalized suggestions of venues based on venues content and reviews as well as users context. Our experiments, conducted on the dataset of the TREC Contextual Suggestion Track, prove that our methodology outperforms state-of-the-art approaches by a significant margin.