Enhanced Contextual Recommendation Using Social Media Data

Making recommendations to a user which are more refined, personalized and contextually appropriate, has become an important problem in a variety of domains. Contextual Point of Interest (POI) recommendation is of particular interest for travel and tourism, to suggest places to visit for a user traveling to a new city. In this paper, I propose an approach which frames contextual POI recommendation as a traditional document ranking problem, where I represent each POI as a document. A significant aspect of this research investigates the generation of an enriched document representation for a POI, by harvesting information such as descriptions, users' reviews, and ratings from the web. I propose a log-linear retrieval model for document ranking using Kernel Density Estimation (KDE) which will eventually rank POIs. Additionally, I propose to use social media, such as Twitter, to supplement the recommendation process. People's spontaneous and unfiltered opinions about a POI give valuable information about that POI which is different from other online reviews. This unique type of user data will be used to enrich the document representation for POIs, and it is possible to incorporate the effect of social media into my retrieval model. I follow the TREC Contextual Suggestion track setup for my evaluation methodology.