Using topic models with browsing history in hybrid collaborative filtering recommender system: Experiments with user ratings

Abstract

Personalizing user experience in recommender systems is possible when there is sufficient information about the user. But when new users join the system, the unavailability of information about these users, referred to as cold-start, inhibits the functionality of a recommender system. We propose an enhancement to the user-based approaches, which are extensively used in the recommender system literature. Our approach combines Wikipedia data and browsing history into the recommendation algorithm. Specifically, we generate topics by using the Latent Dirichlet Allocation (LDA) models on the Wikipedia data, and then use the topics on user browsing history to extract user preferences. Our evaluation employs five approaches and tests their performance in terms of prediction and classification accuracy. We conduct experiments in two domains (movies and restaurants), to gather user ratings and their browsing history for evaluation. Results from both experiments favor our proposed enhancement.

Keywords

• User browsing history
• Latent Dirichlet allocation
• User-based recommender system
• Topic modelling
• Text mining
• User-generated content