A hybrid approach for improving predictive accuracy of collaborative filtering algorithms

Recommender systems represent a class of personalized systems that aim at predicting a user's interest on information items available in the application domain, operating upon user-driven ratings on items and/or item features. One of the most widely used recommendation methods is collaborative filtering that exploits the assumption that users who have agreed in the past in their ratings on observed items will eventually agree in the future. Despite the success of recommendation methods and collaborative filtering in particular, in real-world applications they suffer from the insufficient number of available ratings, which significantly affects the accuracy of prediction. In this paper, we propose recommendation approaches that follow the collaborative filtering reasoning and utilize the notion of lifestyle as an effective user characteristic that can group consumers in terms of their behavior as indicated in consumer behavior and marketing theory. Emanating from a basic lifestyle-based recommendation algorithm we incrementally proceed to the development of hybrid recommendation approaches that address certain dimensions of the sparsity problem and empirically evaluate them providing further evidence of their effectiveness.