IMCRec: A multi-criteria framework for personalized point-of-interest recommendations

Abstract

User preferences on geographical positions and categories of points-of-interest (POIs) have been exploited for POI recommendations. However, in reality, the users’ visiting behaviors are also affected by the attributes of POIs, which reflect the important features of the POIs. Integrating the geographical, category and attribute criteria for POI recommendations suffers challenges of (1) modeling user preferences on multiple attributes with different values, (2) integrating conflicting multiple criteria for POI recommendations, and (3) learning personalized weights from one’s check-in histories with heterogeneous data types. To address these challenges, we propose a new personalized POI recommendation framework, called iMCRec, which recommends POIs by integrating user preferences on geographical, category and attribute criteria with personalized weights. In iMCRec, preference models are first built for individual user’s geographical, category, and attribute preferences. Especially, we propose an attribute preference model by considering both preferences on values of each attribute and importance of different attributes. A sophisticated collaborative filtering method is also developed to fuse the opinion of similar users under the three criteria separately. To learn the personalized weights on the three criteria, a weight learning strategy is proposed. We then develop a fast Multi-Criteria Decision Making (MCDM) algorithm, called FastMCDM, to integrate the three conflicting criteria and efficiently generate top-N POIs as recommendations. Finally, we evaluate the performance of our iMCRec through extensive experiments using two real-world datasets collected from Yelp. Experimental results show that iMCRec not only performs better than the state-of-the-art POI recommendation techniques, but it is also more flexible in dealing with the scale problem and more effective in learning personalized weights than other multi-criteria-based techniques.