Collaborative Filtering Meets Mobile Recommendation: A User-Centered Approach

With the increasing popularity of location tracking services such as GPS, more and more mobile data are being accumulated. Based on such data, a potentially useful service is to make timely and targeted recommendations for users on places where they might be interested to go and activities that they are likely to conduct. For example, a user arriving in Beijing might wonder where to visit and what she can do around the Forbidden City. A key challenge for such recommendation problems is that the data we have on each individual user might be very limited, while to make useful and accurate recommendations, we need extensive annotated location and activity information from user trace data. In this paper, we present a new approach, known as user-centered collaborative location and activity filtering (UCLAF), to pull many users' data together and apply collaborative filtering to find like-minded users and like-patterned activities at different locations. We model the userlocation-activ...

• Type: Conference
• Year: 2010
• Where: AAAI
• Authors: Vincent Wenchen Zheng, Bin Cao, Yu Zheng, Xing Xie, Qiang Yang
• Keywords: AAAI 2010, Intelligent Agents, Location Tracking Services, Mobile Data, User Trace Data