Probabilistic sequential POIs recommendation via check-in data

While on the go, people are using their phones as a personal concierge discovering what is around and deciding what to do. Mobile phone has become a recommendation terminal customized for individuals. While existing research predominantly focuses on one-step recommendation---recommending the next single activity according to current context, this work moves one step beyond by recommending a series of activities, which is a package of sequential Points of Interest (POIs). The recommended POIs are not only relevant to user context (i.e., current location, time, and check-in), but also personalized to his/her check-in history. We presents a probabilistic approach, which is highly motivated from a large-scale commercial mobile check-in data analysis, to ranking a list of sequential POI categories (e.g., "Japanese food" and "bar") and POIs (e.g., "I love sushi"). The approach enables users to plan consecutive activities on the move. Specifically, the probabilistic recommendation approach estimates the transition probability from one POI to another, conditioned on current context and check-in history in a Markov chain. To alleviate the discritization error and sparsity problem, we further introduce context collaboration and integrate prior information. Experiments on over 100k real-world check-in records and 20k POIs validate the effectiveness of the proposed approach.