Attraction recommendation: Towards personalized tourism via collective intelligence

Travel recommendation systems can tackle the problem of information overload and recommend proper attractions on the basis of users' preferences. Most existing travel recommendation systems utilized travel history, yet neglected the low frequency, of tourism and the flexible styles of attractions in different cities, which will cause the inaccuracy in both collaborative filtering recommendation and content-based recommendation. To deal with this issue, we propose a novel personalized travel recommendation framework by leveraging explicit user interaction and multi-modality travel information. As far as we known, it is the first time that attractions are recommended by user interaction and collective intelligence in a unified framework. Specifically, we first collect heterogeneous travel information by multi-user sharing, which is regarded as collective intelligence to provide reliable references by other travelers. Second, valuable knowledge is mined from collective intelligence in order to filter out the noisy data and make travel information structured. Then, personalized attraction similarity (PAS) model is designed to suggest attractions through fusing heterogeneous information with weighted adaptation and simultaneously considering explicit user interaction. Finally, context information such as the user's location is well adopted to refine the recommendation that may influence the user's choice at a particular moment. Experimental results on pseudo-relevance data and real-world data demonstrate that our method gains promising performance in terms of effectiveness as well as efficiency. (C) 2015 Elsevier B.V. All rights reserved.