Context-aware Recommendation System with Anonymous User Profile Learning
نویسندگان
چکیده
Recommendation system requests huge personal data, including personal information, purchase history, social tag/network, professional/personal preference, etc. Privacy preservation gets more and more concern in modern recommendation system. In this paper, we use surfing data in single session with context-aware learning to generate an anonymous user profile for recommendation, which yields very encouraging results. User profile is generated based on prelearned hotel profile with pre-assigned weights. Two major behaviors are captured to learn the temporary user profile, which are search and view functions. A novel factor called “irrelevance” is created to measure the sensitivity of user to each item of hotel profile based on the surfing behaviors. A case study on a flight/hotel inquiring and booking website with different application scenarios and results are analyzed. Keywords–Context awareness; recommendation system; eservice; user profile
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