Research On Personalized Recommendation Technology Based On User Recommend Strategy Preference

Personalized recommendation has been an important research topic in the field of e-commerce. As for users'different preferences on different recommend strategies in personalized recommendation, this thesis researches on personalized recommendation technology based on user recommend strategy preference and designs user recommend strategy preference based recommendation model.Our main researches in this thesis are:1) Design a user recommend strategy preference based recommendation model called as USPR. In this model, a user provides his recommend strategy preferences and get recommendations using the recommend strategy selected by the user.2) On the basis of recommendation model USPR, a recommendation model based on user recommend strategy preference with self-adaptive mechanism called as SA-USPR is proposed. This model considers user short-term and long-term recommend strategy preference and adaptively adjusts user recommend strategy preferences based on users' feedback score.3) Design and implement a recommender system USPSys based on SA-USPR.Our main conrtibution in this thesis is the proposal of a user recommend strategy preference self-adaptive mechanism based on users' feedback score.The researches in this thesis would be valuable to the studies of users' preference on recommend strategies and its' recommendation.