Abstract Collaborative filtering cannot provide personalized recommendation of new items due to the lack of scoring information for incomplete cold start(ICS) or the absence of scoring information for complete cold start(CCS). To address this problem, a recommendation algorithm combing interrelationship mining and collaborative filtering(CF) is proposed. Firstly, relationship features of items are extracted by using interrelationship mining, and the number of available attributes is expanded according to multiple binary relations between attributes. A neighbor selection approach based on interrelationship mining is proposed to increase the diversity of neighboring items. Finally, CF is integrated to solve CCS and ICS problems and then personalized recommendation of new items is realized. The experiments on two real world datasets indicate that the proposed algorithm solves the new item cold start problems of recommender systems effectively.
Fund:Supported by National Natural Science Foundation of China(No.61976109), Natural Science Foundation of Liaoning Province (No.20180550542), Dalian Science and Technology Innovation Fund(No.2018J12GX047), Dalian Key Laboratory Special Fund
Corresponding Authors:
ZHANG Zhipeng, Ph.D., lecturer. His research interests include data mining and recommender system.
About author:: REN Yonggong, Ph.D., professor. His research interests include artificial intelligence and data mining.SHI Jiaxin, master student. Her research interests include artificial intelligence and data mining.
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2020, Vol. 33 
