A hybrid collaborative filtering recommendation model-based on complex attribute of goods
Lanfeng Zhou, Hanwei Tang, Tianzhen Dong · 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) · 2017
Collaborative filtering as the most widely used, the most recommendation algorithm, the shortcomings inherent in the data sparse, cold start and others has been greatly improved, but few studies based on commodity price to improve the prediction accuracy. At the same time, facing the full e-commerce market network Navy, the ratings and reviews also indirectly led to reducing the accuracy of prediction. Therefore, considering comprehensively the subjective scoring of users and the objective scoring of products, the paper puts forward a hybrid collaborative filtering recommendation model by combing situational pre-filtering, social network theories and experts' opinions. And through experiments, the model has higher forecast accuracy than the traditional collaborative filtering, and is more suitable for the commodity with complex attributes.