An adaptive hybrid model based on improved recommendation algorithms

Recommender system uses knowledge discovery techniques to filters information for users, generate personalized recommendations, and help users find the information they need. On the other hand, it helps the company achieve personalized marketing goal, thus helps promote sales, and creates more profits for them. This paper mainly studies the various current recommendation algorithms, including collaborative filtering, association rules, and makes some improvements. Besides, this paper presents an adaptive hybrid model based on a variety of improved recommendation algorithms. Experimental results show that compared with traditional recommendation algorithms, the improved algorithm proposed in this paper has higher accuracy and validity.