E-commerce personalized recommendation analysis by deeply-learned clustering

With the development of Internet, personalized recommendation has played an important role in human modern lives. Since the number of users’ data is always large-scale, traditional algorithms cannot effectively cope with e-commerce personalized recommendation tasks. This paper proposes an e-commerce product personalized recommendation system based on learning clustering representation. Traditional kNN method has limitation in selecting adjacent object set. Thus, we introduce neighbor factor and time function and leverage dynamic selection model to select the adjacent object set. We combine RNN as well as attention mechanism to design the e-commerce product recommendation system. Comprehensive experimental results have shown the effectiveness of our proposed method.