Abstract: This article aims to improve the effectiveness of the algorithm， starts from the psychological needs of users， locates the implicit role group of users， and researches the personalized recommendation algorithms. From a theoretical point of view， the research in this paper effectively ensures the diversity requirements of recommendation systems and improves the accuracy of algorithms to a certain extent. It expands the relevant theory of implicit preference to address the phenomenon of preference evolution. Through verification in real data， multiple experimental evaluation indicators have been significantly improved. This not only provides a theoretical basis and reference for recommendation systems， but also improves the accuracy of recommendation results. It has broad application prospects. From a practical point of view， the classification of users in this article is no longer limited to ordinary social attributes， but can further explore users’ psychological needs， obtains more accurate and diverse recommendation results， improves user satisfaction and experience. Enterprises can guide users to change their interests， increase their loyalty and value， improve their lifecycle， and increase their profits.

Key words:  , recommendation algorithm； implicit role； film recommendation； recommended effect