Improved Collaborative Filtering Recommendation Model

The rise of Web 2.0 has brought about significant changes in online behavior patterns from users. Users are no longer just consumers of information, but the main producers of information. Behavioral information such as clicks and purchases will be recorded by the Internet. Therefore, user interaction data exploded at an exponential rate, leading to the problem of information overload, and the recommendation system is an effective strategy to solve this problem. In response to the above problems, this paper innovates on the basis of traditional recommendation algorithms and proposes an improved collaborative filtering recommendation model. Taking into account the different contributions of user historical interaction data to predicting the target product, the standard attention mechanism cannot play a good role due to the large difference in the length of user historical data. This paper proposes to introduce a smoothing parameter to improve this problem. At the same time, the paper introduces the residual network to learn nonlinear features to overcome the problem of network degradation in the deep network model and help improve the final recommendation result. This paper conducts experiments and evaluations on real public data sets, and compares the performance with current related models. Experiments have verified the rationality and effectiveness of the model design in this paper, and the recommendation accuracy has been significantly improved.