Personalized Collaborative Filtering Recommendation Algorithm based on Linear Regression

Abstract

A personalized collaborative filtering recommendation algorithm based on a linear regression model. Constructing the linear regression model based on the user label weight matrix and the user-item scoring matrix, and the gradient regression method is used to minimize the value of the linear regression cost function to obtain the item label. Then, the user and item label weight matrix are substituted into the linear regression model to obtain the user's predicted scores for all unrated items. Using the SlopeOne algorithm principle, calculate the difference between the predicted score and the actual score, and the predicted result is adjusted to obtain the final predicted score. Sort the results and recommend Top-N items to target users. Experiments show that the algorithm's recommendation accuracy is significantly improved than the traditional collaborative filtering algorithm. And the recommended results are interpretable and can meet the individual needs of users.