Collaborative Filtering Recommendation Algorithm for User Interest and Relationship Based on Score Matrix

Abstract

An improved collaborative filtering recommendation algorithm is proposed to solve the problem of sparse and low recommendation accuracy of traditional collaborative filtering recommendation algorithm. User preferences and user trust relationships are used to calculate the user's preferences for the project, and the user ratings are used to fill the scoring matrix with unrated items. Considering the change of user interest and user relationship, we introduce time based interest weight function and preference degree to the project similarity computation and recommendation process, and identify the nearest neighbor set, so as to achieve the best recommendation. User preferences and user trust relationships are used to calculate the user's preferences for the project, and the user ratings are used to fill the scoring matrix with unrated items.

Improvement of Collaborative Filtering Recommendation Algorithm Based on Intuitionistic Fuzzy Reasoning Under Missing Data

“…The collaborative filtering recommendation algorithm is the earliest and well-applied recommendation algorithm, which is used primarily for preference prediction and item recommendation. By mining a specified user's historical behavior data, the algorithm analyzes the user's interest, finds other users with similar interest in the user set, synthesizes the evaluation of these related users on certain items, forms the system's preference prediction for the items, and finally recommends items with similar interest for the user [19], [20].…”