Abstract: Traditional collaborative filtering algorithms suffer from various shortcomings such as insufficient accuracy due to rating prediction errors, and limited scalability of the algorithm due to the need to cache numerous similarity results. To address these challenges, this paper proposes a user fuzzy preference similarity measurement method based on item label vectors. The method uses fuzzy logic to measure the likes and dislikes of different users towards different item content labels, and represents the similarity between users as a vector across these labels. The corresponding similarity calculation and rating prediction formulas are then designed based on the relationship between this vector and the predicted target item content labels. Experimental results on two commonly used datasets demonstrate significant improvements over recent algorithms. Specifically, compared to existing methods, the proposed approach yields a 12.38% enhancement in mean absolute error, indicating improved rating prediction accuracy, a 7.85% increase in F1 value, indicating enhanced preference prediction accuracy, and a 17.47% improvement in half-life utility, reflecting sorting accuracy. Furthermore, the algorithm proposed in this paper reduces the number of neighbors required for the optimal prediction of each metric, thereby shortening the running time of the algorithm and effectively enhancing the scalability.

Key words: recommendation system, collaborative filtering, similarity, fuzzy logic, label vector