【目的】在個性化推薦中,考慮物品質量和用戶評分傾向性對用戶打分的影響,提高推薦效果。【方法】提出一種改進的協同過濾推薦算法:利用物品質量評估算法實現了用戶評分修正,可以改進查找與用戶興趣相似的最近鄰過程,并在此基礎上進行推薦。【結果】利用MovieLens數據集進行測試,與傳統協同過濾算法相比,改進算法的MAE提高4.7%;與其他幾種改進算法相比,精確度均有不同程度的提高。【局限】只關注現有的評分修正,并沒有考慮用戶的興趣漂移,在一定程度上影響推薦效果。【結論】本文提出的算法推薦結果更加精確,有效地減少了物品質量和用戶評分傾向性對推薦結果的影響,提高了推薦質量。

[Objective] This paper proposes a modified collaborative filtering algorithm, aiming to improve the results of personalized recommendations. [Methods] First, we evaluated item quality and corrected user ratings based on their previous records. Then, we identified users with similar interests to generate better recommendations. [Results] We tested the new algorithm on MovieLens dataset and found the MAE was 4.7% higher than those of the traditional or other modified methods. [Limitations] The new algorithm does not address the interests drifting issues. [Conclusions]The proposed algorithm could recommend products to consumers more effectively.