基于共同评分的协同过滤算法

The collaborative filtering based on co-ratings

协同过滤是目前电子商务推荐系统中使用最广泛最成功的一种个性化推荐算法。受数据稀疏性影响,传统协同过滤算法在较小共同评分项集上计算出的相似度不能准确反映用户间的相似关系,严重影响了推荐系统的精度。针对该问题,在分析共同评分分布及其与相似度关系的基础上,提出了基于共同评分的协同过滤算法,无须计算相似度,直接将共同评分作为最近邻选择标准。MovieLens实验表明该算法能明显提高预测结果的准确性和覆盖率。

Collaborative filtering is one of the most extensive and successful personalized recommendation algorithm in e-commerce recommendation system. Affected by data sparsity, the traditional collaborative filtering algorithms does not reflect the interest similarity of uses calculating similarity between users on the smaller set of common rated items accurately, seriously affecting the accuracy of recommendation system. To solve this problem, collaborative filtering algorithm based on co-ratings was proposed by analyzing the distribution of co-ratings and relationship be- tween co-ratings and similarity, directly using co-ratings as a criterion to select nearest neighbor without calculating similarity. Experiments on MovieLens datasets show that the algorithm can make a substantial increase in prediction accuracy and recommendation coverage.