基于综合相似度迁移的协同过滤算法

Collaborative filtering algorithm based on integrated similarity transfer

摘要:

数据稀疏性问题是传统协同过滤算法的主要瓶颈之一。迁移学习通常是利用目标领域与辅助领域的潜在关系，对辅助领域进行知识迁移，以此来提高目标领域的推荐质量。现有的基于相似度迁移模型，普遍只利用了用户评分信息，并且在评分相似度计算上忽略了用户评分标准个性差异。针对这些问题，提出了一种综合相似度迁移模型，在相似度计算上，即利用了用户评分信息同时也利用了用户属性信息，并且考虑了用户间对满意度的打分标准的差异性，采用了用户评分分布一致性来衡量用户评分相似度的方法，提高了相似度计算的准确性，从而提高了数据迁移的质量。实验结果表明，该模型较其他算法能比较有效地缓解数据稀疏性问题。

Abstract:

Data sparsity is one of the most challenges for traditional collaborative filtering algorithms. Transfer learning methods used the potential relationship between the target domain and the auxiliary domain to transfer the auxiliary domain knowledge, so as to improve the recommendation accuracy of the target domain. The existing transfer model based on similarity generally used only the rating information, and ignores the difference of user rating. To solve these problems, a transfer model based on comprehensive similarity is proposed, used user rating information and user attribute information, taking account of the difference of user rating, used the consistency of ratings, distribution to measure user rating similarity, improved the accuracy of similarity computation, thus improved the quality of data migration. Experimental results showed that the proposed model can effectively alleviate the sparsity of data compared with other algorithms.