Novel Boosting Frameworks to Improve the Performance of Collaborative Filtering
Novel Boosting Frameworks to Improve the Performance of Collaborative Filtering
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发表时间：
2013-10
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通讯作者：
Xiaotian Jiang;Zhendong Niu;Jiamin Guo;Ghulam Mustafa;Zi-Han Lin;Baomi Chen;Qian Zhou
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作者：
Xiaotian Jiang;Zhendong Niu;Jiamin Guo;Ghulam Mustafa;Zi-Han Lin;Baomi Chen;Qian Zhou
推荐系统通常基于协作过滤。以往的协同过滤研究主要集中在单一推荐方法或不同推荐方法的混合。考虑到稀疏性，推荐错误率，样本权重更新和潜力的问题，我们适应AdaBoost，并提出了两个新的协同过滤的提升框架。每个框架都结合了多个同构的过滤器，这些过滤器基于相同的协同过滤算法，具有不同的样本权重。我们使用七种流行的协作过滤算法来评估两个框架与两个不同规模的MovieLens数据集。实验结果表明，该框架提高了协同过滤的性能。
Recommender systems are often based on collaborative ltering. Previous researches on collaborative ltering mainly focus on one single recommender or formulating hybrid with dierent approaches. In consideration of the problems of sparsity, recommender error rate, sample weight update, and potential, we adapt AdaBoost and propose two novel boosting frameworks for collaborative ltering. Each of the frameworks combines multiple homogeneous recommenders, which are based on the same collaborative ltering algorithm with dierent sample weights. We use seven popular collaborative ltering algorithms to evaluate the two frameworks with two MovieLens datasets of dierent scale. Experimental result shows the proposed frameworks improve the performance of collaborative ltering.