Neighbor - based Data Weight Collaborative Filtering 1 Xuesong Yin

Existing Collaborative Filtering (CF) based recommendation approaches suffer from the following issues: (1) the number of resources accessed and evaluated by each user is only such a very small part that leads to sparse rating matrix; (2) dynamic change of user interest makes recommended resources largely deviate from the need of the user. To address these problems, we develop a novel algorithm titled as neighborbased data weight CF recommendation of learning resources (NARR). Firstly, the neighbor of the user or the neighbor of the resource is selected in terms of the rating matrix; secondly, we compute data weight for representing dynamic change of user interest; finally, we use neighbor relationship and data weight in the objective of CF-based algorithm to choose learning resources. Experiments results show the feasibility and effectiveness of the proposed method.