Neighbor-based Data Weight Collaborative Filtering

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 neighbor- based 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.