CoBaR: Confidence-Based Recommender
This work addresses a specific bottleneck in recommender systems for users and items, but it is incremental as it builds on existing non-personalized methods.
The paper tackled the problem of fixed neighborhood sizes in collaborative filtering by proposing a confidence-based extension with hierarchical clustering to create optimal user groups, resulting in improved performance over traditional algorithms across four public datasets.
Neighborhood-based collaborative filtering algorithms usually adopt a fixed neighborhood size for every user or item, although groups of users or items may have different lengths depending on users' preferences. In this paper, we propose an extension to a non-personalized recommender based on confidence intervals and hierarchical clustering to generate groups of users with optimal sizes. The evaluation shows that the proposed technique outperformed the traditional recommender algorithms in four publicly available datasets.