Papers › Predicting ratings in multi-criteria recommender systems via a collective factor model
Predicting ratings in multi-criteria recommender systems via a collective factor model
Ge Fan, Chaoyun Zhang, Junyang Chen, Kaishun Wu
In a multi-criteria recommender system, users are allowed to give an overall rating to an item and provide a score on each of its attribute. Finding an effective method to exploit a user s multi-criteria ratings to predict the overall rating becomes one of the most important challenges. Among traditional solutions, most of the architectures are not designed in an end-to-end manner. These approaches initially estimate a user s multi-criteria scores, and train a separate model to predict the user s overall rating. This introduces extra training overhead, and the overall prediction accuracy is usually sensitive to its multi-criteria ratings models. In this paper, we propose a collective model to predict user s overall rating by automatically weighting each of the predicted multi-criteria sub-scores. The proposed architecture integrates the multi-criteria ratings and the overall rating models in a unified system, which allows to train and perform multi-criteria recommendation in an end-to-end manner. Experiments on 3 real datasets show that our proposed architectures achieve up to 13.14% lower prediction error over baseline approaches.
Code
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Recommendation Systems | BeerAdvocate | CFM | MAE | 0.5833 | #1 of 1 | Archive leaderboard | report |
| Recommendation Systems | BeerAdvocate | CFM | RMSE | 0.5833 | #1 of 1 | Archive leaderboard | report |
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