Abstract
Recommender systems always recommend items to a user based on predicted ratings. However, due to biases of different users, it is not easy to know a user’s preference through the predicted ratings. This paper defines a user preference relationship based on the user’s ratings to improve the recommendation accuracy. By considering group information, we extend the preference relationship to form four types of correlations including (user, item), (user group, item), (user, item group), and (user group, item group). And then, this paper exploits pair-wise comparisons between two items or two group of items for a singer user or a group of users. The gradient descent algorithm is used to learn latent factors on partial orders to make recommendations. Experimental results show the effectiveness of the proposed method.
This work was partly supported by National Natural Science Foundation of China (61562009, 61420106005).
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Zhu, K., Huang, J., Zhong, N. (2016). Exploiting Group Pairwise Preference Influences for Recommendations. In: Flores, V., et al. Rough Sets. IJCRS 2016. Lecture Notes in Computer Science(), vol 9920. Springer, Cham. https://doi.org/10.1007/978-3-319-47160-0_39
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DOI: https://doi.org/10.1007/978-3-319-47160-0_39
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