Neural Collaborative Preference Learning With Pairwise Comparisons

doi:10.1109/TMM.2020.3006373

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Neural Collaborative Preference Learning With Pairwise Comparisons

2021-01-01

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DOI：10.1109/TMM.2020.3006373

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Abstract

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Collaborative Ranking (CR), as an effective recommendation framework, has attracted increasing attention in recent years. Most CR methods simply adopt the inner product between user/item embeddings as the rating score function, with an assumption that the interacted items are preferred to non-interacted ones. However, such fixed score functions and assumptions might not be sufficient to capture the real preference ranking list from the complicated interactions in real-world data. To alleviate this issue, we develop a novel collaborative ranking framework that learns an arbitrary utility function for item ranking with user preference concerned. In the core of our framework, a neural network is employed to model the utility function for personalized ranking with the strength of its nonlinearity. On top of this, we further adopt a pairwise ranking loss for user-item pairs to preserve the preference order of items for users. Besides, such a utility function enables us to generate the final top-K preference list in a much easier way. Finally, extensive experiments on four real-world datasets show the validity of our proposed method.

Keywords:

Recommender system

collaborative ranking

neural networks

preference ranking

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