Neural Collaborative Ranking
Bo Song, Xin Yang, Yi Cao, Congfu Xu

TL;DR
This paper introduces Neural Network based Collaborative Ranking (NCR), a novel deep learning framework for personalized recommendation that models pairwise user preferences without assuming non-interacted items are negative, showing superior results.
Contribution
It proposes a new neural ranking framework that relaxes negative sampling assumptions and effectively captures latent preferences using neural networks.
Findings
NCR outperforms several state-of-the-art recommendation methods.
The neural pairwise preference model effectively captures user-item interactions.
Experimental results on real datasets demonstrate the model's superior performance.
Abstract
Recommender systems are aimed at generating a personalized ranked list of items that an end user might be interested in. With the unprecedented success of deep learning in computer vision and speech recognition, recently it has been a hot topic to bridge the gap between recommender systems and deep neural network. And deep learning methods have been shown to achieve state-of-the-art on many recommendation tasks. For example, a recent model, NeuMF, first projects users and items into some shared low-dimensional latent feature space, and then employs neural nets to model the interaction between the user and item latent features to obtain state-of-the-art performance on the recommendation tasks. NeuMF assumes that the non-interacted items are inherent negative and uses negative sampling to relax this assumption. In this paper, we examine an alternative approach which does not assume that…
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Taxonomy
TopicsRecommender Systems and Techniques · Image Retrieval and Classification Techniques · Advanced Graph Neural Networks
