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Register a new userCOMET: Convolutional Dimension Interaction for Collaborative Filtering
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Zhuoyi Lin
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Latent factor models play a dominant role among recommendation techniques. However, most of the existing latent factor models assume both historical interactions and embedding dimensions are independent of each other, and thus regrettably ignore the high-order interaction information among historical interactions and embedding dimensions. In this paper, we propose a novel latent factor model called COMET (COnvolutional diMEnsion inTeraction), which simultaneously model the high-order interaction patterns among historical interactions and embedding dimensions. To be specific, COMET stacks the embeddings of historical interactions horizontally at first, which results in two embedding maps. In this way, internal interactions and dimensional interactions can be exploited by convolutional neural networks with kernels of different sizes simultaneously. A fully-connected multi-layer perceptron is then applied to obtain two interaction vectors. Lastly, the representations of users and items are enriched by the learnt interaction vectors, which can further be used to produce the final prediction. Extensive experiments and ablation studies on various public implicit feedback datasets clearly demonstrate the effectiveness and the rationality of our proposed method.
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A growing proportion of human interactions are digitized on social media platforms and subjected to algorithmic decision-making, and it has become increasingly important to ensure fair treatment from these algorithms. In this work, we investigate gender bias in collaborative-filtering recommender systems trained on social media data. We develop neural fair collaborative filtering (NFCF), a practical framework for mitigating gender bias in recommending sensitive items (e.g. jobs, academic concentrations, or courses of study) using a pre-training and fine-tuning approach to neural collaborative filtering, augmented with bias correction techniques. We show the utility of our methods for gender de-biased career and college major recommendations on the MovieLens dataset and a Facebook dataset, respectively, and achieve better performance and fairer behavior than several state-of-the-art models.
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