Representation Extraction and Deep Neural Recommendation for Collaborative Filtering

• URL: http://arxiv.org/abs/2012.04979v1
• Date: Wed, 9 Dec 2020 11:15:23 GMT
• Title: Representation Extraction and Deep Neural Recommendation for Collaborative Filtering
• Authors: Arash Khoeini, Saman Haratizadeh, Ehsan Hoseinzade
• Abstract summary: This paper investigates the usage of novel representation learning algorithms to extract users and items representations from rating matrix. We propose a modular algorithm consisted of two main phases: REpresentation eXtraction and a deep neural NETwork (RexNet) RexNet is not dependent on unstructured auxiliary data such as visual and textual information, instead, it uses only the user-item rate matrix as its input.
• Score: 9.367612782346207
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many Deep Learning approaches solve complicated classification and regression problems by hierarchically constructing complex features from the raw input data. Although a few works have investigated the application of deep neural networks in recommendation domain, they mostly extract entity features by exploiting unstructured auxiliary data such as visual and textual information, and when it comes to using user-item rating matrix, feature extraction is done by using matrix factorization. As matrix factorization has some limitations, some works have been done to replace it with deep neural network. but these works either need to exploit unstructured data such item's reviews or images, or are specially designed to use implicit data and don't take user-item rating matrix into account. In this paper, we investigate the usage of novel representation learning algorithms to extract users and items representations from rating matrix, and offer a deep neural network for Collaborative Filtering. Our proposed approach is a modular algorithm consisted of two main phases: REpresentation eXtraction and a deep neural NETwork (RexNet). Using two joint and parallel neural networks in RexNet enables it to extract a hierarchy of features for each entity in order to predict the degree of interest of users to items. The resulted predictions are then used for the final recommendation. Unlike other deep learning recommendation approaches, RexNet is not dependent to unstructured auxiliary data such as visual and textual information, instead, it uses only the user-item rate matrix as its input. We evaluated RexNet in an extensive set of experiments against state of the art recommendation methods. The results show that RexNet significantly outperforms the baseline algorithms in a variety of data sets with different degrees of density.

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