Abstract

Personalized recommender systems, as effective approaches for alleviating information overload, have received substantial attention in the last decade. Learning effective latent factors plays the most important role in recommendation methods. Several recent works extracted latent factors from user-generated content such as ratings and reviews and suffered from the sparsity problem and the unbalanced distribution problem. To tackle these problems, we enrich the latent representations by incorporating user-generated content and item raw content. Deep neural networks have emerged as very appealing in learning effective representations in many applications. In this paper, we propose a novel deep neural architecture named DeepFusion to jointly learn user and item representations from numerical ratings, textual reviews, and item metadata. In this framework, we utilize multiple types of deep neural networks that are best suited for each type of heterogeneous inputs and introduce an extra layer to obtain the joint representations for users and items. Experiments conducted on the Amazon product data demonstrate that our approach outperforms multiple state-of-the-art baselines. We provide further insight into the design selections and hyperparameters of our recommendation method. In addition, we further explore the relative importance of various item metadata information on improving the rating prediction performance towards personalized product recommendation, which is extremely valuable for feature extraction in practice.

Highlights

  • With the exploding growth of the network scale and the number of products, it is difficult for customers to deal with the large amount of available information

  • E main contributions of our work are summarized as follows: (i) We propose a novel personalized recommendation method based on deep learning and multiview fusion, called DeepFusion, for rating prediction task in product recommendation. e method is capable of incorporating user-generated content and item raw content including numerical ratings, textual reviews, and item metadata in a unified space

  • We used the mean absolute error (MAE) to evaluate our model, which has been widely used in previous studies

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Summary

Introduction

With the exploding growth of the network scale and the number of products, it is difficult for customers to deal with the large amount of available information. E success of many e-commerce companies is due to their accurate and personalized product recommender systems, such as Amazon, eBay, Yelp, and Netflix [2]. Researchers have found that additional data sources beyond ratings are extremely helpful in personalized recommendation. In the Netflix dataset [8], the number of movies that have been rated by users is only approximately 1 percent of the total number of movies. The distribution of user-item interaction data is highly unbalanced. According to Anderson [9], due to the long-tail effect, only a few users interact with a large number of items, while most users rarely, or never, interact

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