A hybrid recommender system using artificial neural networks |
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Affiliation: | 1. School of Engineering and Applied Science, Aston University, Birmingham, B4 7ET, UK;2. Institute of Image Communication and Network Engineering, Shanghai Jiaotong University, China;3. Department of Computer Science, Shanghai Jiaotong University, China |
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Abstract: | In the context of recommendation systems, metadata information from reviews written for businesses has rarely been considered in traditional systems developed using content-based and collaborative filtering approaches. Collaborative filtering and content-based filtering are popular memory-based methods for recommending new products to the users but suffer from some limitations and fail to provide effective recommendations in many situations. In this paper, we present a deep learning neural network framework that utilizes reviews in addition to content-based features to generate model based predictions for the business-user combinations. We show that a set of content and collaborative features allows for the development of a neural network model with the goal of minimizing logloss and rating misclassification error using stochastic gradient descent optimization algorithm. We empirically show that the hybrid approach is a very promising solution when compared to standalone memory-based collaborative filtering method. |
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