A hybrid recommender system using artificial neural networks

作者：Paradarami Tulasi K^*; Bastian Nathaniel D; Wightman Jennifer L

来源：Expert Systems with Applications, 2017, 83: 300-313.

DOI：10.1016/j.eswa.2017.04.046

• Information retrieval
• Artificial neural networks
• Recommender systems
• Supervised learning

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.

• 出版日期2017-10-15
• 单位西北大学