Paper Info
Title
A hybrid recommender system using artificial neural networks.
Abstract
Neural network based hybrid recommender system utilizing review metadata is proposed.The system optimizes model hyper-parameters to minimize log-loss.Validate predictive capability of model against heterogeneous business categories. 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.
Year
DOI
Venue
2017
10.1016/j.eswa.2017.04.046
Expert Syst. Appl.
Keywords
Field
DocType
Information retrieval,Artificial neural networks,Recommender systems,Supervised learning
Recommender system,Data mining,Metadata,Stochastic gradient descent,Collaborative filtering,Computer science,Filter (signal processing),Supervised learning,Artificial intelligence,Deep learning,Artificial neural network,Machine learning
Journal
Volume
Issue
ISSN
83
C
0957-4174
Citations 
PageRank 
References 
12
0.51
31
Authors
3
Name
Order
Citations
PageRank
Tulasi K. Paradarami1120.51
Nathaniel D Bastian2233.31
Jennifer L. Wightman3120.51