Paper Info
Title
Do "Also-Viewed" Products Help User Rating Prediction?
Abstract
For online product recommendation engines, learning high-quality product embedding that captures various aspects of the product is critical to improving the accuracy of user rating prediction. In recent research, in conjunction with user feedback, the appearance of a product as side information has been shown to be helpful for learning product embedding. However, since a product has a variety of aspects such as functionality and specifications, taking into account only its appearance as side information does not suffice to accurately learn its embedding. In this paper, we propose a matrix co-factorization method that leverages information hidden in the so-called \"also-viewed\" products, i.e., a list of products that has also been viewed by users who have viewed a target product. \"Also-viewed\" products reflect various aspects of a given product that have been overlooked by visually-aware recommendation methods proposed in past research. Experiments on multiple real-world datasets demonstrate that our proposed method outperforms state-of-the-art baselines in terms of user rating prediction. We also perform classification on the product embedding learned by our method, and compare it with a state-of-the-art baseline to demonstrate the superiority of our method in generating high-quality product embedding that better represents the product.
Year
DOI
Venue
2017
10.1145/3038912.3052581
WWW
Keywords
Field
DocType
Collaborative filtering, Product embedding, Online shopping
Data mining,World Wide Web,Embedding,Collaborative filtering,Computer science,Matrix (mathematics),Baseline (configuration management),Side information,Artificial intelligence,Machine learning
Conference
Citations 
PageRank 
References 
10
0.49
22
Authors
4
Name
Order
Citations
PageRank
Chanyoung Park116312.04
Dong Hyun Kim21647.55
Jinoh Oh330315.32
Hwanjo Yu41715114.02