Please use this identifier to cite or link to this item: https://doi.org/10.1145/3314578
Title: Deep Item-based Collaborative Filtering for Top-N Recommendation
Authors: Feng Xue
Xiangnan He 
Xiang Wang 
Jiandong Xu
Kai Liu
Richang Hong 
Keywords: Collaborative filtering
item-based CF
neural networks
deep learning
implicit feedback
Issue Date: 11-Nov-2018
Publisher: Association for Computing Machinery
Citation: Feng Xue, Xiangnan He, Xiang Wang, Jiandong Xu, Kai Liu, Richang Hong (2018-11-11). Deep Item-based Collaborative Filtering for Top-N Recommendation. ACM Transactions on Information Systems 37 (3). ScholarBank@NUS Repository. https://doi.org/10.1145/3314578
Abstract: Item-based Collaborative Filtering (ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user's profile with the items that the user has consumed, ICF recommends items that are similar to the user's profile. With the prevalence of machine learning in recent years, significant processes have been made for ICF by learning item similarity (or representation) from data. Nevertheless, we argue that most existing works have only considered linear and shallow relationships between items, which are insufficient to capture the complicated decision-making process of users. In this article, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationships among items. Going beyond modeling only the second-order interaction (e.g., similarity) between two items, we additionally consider the interaction among all interacted item pairs by using nonlinear neural networks. By doing this, we can effectively model the higher-order relationship among items, capturing more complicated effects in user decision-making. For example, it can differentiate which historical itemsets in a user's profile are more important in affecting the user to make a purchase decision on an item. We treat this solution as a deep variant of ICF, thus term it as DeepICF. To justify our proposal, we perform empirical studies on two public datasets from MovieLens and Pinterest. Extensive experiments verify the highly positive effect of higher-order item interaction modeling with nonlinear neural networks. Moreover, we demonstrate that by more fine-grained second-order interaction modeling with attention network, the performance of our DeepICF method can be further improved. © 2019 Association for Computing Machinery.
Source Title: ACM Transactions on Information Systems
URI: https://scholarbank.nus.edu.sg/handle/10635/168418
ISSN: 10468188
DOI: 10.1145/3314578
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