Deep Item-based Collaborative Filtering for Top-N Recommendation

By Feng Xue, Xiangnan He, Xiang Wang, Jiandong Xu, Kai Liu, Richang Hong. ACM Trans. Inf. Syst., 2018

313 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/eb2ae9f753c4318c0a052218af0a2ba1fdfd8969

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Xiangnan He
2. Xiang Wang
3. Meng Wang
4. Le Wu
5. Kun Zhang
6. Yongdong Zhang
7. Yan Li
8. Kuan Deng

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Meng Wang
4. Le Wu
5. Kun Zhang
6. Tat-seng Chua
7. Yongdong Zhang
8. Kuan Deng

In Management Science and Operations Research

1. Xiang Wang
2. Meng Wang
3. Xiangnan He
4. Le Wu
5. Kun Zhang
6. Yongdong Zhang
7. Kuan Deng
8. Yan Li

Citations in the corpus

• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, methodology): “ers integrate neural attention mechanism into history representation learning [25], [26], [42].”
• Neural Graph Collaborative Filtering (2019, unknown): “Intuitively, the interacted items provide direct evidence on a user’s preference [16, 37]; analogously, the users that consume an item can be treated as the item’s features and used to measure the collaborative similarity of two items.”
• LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation (2020, background): “hat historical items have different contributions to shape personal interest. Towards this end, attention mechanisms areintroducedtocapturethevaryingcontributions,suchasACF[3], NAIS [17], and DeepICF [43], to automatically learn the importance of each historical item. When revisiting historical inte”