# Deep Item-based Collaborative Filtering for Top-N Recommendation

arXiv: https://arxiv.org/abs/1811.04392 · HTML: https://mlsanity.com/paper/1811.04392/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2018.

## Built on by (most cited first, 20 of 20)

- [Recommendation Unlearning](https://mlsanity.com/paper/2201.06820/) (2022)
- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [Multi-Behavior Sequential Recommendation with Temporal Graph Transformer](https://mlsanity.com/paper/2206.02687/) (2022)
- [Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-Training](https://mlsanity.com/paper/2305.07633/) (2023)
- [CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations](https://mlsanity.com/paper/2604.14586/) (2026)
- [Collaborative Reflection-Augmented Autoencoder Network for Recommender Systems](https://mlsanity.com/paper/2201.03158/) (2022)
- [Recommendation Is a Dish Better Served Warm](https://mlsanity.com/paper/2508.07856/) (2025)
- [Dual-embedding based Neural Collaborative Filtering for Recommender Systems](https://mlsanity.com/paper/2102.02549/) (2021)
- [Learning Complex Users' Preferences for Recommender Systems](https://mlsanity.com/paper/2107.01529/) (2021)
- [Modeling Reference-dependent Choices with Graph Neural Networks](https://mlsanity.com/paper/2408.11302/) (2024)
- [Represent Items by Items: An Enhanced Representation of the Target Item for Recommendation](https://mlsanity.com/paper/2104.12483/) (2021)
- [One-Bit Matrix Completion with Differential Privacy](https://mlsanity.com/paper/2110.00719/) (2021)
- [Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach](https://mlsanity.com/paper/2204.11602/) (2022)
- [Implicit ZCA Whitening Effects of Linear Autoencoders for Recommendation](https://mlsanity.com/paper/2308.13536/) (2023)
- [Epsilon non-Greedy: A Bandit Approach for Unbiased Recommendation via Uniform Data](https://mlsanity.com/paper/2310.04855/) (2023)
- [Reviewing Developments of Graph Convolutional Network Techniques for Recommendation Systems](https://mlsanity.com/paper/2311.06323/) (2023)
- [Adaptive spectral graph wavelets for collaborative filtering](https://mlsanity.com/paper/2312.03167/) (2023)
- [GPU-accelerated Multi-relational Parallel Graph Retrieval for Web-scale Recommendations](https://mlsanity.com/paper/2502.11490/) (2025)
- [Leveraging Geometric Insights in Hyperbolic Triplet Loss for Improved Recommendations](https://mlsanity.com/paper/2508.11978/) (2025)
- [LLM-I2I: Boost Your Small Item2Item Recommendation Model with Large Language Model](https://mlsanity.com/paper/2512.21595/) (2025)

## Abstract (arXiv)

Item-based Collaborative Filtering(short for 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 relationship between items, which are insufficient to capture the complicated decision-making process of users. In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship 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. Through this way, 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.