# DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System

arXiv: https://arxiv.org/abs/1901.04704 · HTML: https://mlsanity.com/paper/1901.04704/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2019.

## Built on by (most cited first, 15 of 15)

- [Agentic Web: Weaving the Next Web with AI Agents](https://mlsanity.com/paper/2507.21206/) (2025)
- [DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation](https://mlsanity.com/paper/2206.10848/) (2022)
- [A Survey on Fairness-aware Recommender Systems](https://mlsanity.com/paper/2306.00403/) (2023)
- [Federated Recommendation with Additive Personalization](https://mlsanity.com/paper/2301.09109/) (2023)
- [TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations](https://mlsanity.com/paper/2111.07378/) (2021)
- [Unified Representation Learning for Multi-Intent Diversity and Behavioral Uncertainty in Recommender Systems](https://mlsanity.com/paper/2509.04694/) (2025)
- [G3SR: Global Graph Guided Session-based Recommendation](https://mlsanity.com/paper/2203.06467/) (2022)
- [Dual-embedding based Neural Collaborative Filtering for Recommender Systems](https://mlsanity.com/paper/2102.02549/) (2021)
- [Simultaneous Learning of the Inputs and Parameters in Neural Collaborative Filtering](https://mlsanity.com/paper/2203.07463/) (2022)
- [Cross-modal Place Recognition in Image Databases using Event-based Sensors](https://mlsanity.com/paper/2307.01047/) (2023)
- [Reasoning-guided Collaborative Filtering with Language Models for Explainable Recommendation](https://mlsanity.com/paper/2602.05544/) (2026)
- [BCFNet: A Balanced Collaborative Filtering Network with Attention Mechanism](https://mlsanity.com/paper/2103.06105/) (2021)
- [Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach](https://mlsanity.com/paper/2204.11602/) (2022)
- [One-class Recommendation Systems with the Hinge Pairwise Distance Loss and Orthogonal Representations](https://mlsanity.com/paper/2208.14594/) (2022)
- [Epistemic Uncertainty-aware Recommendation Systems via Bayesian Deep Ensemble Learning](https://mlsanity.com/paper/2504.10753/) (2025)

## Abstract (arXiv)

In general, recommendation can be viewed as a matching problem, i.e., match proper items for proper users. However, due to the huge semantic gap between users and items, it's almost impossible to directly match users and items in their initial representation spaces. To solve this problem, many methods have been studied, which can be generally categorized into two types, i.e., representation learning-based CF methods and matching function learning-based CF methods. Representation learning-based CF methods try to map users and items into a common representation space. In this case, the higher similarity between a user and an item in that space implies they match better. Matching function learning-based CF methods try to directly learn the complex matching function that maps user-item pairs to matching scores. Although both methods are well developed, they suffer from two fundamental flaws, i.e., the limited expressiveness of dot product and the weakness in capturing low-rank relations respectively. To this end, we propose a general framework named DeepCF, short for Deep Collaborative Filtering, to combine the strengths of the two types of methods and overcome such flaws. Extensive experiments on four publicly available datasets demonstrate the effectiveness of the proposed DeepCF framework.