# Neural Interactive Collaborative Filtering

arXiv: https://arxiv.org/abs/2007.02095 · HTML: https://mlsanity.com/paper/2007.02095/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2020.

## Built on by (most cited first, 24 of 24)

- [Advances and Challenges in Conversational Recommender Systems: A Survey](https://mlsanity.com/paper/2101.09459/) (2021)
- [Hypergraph Contrastive Collaborative Filtering](https://mlsanity.com/paper/2204.12200/) (2022)
- [AutoLoss: Automated Loss Function Search in Recommendations](https://mlsanity.com/paper/2106.06713/) (2021)
- [DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation](https://mlsanity.com/paper/2206.10848/) (2022)
- [Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy](https://mlsanity.com/paper/2204.07433/) (2022)
- [Talk the Walk: Synthetic Data Generation for Conversational Music Recommendation](https://mlsanity.com/paper/2301.11489/) (2023)
- [Metaphorical User Simulators for Evaluating Task-oriented Dialogue Systems](https://mlsanity.com/paper/2204.00763/) (2022)
- [Beyond Single Items: Exploring User Preferences in Item Sets with the Conversational Playlist Curation Dataset](https://mlsanity.com/paper/2303.06791/) (2023)
- [Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations](https://mlsanity.com/paper/2204.00327/) (2022)
- [Meta Policy Learning for Cold-Start Conversational Recommendation](https://mlsanity.com/paper/2205.11788/) (2022)
- [Self-Supervised Bot Play for Conversational Recommendation with Justifications](https://mlsanity.com/paper/2112.05197/) (2021)
- [Towards Explainable Conversational Recommender Systems](https://mlsanity.com/paper/2305.18363/) (2023)
- [Inferring Substitutable and Complementary Products with Knowledge-Aware Path Reasoning based on Dynamic Policy Network](https://mlsanity.com/paper/2110.03276/) (2021)
- [Centrality-aware Product Retrieval and Ranking](https://mlsanity.com/paper/2410.15930/) (2024)
- [Flow Matching based Sequential Recommender Model](https://mlsanity.com/paper/2505.16298/) (2025)
- [Data-Efficient Reinforcement Learning for Malaria Control](https://mlsanity.com/paper/2105.01620/) (2021)
- [Dual Policy Learning for Aggregation Optimization in Graph Neural Network-based Recommender Systems](https://mlsanity.com/paper/2302.10567/) (2023)
- [Exploring ChatGPT for Next-generation Information Retrieval: Opportunities and Challenges](https://mlsanity.com/paper/2402.11203/) (2024)
- [Generating Self-Serendipity Preference in Recommender Systems for Addressing Cold Start Problems](https://mlsanity.com/paper/2204.12651/) (2022)
- [Is Meta-Learning the Right Approach for the Cold-Start Problem in Recommender Systems?](https://mlsanity.com/paper/2308.08354/) (2023)
- [Towards Validating Long-Term User Feedbacks in Interactive Recommendation Systems](https://mlsanity.com/paper/2308.11137/) (2023)
- [Interactive Graph Convolutional Filtering](https://mlsanity.com/paper/2309.01453/) (2023)
- [Spattack: Subgroup Poisoning Attacks on Federated Recommender Systems](https://mlsanity.com/paper/2507.06258/) (2025)
- [Fine-Tuning Diffusion-Based Recommender Systems via Reinforcement Learning with Reward Function Optimization](https://mlsanity.com/paper/2511.06937/) (2025)

## Abstract (arXiv)

In this paper, we study collaborative filtering in an interactive setting, in which the recommender agents iterate between making recommendations and updating the user profile based on the interactive feedback. The most challenging problem in this scenario is how to suggest items when the user profile has not been well established, i.e., recommend for cold-start users or warm-start users with taste drifting. Existing approaches either rely on overly pessimistic linear exploration strategy or adopt meta-learning based algorithms in a full exploitation way. In this work, to quickly catch up with the user's interests, we propose to represent the exploration policy with a neural network and directly learn it from the feedback data. Specifically, the exploration policy is encoded in the weights of multi-channel stacked self-attention neural networks and trained with efficient Q-learning by maximizing users' overall satisfaction in the recommender systems. The key insight is that the satisfied recommendations triggered by the exploration recommendation can be viewed as the exploration bonus (delayed reward) for its contribution on improving the quality of the user profile. Therefore, the proposed exploration policy, to balance between learning the user profile and making accurate recommendations, can be directly optimized by maximizing users' long-term satisfaction with reinforcement learning. Extensive experiments and analysis conducted on three benchmark collaborative filtering datasets have demonstrated the advantage of our method over state-of-the-art methods.