# HCFRec: Hash Collaborative Filtering via Normalized Flow with Structural Consensus for Efficient Recommendation

arXiv: https://arxiv.org/abs/2205.12042 · HTML: https://mlsanity.com/paper/2205.12042/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 24 May 2022.

## Builds on (most cited first, 12 of 12)

- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (2018)
- [A Survey on Learning to Hash](https://mlsanity.com/paper/1606.00185/) (2016)
- [End-to-End Object Detection with Adaptive Clustering Transformer](https://mlsanity.com/paper/2011.09315/) (2020)
- [Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation](https://mlsanity.com/paper/2202.04893/) (2022)
- [A Unified Framework for Cross-Domain and Cross-System Recommendations](https://mlsanity.com/paper/2108.07976/) (2021)
- [Content-aware Neural Hashing for Cold-start Recommendation](https://mlsanity.com/paper/2006.00617/) (2020)
- [Recurrent Binary Embedding for GPU-Enabled Exhaustive Retrieval from Billion-Scale Semantic Vectors](https://mlsanity.com/paper/1802.06466/) (2018)
- [Compositional Coding for Collaborative Filtering](https://mlsanity.com/paper/1905.03752/) (2019)
- [Item Recommendation with Variational Autoencoders and Heterogenous Priors](https://mlsanity.com/paper/1807.06651/) (2018)
- [Distributed Collaborative Hashing and Its Applications in Ant Financial](https://mlsanity.com/paper/1804.04918/) (2018)

## Built on by (most cited first, 1 of 1)

- [Federated Hash Projected Latent Factor Learning](https://mlsanity.com/paper/2606.26192/) (2026)

## Abstract (arXiv)

The ever-increasing data scale of user-item interactions makes it challenging for an effective and efficient recommender system. Recently, hash-based collaborative filtering (Hash-CF) approaches employ efficient Hamming distance of learned binary representations of users and items to accelerate recommendations. However, Hash-CF often faces two challenging problems, i.e., optimization on discrete representations and preserving semantic information in learned representations. To address the above two challenges, we propose HCFRec, a novel Hash-CF approach for effective and efficient recommendations. Specifically, HCFRec not only innovatively introduces normalized flow to learn the optimal hash code by efficiently fit a proposed approximate mixture multivariate normal distribution, a continuous but approximately discrete distribution, but also deploys a cluster consistency preserving mechanism to preserve the semantic structure in representations for more accurate recommendations. Extensive experiments conducted on six real-world datasets demonstrate the superiority of our HCFRec compared to the state-of-art methods in terms of effectiveness and efficiency.