# Learning Similarity Preserving Binary Codes for Recommender Systems

arXiv: https://arxiv.org/abs/2204.08569 · HTML: https://mlsanity.com/paper/2204.08569/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 18 Apr 2022.

## Builds on (most cited first, 13 of 13)

- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches](https://mlsanity.com/paper/1907.06902/) (2019)
- [HashNet: Deep Learning to Hash by Continuation](https://mlsanity.com/paper/1702.00758/) (2017)
- [Deep Cross-Modal Hashing](https://mlsanity.com/paper/1602.02255/) (2016)
- [Learning to Hash with Graph Neural Networks for Recommender Systems](https://mlsanity.com/paper/2003.01917/) (2020)
- [Unsupervised Generative Adversarial Cross-modal Hashing](https://mlsanity.com/paper/1712.00358/) (2017)
- [Hashing-Accelerated Graph Neural Networks for Link Prediction](https://mlsanity.com/paper/2105.14280/) (2021)
- [Content-aware Neural Hashing for Cold-start Recommendation](https://mlsanity.com/paper/2006.00617/) (2020)
- [NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing](https://mlsanity.com/paper/1805.05361/) (2018)
- [Compositional Coding for Collaborative Filtering](https://mlsanity.com/paper/1905.03752/) (2019)
- [Unsupervised Multi-Index Semantic Hashing](https://mlsanity.com/paper/2103.14460/) (2021)
- [Unsupervised Neural Generative Semantic Hashing](https://mlsanity.com/paper/1906.00671/) (2019)
- [Projected Hamming Dissimilarity for Bit-Level Importance Coding in Collaborative Filtering](https://mlsanity.com/paper/2103.14455/) (2021)

## Abstract (arXiv)

Hashing-based Recommender Systems (RSs) are widely studied to provide scalable services. The existing methods for the systems combine three modules to achieve efficiency: feature extraction, interaction modeling, and binarization. In this paper, we study an unexplored module combination for the hashing-based recommender systems, namely Compact Cross-Similarity Recommender (CCSR). Inspired by cross-modal retrieval, CCSR utilizes Maximum a Posteriori similarity instead of matrix factorization and rating reconstruction to model interactions between users and items. We conducted experiments on MovieLens1M, Amazon product review, Ichiba purchase dataset and confirmed CCSR outperformed the existing matrix factorization-based methods. On the Movielens1M dataset, the absolute performance improvements are up to 15.69% in NDCG and 4.29% in Recall. In addition, we extensively studied three binarization modules: sign, scaled tanh, and sign-scaled tanh. The result demonstrated that although differentiable scaled tanh is popular in recent discrete feature learning literature, a huge performance drop occurs when outputs of scaled tanh are forced to be binary.