# Collaborative Generative Hashing for Marketing and Fast Cold-start Recommendation

arXiv: https://arxiv.org/abs/2011.00953 · HTML: https://mlsanity.com/paper/2011.00953/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [Exploring Customer Price Preference and Product Profit Role in Recommender Systems](https://mlsanity.com/paper/2203.06641/) (2022)
- [A Scalable Recommendation Engine for New Users and Items](https://mlsanity.com/paper/2209.06128/) (2022)

## Abstract (arXiv)

Cold-start has being a critical issue in recommender systems with the explosion of data in e-commerce. Most existing studies proposed to alleviate the cold-start problem are also known as hybrid recommender systems that learn representations of users and items by combining user-item interactive and user/item content information. However, previous hybrid methods regularly suffered poor efficiency bottlenecking in online recommendations with large-scale items, because they were designed to project users and items into continuous latent space where the online recommendation is expensive. To this end, we propose a collaborative generated hashing (CGH) framework to improve the efficiency by denoting users and items as binary codes, then fast hashing search techniques can be used to speed up the online recommendation. In addition, the proposed CGH can generate potential users or items for marketing application where the generative network is designed with the principle of Minimum Description Length (MDL), which is used to learn compact and informative binary codes. Extensive experiments on two public datasets show the advantages for recommendations in various settings over competing baselines and analyze its feasibility in marketing application.