Collaborative Generative Hashing for Marketing and Fast Cold-start Recommendation

TL;DR

This paper introduces a collaborative generated hashing framework that encodes users and items as binary codes for efficient, scalable recommendations and marketing applications, addressing cold-start issues in large-scale e-commerce systems.

Contribution

It proposes a novel hashing-based method that improves recommendation efficiency and generates potential users or items using a MDL principle, outperforming existing hybrid approaches.

Findings

Outperforms baseline methods in recommendation accuracy.

Enables fast online recommendation with binary hashing.

Demonstrates effectiveness in marketing applications.

Abstract

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.