2020/11/02 by Yan Zhang, Ivor W. Tsang, Zhang, Yan +3
Computer Science · #Advanced Image and Video Retrieval Techniques #Caching and Content Delivery #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Recommender Systems and Techniques #cs.IR
paper · pdf · doi:10.48550/arxiv.2011.00953
11 pages, 8 figures
arxiv created 2020/11/02 · openalex publication_date 2020/11/02 · arxiv updated 2020/11/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.