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, 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.

中文翻译：

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用于营销和快速冷启动推荐的协作生成哈希

随着电子商务中数据的爆炸式增长，冷启动一直是推荐系统中的一个关键问题。大多数为缓解冷启动问题而提出的现有研究也被称为混合推荐系统，它通过结合用户-项目交互和用户/项目内容信息来学习用户和项目的表示。然而，以前的混合方法在大规模项目的在线推荐中经常遇到效率低下的瓶颈，因为它们旨在将用户和项目投影到在线推荐昂贵的连续潜在空间中。为此，我们提出了一种协作生成哈希（CGH）框架，通过将用户和项目表示为二进制代码来提高效率，然后可以使用快速哈希搜索技术来加速在线推荐。此外，所提出的 CGH 可以为营销应用程序生成潜在用户或项目，其中生成网络的设计遵循最小描述长度原则，用于学习紧凑和信息丰富的二进制代码。对两个公共数据集的大量实验显示了在各种设置中推荐相对于竞争基线的优势，并分析了其在营销应用中的可行性。