Cross-domainIn the context of personalization e-commerce cyberspace based on massive data, the traditional single-domain recommendation algorithm is difficult to adapt to cross-domain information recommendation service. Collaborative filtering is a simple and common recommendation algorithm, but when the ...
Multi-Domain Recommendation 一些MDR 的方法可以直接用来解决 SDR 问题 [3]提出一个 multi-domain collaborative filtering (MCF) 框架来解决多个域中数据稀疏的问题,后续又有工作采用其他技术来迁移来自相同 users 的知识,例如 feature combination, transfer learning, 和 active learning Dual-Target CDR 现有方法采用...
综述文章题为Cross-Domain Recommendation: Challenges, Progress, and Prospects(IJCAI 2021最具影响力论文Top 15)。除了本文作者Feng Zhu,这篇综述的作者名单中还有蚂蚁集团推荐领域的相关专家 Chaochao Chen, Jun Zhou, Longfei Li,以及澳洲麦考瑞大学推荐领域的国际知名学者Yan Wang 和 Guanfeng Liu。 【注】: ...
Cross-domain recommender systems: A survey of the State of the Art Cross-domain recommendation is an emerging research topic. In the last few years an increasing amount of work has been published in various areas related to the Recommender System field, namely User Modeling, Information Retrieval...
A recommender system is usually used for a single domain, which is when items and users’ ratings are in the same domain. However, sometimes a recommender system may need to recommend items to users...
A method for generating recommendation for a target domain based on data from other source domains is provided, comprising the steps of: aggregating data in each of said other domains; splitting said aggregated data to a train data set, a validation data set and a test data set; extracting ...
Cross-domain recommendation offers a potential avenue for alleviating data sparsity and cold-start problems. Embedding and mapping, as a classic cross-doma... C Zhao,H Zhao,M He,... 被引量: 0发表: 2024年 Exploring chemical compound space with a graph-based recommender system Through the corr...
To address the problem of sparse data and cold-start when facing new users and items in the single-domain recommendation, cross-domain recommendation has gradually become a hot topic in the recommendation system. This method enhances target domain recommendation performance by incorporating relevant info...
To alleviate the problem of sparse target domain data and cold start in cross domain recommendation, Cross-domain recommendation model of deep feature extraction and attention mechanism (CRDFEAM) model is proposed by combining the techniques including th
To overcome data sparsity problem, we propose a cross domain recommendation system named CCCFNet which can combine collaborative filtering and content-based filtering in a unified framework. We first introduce a factorization framework to tie CF and content-based filte...