Barter 发表于 2025-3-28 16:28:05
Advances in Collaborative Filtering. The CF methods discussed in this chapter have been proposed a decade ago but still show state-of-the art accuracy in recent studies. The modeling patterns identified in this chapter are applicable to a variety of recommender problems such as item recommendation, rating prediction, cold start recommendation and context-aware recommenders.迁移 发表于 2025-3-28 19:47:33
http://reply.papertrans.cn/83/8242/824122/824122_42.pngRepatriate 发表于 2025-3-29 02:42:14
http://reply.papertrans.cn/83/8242/824122/824122_43.pngCultivate 发表于 2025-3-29 04:34:45
http://reply.papertrans.cn/83/8242/824122/824122_44.pngALT 发表于 2025-3-29 08:37:51
http://reply.papertrans.cn/83/8242/824122/824122_45.png烦人 发表于 2025-3-29 13:35:42
http://reply.papertrans.cn/83/8242/824122/824122_46.pngDefiance 发表于 2025-3-29 19:27:28
Advances in Collaborative FilteringF algorithms have shown great prediction quality both in academic research and in industrial applications. This chapter surveys core methods in the field. Matrix factorization techniques, which became a first choice for implementing CF, are described together with other innovations. We also describeEndometrium 发表于 2025-3-29 22:32:20
http://reply.papertrans.cn/83/8242/824122/824122_48.pngBILE 发表于 2025-3-30 03:11:22
http://reply.papertrans.cn/83/8242/824122/824122_49.png爱管闲事 发表于 2025-3-30 05:12:38
Context-Aware Recommender Systems: From Foundations to Recent Developmentson, information retrieval, ubiquitous and mobile computing, data mining, marketing, and management. Prior work has extensively demonstrated that relevant contextual information does matter in recommender systems and that it is important to take this information into account when providing recommenda