Cross-domain recommender systems, thus, aim to generate or enhance recommendations in a target domain by exploiting knowledge from source domains. In this chapter, we formalize the cross-domain recommendation problem, unify the perspectives from which it has been addressed, analytically categorize, ...
sometimes a recommender system may need to recommend items to users that cross over two or more domains. One reason to exploit multiple domains using the cross-domain recommender systems is to solve the problems of data sparsity or cold start (new user). That is, there may be insufficient da...
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...
In this paper, we propose a method namely User Profile as a Bridge in Cross-domain Recommender Systems (UP-CDRSs) for transferring knowledge between domains through user profile. Firstly, we build a user profile using demographical information of a user, explicit ratings and content information ...
Collaborative Filtering (CF) has emerged as one of the most prominent implementation strategies for building recommender systems. The key idea is to exploit the usage patterns of individuals to generate personalized recommendations. CF techniques, especially for newly launched platforms, often face a cri...
Cross-domain recommender systems can alleviate this question and have been a hot research area. Adversarial domain adaptation has been a hot topic in cross-domain recommender systems due to its ability to mitigate the differences in feature distribution among different domains. Recent research has ...
Cross-domain recommender systems (CDRS) have emerged as a favorable solution to solve issues related to cold start, data sparsity, and diversity by leveraging knowledge from the source domains. This systematic literature review delves into the latest deep learning approaches utilized for CDRS, ...
这个aSDAE在论文A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems中被提出。全称additional Stacked Denoising Autoencoder。它可以复原出两个输入,并在复原过程中融合两者信息。 利用这个基础部件,论文提出了整体框架:也就是在源域和目标域分别训练各自的模型,最后将用户的隐向量进行...
However, two challenging issues exist in cross-domain recommender systems: 1) domain shift which makes the knowledge from source domain inconsistent with that in the target domain; 2) knowledge extracted from only one source domain is insufficient, while knowledge is potentially available in many ...
CCCFNet: a content-boosted collaborative filtering neural network for cross domain recommender systemsJianxun Lian , Fuzheng Zhang , Xing Xie , Guangzhong Sun International Conference on World Wide Web (WWW'17 Companion) | April 2017 Published by International ...