Cross domain recommendation based on multi-type media fusion

Tan, Shulong; Bu, Jiajun; Qin, Xuzhen; Chen, Chun; Cai, Deng

doi:10.1016/j.neucom.2013.08.034

Cited by 68 publications

(19 citation statements)

References 25 publications

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“…One natural way is to transfer user interests in other domains to the target domain. Tan et al [22] propose a Bayesian hierarchical approach based on Latent Dirichlet Allocation (LDA) to transfer user interests cross domains or media. Another way is to add users' review text to recommendations [23].…”

Section: Collaborative Filteringmentioning

confidence: 99%

Collaborative filtering with weighted opinion aspects

Yang

Liu

et al. 2016

Neurocomputing

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Section: Collaborative Filteringmentioning

confidence: 99%

Collaborative filtering with weighted opinion aspects

Yang

Liu

et al. 2016

Neurocomputing

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“…Results showed that it is promising to utilize multiple information sources for recommendations. The work described in [8] found a correlation between objects by using a Bayesian hierarchical approach based on the Latent Dirichlet Allocation (LDA) method by modeling users' interests and objects' topics. Output correlations were used to give recommendations to target users based on their interests.…”

Section: Related Workmentioning

confidence: 99%

“…The work described in [6] stated that identity resolution solutions can be used by various applications, such as security, privacy and recommendation systems. Some research efforts in recommendation systems concentrate on recommendations across domains, e.g., [8][9][10]. However, these cross-domain recommendation systems focus solely on matching items and have not considered users' preferences across available platforms.…”

Section: Introductionmentioning

confidence: 99%

Making recommendations by integrating information from multiple social networks

2016

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It is becoming a common practice to use recommendation systems to serve users of web-based platforms such as social networking platforms, review web-sites, and e-commerce web-sites. Each platform produces recommendations by capturing, maintaining and analyzing data related to its users and their behavior. However, people generally use different web-based platforms for different purposes. Thus, each platform captures its own data which may reflect certain aspects related to its users. Integrating data from multiple platforms may widen the perspective of the analysis and may help in modeling users more effectively. Motivated by this, we developed a recommendation framework which integrates data collected from multiple platforms. For this purpose, we collected and anonymized datasets which contain information from several social networking and social media platforms, namely BlogCatalog, Twitter, Flickr, Facebook, YouTube and LastFm. The collected and integrated data forms a consolidated repository that may become a valuable source for researchers and practitioners. We implemented a number of recommendation methodologies to observe their performance for various cases which involve using single versus multiple features from a single source versus multiple sources. The conducted

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“…One is to discover domain relations by Collaborative Filtering (CF) [6,14,7,9,27,4,5], and the other is to leverage various inter-domain content information [20,18,10,1,3,16,22].…”

Section: Related Workmentioning

confidence: 99%

“…An important problem CF faces is data sparsity [4,16,9]. To alleviate the problem in a more direct way, researchers have been seeking the possibility of leveraging inter-domain relations and contents for cross-domain recommendations.…”

Section: Related Workmentioning

confidence: 99%

Browser-oriented universal cross-site recommendation and explanation based on user browsing logs

Zhang

2014

Proceedings of the 8th ACM Conference on Recommender Systems

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Our research aims to bridge the gap between different websites to provide cross-site recommendations based on browsers. Recent advances have made recommender systems essential to various online applications, such as e-commerce, social networks, and review service websites. However, practical systems mainly focus on recommending inner-site homogeneous items. For example, a movie review website usually recommends other movies within the site when a user has enjoyed a movie online. However, it would be exciting if the system recommends some attractive products related to this movie from some e-commerce websites like Amazon or eBay.Such an ability to provide heterogeneous cross-site recommendations may shed light on brand new and promising business models, which could benefit both the online shops in expanding the marketing efforts, and the online users in discovering items of interest from a wider scope. In this research, we propose and formalize the problem of universal recommendation, record and analyze user browsing actions in web browsers, and provide browser-oriented cross-site recommendations when the users are surfing online.

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Cross domain recommendation based on multi-type media fusion

Cited by 68 publications

References 25 publications

Collaborative filtering with weighted opinion aspects

Collaborative filtering with weighted opinion aspects

Making recommendations by integrating information from multiple social networks

Browser-oriented universal cross-site recommendation and explanation based on user browsing logs

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