Learning to recommend with social trust ensemble

Ma, Hao; King, Irwin; Lyu, Michael R.

doi:10.1145/1571941.1571978

Cited by 729 publications

(456 citation statements)

References 19 publications

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“…The last method we compare against is the one described in [16]. The focus of that work is on explicit feedback (ratings) and the social trust matrix A is precomputed.…”

Section: Methods In Comparisonmentioning

confidence: 99%

“…In [16] a trust ensemble model is introduced, the user is modeled as an ensemble of his own and his friends preferences. While the functional form of this model has similarities with the approach introduced in the current work there are two crucial differences: 1) their method only deals with explicit feedback data (ratings) while we focus on implicit feedback data which is the norm in industry applications, 2) they precompute the weight of the influence or trust of friends on the users based on the ratings, while in SECoFi the interaction weights are computed in the model.…”

Section: Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

Socially Enabled Preference Learning from Implicit Feedback Data

Delporte

Karatzoglou

Matuszczyk³

et al. 2013

Machine Learning and Knowledge Discovery in Databases

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Abstract. In the age of information overload, collaborative filtering and recommender systems have become essential tools for content discovery. The advent of online social networks has added another approach to recommendation whereby the social network itself is used as a source for recommendations i.e. users are recommended items that are preferred by their friends. In this paper we develop a new model-based recommendation method that merges collaborative and social approaches and utilizes implicit feedback and the social graph data. Employing factor models, we represent each user profile as a mixture of his own and his friends' profiles. This assumes and exploits "homophily" in the social network, a phenomenon that has been studied in the social sciences. We test our model on the Epinions data and on the Tuenti Places Recommendation data, a large-scale industry dataset, where it outperforms several state-of-the-art methods.

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“…The last method we compare against is the one described in [16]. The focus of that work is on explicit feedback (ratings) and the social trust matrix A is precomputed.…”

Section: Methods In Comparisonmentioning

confidence: 99%

Section: Optimizationmentioning

confidence: 99%

Socially Enabled Preference Learning from Implicit Feedback Data

Delporte

Karatzoglou

Matuszczyk³

et al. 2013

Machine Learning and Knowledge Discovery in Databases

View full text Add to dashboard Cite

show abstract

“…When compared in diverse many people will gather information that may be sensitive aspect. Insiders will be with complete overview about the internal working system and tasks that has to be performed [15].…”

Section: O Unsupervised Ensemble Based Modelmentioning

confidence: 99%

Efficient Mining and Recommendation of Extensive Data Through Collaborative Filtering in E-Commerce: A Survey

Kumar¹

2018

IJET

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E-Commerce is the most widely used technique nowadays. Buying and selling goods on the Internet has been most admired and frequently utilized. The humongous growth of the content available on the internet has made laborious for users to search and utilize information for classifying the products. Recommendation system regarded as the best way to help the customers in buying the related products. (GRS) group recommender system aims at enhancing the customer's benefits for buying the products. This paper summarizes the fuzzy tree matching, modeling user preference dynamics, web page recommendation, uncertainty analysis for keywords, recommender system application, temporal topic model for friend recommendation, autocratic decision-making system based on (GRS),modeling user recommender, evaluating recommender system and enhancing (GRS).

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“…At present, the personalized recommendation technology based on algorithm to classify generally includes the following two kinds: one is recommendation based on the content [1,2]; the other is based on collaborative filtering [3,4,5].…”

Section: Related Workmentioning

confidence: 99%

The Study of Enterprise Personalized Knowledge Service Based on the Knowledge Scenario

Zhuang¹,

Li²,

Song³

2016

Proceedings of the 2016 6th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2016)

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Abstract. In recent years, more and more enterprises establish the knowledge management system to share knowledge within a company, track projects and improve work efficiency. However, the increasingly complex content affects the efficiency of gain and share the knowledge. The article investigates the personalized recommendation technology and introduces the recommendation technology based on context-aware into the knowledge management system. Through the knowledge scenario such as the context of domain, goal and so on, increase the accuracy of the knowledge recommendation. Accordingly, the knowledge sharing in enterprises become more and more convenient, and the knowledge transfer become more efficient.

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Learning to recommend with social trust ensemble

Cited by 729 publications

References 19 publications

Socially Enabled Preference Learning from Implicit Feedback Data

Socially Enabled Preference Learning from Implicit Feedback Data

Efficient Mining and Recommendation of Extensive Data Through Collaborative Filtering in E-Commerce: A Survey

The Study of Enterprise Personalized Knowledge Service Based on the Knowledge Scenario

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