Use of social network information to enhance collaborative filtering performance

Liu, Fengkun; Lee, Hong Joo

doi:10.1016/j.eswa.2009.12.061

Cited by 251 publications

(143 citation statements)

References 21 publications

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“…Social link information have been captured from real time social networking sites and used in devising Hybrid approaches utilizing fundamental CF methodologies [16], [28], [29], [30]. As the online content is progressively being created, edited and shared over social network communities social tagging provides a powerful way for users to organize, administer, consolidate and search for innumerable kinds of resources.…”

Section: Discussionmentioning

confidence: 99%

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Systematic Evaluation of Social Recommendation Systems: Challenges and Future

Rastogi¹,

Singh²

2016

ijacsa

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Abstract-The issue of information overload could be effectively managed with the help of intelligent system which is capable of proactively supervising the users in accessing relevant or useful information in a tailored way, by pruning the large space of possible options. But the key challenge lies in what all information can be collected and assimilated to make effective recommendations. This paper discusses reasons for evolution of recommender systems leading to transition from traditional to social information based recommendations. Social Recommender System (SRS) exploits social contextual information in the form of social links of users, social tags, user-generated data that contain huge supplemental information about items or services that are expected to be of interest of user or about features of items. Therefore, having tremendous potential for improving recommendation quality. Systematic literature review has been done for SRS by categorizing various kinds of social-contextual information into explicit and implicit user-item information. This paper also analyses key aspects of any generic recommender system namely Domain, Personalization Levels, Privacy and Trustworthiness, Recommender algorithms to give a better understanding to researchers new in this field.

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Section: Discussionmentioning

confidence: 99%

“…Next, they developed approaches for selecting neighbors using Pearson's correlations and augmented it with friends' data. As a result, the model generated recommendations about items using proposed CF with suggested neighbor sets [29].…”

Section: B Social Linksmentioning

confidence: 99%

Systematic Evaluation of Social Recommendation Systems: Challenges and Future

Rastogi¹,

Singh²

2016

ijacsa

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“…There have been some recent forays into social recommendations [9], [10], [23]- [25], all based on the assumption that any pair of friends in a social network will have similar interests. The studies [24], [25] incorporate this network-based similarity property between users into a state-of-the-art matrix factorization recommendation approach.…”

Section: Social Recommender Systemsmentioning

confidence: 99%

Recommender System Using Implicit Social Information

Song

Haihong

2015

IEICE Trans. Inf. & Syst.

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SUMMARYSocial recommendation systems that make use of the user's social information have recently attracted considerable attention. These recommendation approaches partly solve cold-start and data sparsity problems and significantly improve the performance of recommendation systems. The essence of social recommendation methods is to utilize the user's explicit social connections to improve recommendation results. However, this information is not always available in real-world recommender systems. In this paper, a solution to this problem of explicit social information unavailability is proposed. The existing user-item rating matrix is used to compute implicit social information, and then an ISRec (implicit social recommendation algorithm) which integrates this implicit social information and the user-item rating matrix for social recommendation is introduced. Experimental results show that our method performs much better than state-of-the-art approaches; moreover, complexity analysis indicates that our approach can be applied to very large datasets because it scales linearly with respect to the number of observations in the matrices. key words: recommender systems, implicit social relationships, item relations, probabilistic matrix factorization

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“…The increasingly popular online social networks provide additional information to enhance pure rating-based RSes. Several social-trust based RSes have recently been proposed to improve recommendation accuracy, to just name a few, [9], [10], [11], [12], [14], [16], [17], and [18]. The common rationale behind all of them is that a user's taste is similar to and/or influenced by her trusted friends in social networks.…”

Section: Introductionmentioning

confidence: 99%

Circle-based recommendation in online social networks

Yang

Steck²,

Liu

2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

303

147

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Online social network information promises to increase recommendation accuracy beyond the capabilities of purely rating/feedback-driven recommender systems (RS). As to better serve users' activities across different domains, many online social networks now support a new feature of "Friends Circles", which refines the domain-oblivious "Friends" concept. RS should also benefit from domain-specific "Trust Circles". Intuitively, a user may trust different subsets of friends regarding different domains. Unfortunately, in most existing multi-category rating datasets, a user's social connections from all categories are mixed together. This paper presents an effort to develop circle-based RS. We focus on inferring category-specific social trust circles from available rating data combined with social network data. We outline several variants of weighting friends within circles based on their inferred expertise levels. Through experiments on publicly available data, we demonstrate that the proposed circle-based recommendation models can better utilize user's social trust information, resulting in increased recommendation accuracy.

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Use of social network information to enhance collaborative filtering performance

Cited by 251 publications

References 21 publications

Systematic Evaluation of Social Recommendation Systems: Challenges and Future

Systematic Evaluation of Social Recommendation Systems: Challenges and Future

Recommender System Using Implicit Social Information

Circle-based recommendation in online social networks

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