Incorporating social networks and user opinions for collaborative recommendation

Liu, Bin; Yuan, Zheng

doi:10.1145/1869652.1869661

Cited by 20 publications

(16 citation statements)

References 11 publications

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“…People tend to spend time with and live nearer people of the same race, gender, educational background, occupation, class, and other demographic and socio-economic variables. Thus, network ties might capture useful similarity information [7], [8], [12], especially when combined with indicators of tie strength such as interaction frequency [13], ratings of trust [14], or implied computed trust [15], [16].…”

Section: A Useful Theories and Forcesmentioning

confidence: 99%

Network-Centric Recommendation: Personalization with and in Social Networks

Sharma

Cosley

2011

2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing

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Abstract-People often rely on the collective intelligence of their social network for making choices, which in turn influences their preferences and decisions. However, traditional recommender systems largely ignore social context, and even networkaware recommenders don't explicitly support social goals and concerns such as shared consumption and identity management. We present relevant theories and research questions for a more network-centric approach to recommendations and introduce PopCore, a platform for studying them in Facebook. An initial 50-user study with PopCore gives insights into tradeoffs around the popularity, likeability, and rateability of recommendations made by a set of network-centric algorithms and to people's thoughts about the idea of network-centric recommendation.

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Section: A Useful Theories and Forcesmentioning

confidence: 99%

Network-Centric Recommendation: Personalization with and in Social Networks

Sharma

Cosley

2011

2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing

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“…RWR-based recommendation systems have been shown to work effectively in many domains ( [51], [52], [53], [54], [55], [56], [57], [58], [59]), for which Bayesian transition matrices can also be introduced. …”

Section: Resultsmentioning

confidence: 99%

Random Walk-based Recommendation with Restart using Social Information and Bayesian Transition Matrices

Pongnumkul¹,

Motohashi²

2015

IJCA

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A recommendation system is an information retrieval system that employs user, product, and other related information to infer relationships among data to offer product recommendations. The basic assumption is that friends or users with similar behavior will have similar interests. The large number of products available today makes it impossible for any user to explore all of them and increases the importance of recommendation systems. However, a recommendation system normally requires comprehensive data relating users and products. Insufficiently comprehensive data creates difficulties for creating good recommendations. Recommendation systems for incomplete data have become an active research area. One approach to solve this problem is to use random walk with restart (RWR), which significantly reduces the quantity of data required and has been shown to outperform collaborative filtering, the currently popular approach. This study explores how to increase the efficiency of the RWR approach. We replace transition matrices that use information regarding relationships between user, usage, and tags with transition matrices that use Bayesian probabilities, and we compare the efficiency of the two approaches using mean average precision. An experiment was conducted using music information data from last.fm. The result shows that our approach provides better recommendations.

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“…Another Recommendation model [4] uses the "evaluated trust" value to trust any stranger. The trust evaluation involves the degree of satisfaction between two parties along with the balance and considers the number of interactions between them.…”

Section: Related Survey Of Existing Techniquesmentioning

confidence: 99%