A Social Trust Based Friend Recommender for Online Communities

Nepal, Surya; Paris, Cécile; Pour, Payam Aghaei; Bista, Sanat Kumar; Freyne, Jill

doi:10.4108/icst.collaboratecom.2013.254213

Cited by 13 publications

(13 citation statements)

References 19 publications

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“…Besides, there is recent work reporting significant recommendation performance improvement for social recommender systems [21,34,35,36,37,38,39]. On the other hand, there are also unsuccessful attempts at applying social recommendation [40,41].…”

Section: Literature Reviewmentioning

confidence: 99%

Development of a group recommender application in a Social Network

Quijano-Sánchez

Díaz-Agudo

Recio-García

2014

Knowledge-Based Systems

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In today's society, recommendations are becoming increasingly important. With the advent of the Social Web and the growing popularity of Social Networks, where users explicitly provide personal information and interact with others and the system, it is becoming clear that the key for the success of recommendations is to develop new strategies which focus on social recommendations leveraged by these new sources of knowledge. In our work, we focus on group recommender systems. These systems traditionally suffer from a number of shortcomings that hamper their effectiveness. In this paper we continue our research, that focuses on improving the overall quality of group recommendations through the addition of social knowledge to existing recommendation strategies. To do so, we use the information stored in Social Networks to elicit social factors following two approaches: the cognitive modeling approach, that studies how people's way of thinking predisposes their actions; and the social approach, that studies how people's relationships predispose their actions. We show the value of using models of social cognition extracted from Social Networks in group recommender systems through the instantiation of our model into a real-life Facebook movie recommender application.

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Section: Literature Reviewmentioning

confidence: 99%

Development of a group recommender application in a Social Network

Quijano-Sánchez

Díaz-Agudo

Recio-García

2014

Knowledge-Based Systems

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“…These two aspects are not considered in this article for the propagation. We refer readers to Nepal, Sherchan, and Paris (2011) and Nepal et al (2013b) for the comprehensive STrust model. For detailed analysis of the model and effect of various parameters on PopTrust, we refer readers to Nepal et al (2012Nepal et al ( , 2013a.…”

Section: Strust: a Social Trust Modelmentioning

confidence: 99%

“…Our current recommenders are built using our original trust model, which embodies explicit and implicit interactions among users (as it is based on users' behavior in the network). We have already conducted a series of evaluation experiments with these recommenders, comparing them to recommenders that exploit only explicit friendships among users (i.e., recommenders that exploit the social graph, considering direct friendships and "friends of a friend"-the FOAF concept) (Nepal et al 2013b;Nepal et al 2013c). These experiments showed that capturing implicit links (via the social trust concept) leads to performance improvements.…”

Section: Experiments With the Trust Propagation Modelmentioning

confidence: 99%

Behavior‐Based Propagation of Trust in Social Networks with Restricted and Anonymous Participation

Nepal

Bista

Paris

2014

Computational Intelligence

Self Cite

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Increasing interactions and engagements in social networks through monetary and material incentives is not always feasible. Some social networks, specifically those that are built on the basis of fairness, cannot incentivize members using tangible things and thus require an intangible way to do so. In such networks, a personalized recommender could provide an incentive for members to interact with other members in the community. Behavior‐based trust models that generally compute social trust values using the interactions of a member with other members in the community have proven to be good for this. These models, however, largely ignore the interactions of those members with whom a member has interacted, referred to as “friendship effects.” Results from social studies and behavioral science show that friends have a significant influence on the behavior of the members in the community. Following the famous Spanish proverb on friendship “Tell Me Your Friends and I Will Tell You Who You Are,” we extend our behavior‐based trust model by incorporating the “friendship effect” with the aim of improving the accuracy of the recommender system. In this article, we describe a trust propagation model based on associations that combines the behavior of both individual members and their friends. The propagation of trust in our model depends on three key factors: the density of interactions, the degree of separation, and the decay of friendship effect. We evaluate our model using a real data set and make observations on what happens in a social network with and without trust propagation to understand the expected impact of trust propagation on the ranking of the members in the recommended list. We present the model and the results of its evaluation. This work is in the context of moderated networks for which participation is by invitation only and in which members are anonymous and do not know each other outside the community. Copyright © 2014 John Wiley & Sons, Ltd.

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“…There is little evidence of other recommender systems which attempt to do this but work by Nepal et al [16] also focuses on recommendations to change user behaviour. They built a social rec-ommender for an online community to deliver government services to citizens.…”

Section: Related Workmentioning

confidence: 99%

NudgeAlong: A Case Based Approach to Changing User Behaviour

O’Shea¹,

Delany²,

Lane³

et al. 2014

Case-Based Reasoning Research and Development

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Abstract. Companies want to change the way that users interact with their services. One of the main ways to do this is through messaging. It is well known that different users are likely to respond to different types of messages. Targeting the right message type at the right user is key to achieving successful behaviour change. This paper frames this as a case based reasoning problem. The case representation captures a summary of a user's interactions with a company's services over time. The case solution represents a message type that resulted in a desired change in the user's behaviour. This paper describes this framework, how it has been tested using simulation and a short description of a test deployment.

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A Social Trust Based Friend Recommender for Online Communities

Cited by 13 publications

References 19 publications

Development of a group recommender application in a Social Network

Development of a group recommender application in a Social Network

Behavior‐Based Propagation of Trust in Social Networks with Restricted and Anonymous Participation

NudgeAlong: A Case Based Approach to Changing User Behaviour

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