Interaction Based Content Recommendation in Online Communities

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

doi:10.1007/978-3-642-38844-6_2

Cited by 7 publications

(8 citation statements)

References 16 publications

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“…1) Interaction frequency Frequency of interaction between two users indicate the strength of the relationship between user and friends [31]. This is because user with similar interests or preferences will actively interact and share information [32]. The more user interacts with their friends, the more likely they have the same preferences as a discussion topic [33].…”

Section: Friendship Strength Based On User's Interactionsmentioning

confidence: 99%

Collaborative Item Recommendations Based on Friendship Strength in Social Network

Jamil¹,

Noah²,

Mohd³

2020

IJMLC

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Recommender Systems (RSs) are among the solutions in addressing the information overload problems. One of the RS main problem is cold start users where RSs do not have enough information to identify the user's preferences and thus unable to recommend relevant items. One of the approaches to overcome such problem is through social relationship which can be extracted from social networks. Existing studies used friendship relation to find nearest neighbors combined with implicit data such as tweet content, posts, like or tags to identify user's preference. This study proposed friendship strength through user's interaction and item rating values to identify users' preferences. User-based collaborative filtering methods are used in developing the system. Two datasets are synthetically obtained from MovieLens database for user's rating information. Meanwhile Twitter users' interactions data, was retrieved from Higgs Boson topic available from the Standford University. There are four phases of development namely user profile phase, friendship strength phase, user's preference phase and items recommendation phase. The findings of the study show that there is an improvement in the proposed method compared to the baseline based on the recall (R), precision (P) and F score measures.

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Section: Friendship Strength Based On User's Interactionsmentioning

confidence: 99%

Collaborative Item Recommendations Based on Friendship Strength in Social Network

Jamil¹,

Noah²,

Mohd³

2020

IJMLC

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“…Matthews et al [31] and Muller et al [35] examined online communities in the intranet and how community owners can enhance the value of their communities. Online communities can be implicit, identified by the system (e.g., [27,36]), or explicit through membership (e.g., [31]). Our work examines explicitly defined online communities in a large enterprise.…”

Section: Related Workmentioning

confidence: 99%

Recommending social media content to community owners

Ronen

Guy²,

Kravi

et al. 2014

Proceedings of the 37th International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

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Online communities within the enterprise offer their leaders an easy and accessible way to attract, engage, and influence others. Our research studies the recommendation of social media content to leaders (owners) of online communities within the enterprise. We developed a system that suggests to owners new content from outside the community, which might interest the community members. As online communities are taking a central role in the pervasion of social media to the enterprise, sharing such recommendations can help owners create a more lively and engaging community. We compared seven different methods for generating recommendations, including content-based, memberbased, and hybridization of the two. For member-based recommendations, we experimented with three groups: owners, active members, and regular members. Our evaluation is based on a survey in which 851 community owners rated a total of 8,218 recommended content items. We analyzed the quality of the different recommendation methods and examined the effect of different community characteristics, such as type and size.

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“…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%

“…To encourage interactions, we turn to recommendations, in particular people and content recommendations, based on members’ social trust behavior , or their behavior (both active and passive) towards each other in the network (Nepal et al ; Nepal et al ). Unlike electronic commerce applications, where incentives are provided in tangible ways (e.g., monetary and material forms), the incentives in this model is to build a standing in the community through trusting and trusted interactions in the community.…”

Section: Introductionmentioning

confidence: 99%

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

Nepal

Bista

Paris

2014

Computational Intelligence

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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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Interaction Based Content Recommendation in Online Communities

Cited by 7 publications

References 16 publications

Collaborative Item Recommendations Based on Friendship Strength in Social Network

Collaborative Item Recommendations Based on Friendship Strength in Social Network

Recommending social media content to community owners

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

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