People Recommendation on Social Media

Guy, Ido

doi:10.1007/978-3-319-90092-6_15

Cited by 22 publications

(10 citation statements)

References 114 publications

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“…Recommendation mechanisms attempt to discover user preferences over items by modeling the association between users and items. The mechanism attempts to clarify user preferences by modeling the association between users and items [14], [42], and then prevent information overload by presenting appropriate content to targeted users on the basis of collected or inferred information [29], [45]. Hence, they recommend items that match the preferences of users to assist active users in selecting products from potential options [7].…”

Section: Recommendation Mechanismmentioning

confidence: 99%

Social Recommendations for Facebook Brand Pages

Chiu

2021

J. theor. appl. electron. commer. res.

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The objective of this research is to bridge the gap by proposing a content-based framework that is specifically designed to operate in social media environment. This study proposed a recommendation framework that integrated the features of brand pages and information on user behavior on brand pages. Data were obtained from 2,076 official brand pages in Taiwan, and a total of 500,000 interaction data were obtained and processed. Decision trees were used to classify brand pages and detect features that facilitate distinguishing among brand pages. Our method involved a simple training procedure with no restriction to a particular classifier learning algorithm and yields improved results on data sets extracted from a considerable number of Facebook brand pages. The results represented the diversity of social media user criteria in evaluating whether an activity is interesting to users. Moreover, these prevalent features indicate that brand pages distinguish themselves from others through their willingness to engage and interact with users. These findings not only enabled using brand page recommendation models for facilitating the selection of the most suitable brand page, but also useful for researchers seeking to develop a recommendation system for social media.

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Section: Recommendation Mechanismmentioning

confidence: 99%

Social Recommendations for Facebook Brand Pages

Chiu

2021

J. theor. appl. electron. commer. res.

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“…Based on Definition 2.1, we formalize two classes of user-to-user recommender. An overview that emphasizes unidirectional user recommendation in social media was conducted in [62].…”

Section: Pizzato Et Al Defined Inmentioning

confidence: 99%

“…One aspect was that over time, users exhausted the list of recommended individuals and connected to most of their potential ties. Since the network is symmetric, most connections are performed with familiar people [62,70]. The recommendations suggested by RRSs therefore became less effective and were frequently ignored.…”

Section: Zhang Et Al [169]mentioning

confidence: 99%

“…Social network sites (SNSs, also commonly referred to as OSNs: online social networks) have emerged at the second half of the 2000s as a means for Web users to connect and share information with one another. Generally speaking, there are two types of SNSs [62]: the first is a symmetric (confirmed) SNS, which is based on bi-directional relationships that have to be confirmed by both sides. Typically, one user sends an invitation and the other has to accept the invitation for the connection to form.…”

Section: Reciprocal Recommendation In Social Networkmentioning

confidence: 99%

“…Social network-driven strategies. Given the distinctive characteristics exhibited by RRS, another problem deserving further attention relates to techniques which have demonstrated success in social RS [62,143]. These systems have succeeded in generating unidirectional recommendations upon data extracted via social network analysis, such as information inherent to the network structure or data describing influence, popularity or trust between users.…”

Section: B2 Professional Collaboration and Knowledge-transfermentioning

confidence: 99%

See 2 more Smart Citations

Reciprocal Recommender Systems: Analysis of State-of-Art Literature, Challenges and Opportunities towards Social Recommendation

Palomares¹,

Porcel²,

Pizzato³

et al. 2020

Preprint

Self Cite

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Many social services including online dating, social media, recruitment and online learning, largely rely on "matching people with the right people". The success of these services and the user experience with them often depends on their ability to match users. Reciprocal Recommender Systems (RRS) arose to facilitate this process by identifying users who are a potential match for each other, based on information provided by them. These systems are inherently more complex than user-item recommendation approaches and unidirectional user recommendation services, since they need to take into account both users' preferences towards each other in the recommendation process. This entails not only predicting accurate preference estimates as classical recommenders do, but also defining adequate fusion processes for aggregating user-to-user preferential information. The latter is a crucial and distinctive, yet barely investigated aspect in RRS research. This paper presents a snapshot analysis of the extant literature to summarize the state-of-the-art RRS research to date, focusing on the fundamental features that differentiate RRSs from other classes of recommender systems. Following this, we discuss the challenges and opportunities for future research on RRSs, with special focus on (i) fusion strategies to account for reciprocity and (ii) emerging application domains related to social recommendation.

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People-to-People Reciprocal Recommenders

Koprinska¹,

Yacef²

2012

Recommender Systems Handbook

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People Recommendation on Social Media

Cited by 22 publications

References 114 publications

Social Recommendations for Facebook Brand Pages

Social Recommendations for Facebook Brand Pages

Reciprocal Recommender Systems: Analysis of State-of-Art Literature, Challenges and Opportunities towards Social Recommendation

People-to-People Reciprocal Recommenders

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