On the Efficiency of Social Recommender Networks

Wong, Felix Ming Fai; Liu, Zhenming; Chiang, Mung

doi:10.1109/tnet.2015.2475616

Cited by 23 publications

(5 citation statements)

References 25 publications

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“…The Kirchhoff index arises in many applications such as noisy consensus problems [PB14] and social recommender systems [WLC16]. It is a global index, and any changes of network structure, e.g.…”

Section: Discussionmentioning

confidence: 99%

“…In many real scenarios, the Kirchhoff index [KR93] of a network, defined as the sum of effective resistances over all vertex pairs, can be used as a unifying indicator to measure the interesting quantities associated with different problems in networks. For example, Kirchhoff index can be used to measure the mean cost of search in a complex network [FQY14], robustness of first-order consensus algorithm in noisy networks [PB14], the global utility of social recommender systems [WLC16], among others. Notwithstanding the relevance of the Kirchhoff index in various applications, there is a disconnect between this notion and efficiency of algorithms for estimating it.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Kirchhoff Index as a Measure of Edge Centrality in Weighted Networks: Nearly Linear Time Algorithms

Li¹,

Zhang²

2018

Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms

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Estimating the relative importance of vertices and edges is a fundamental issue in the analysis of complex networks, and has found vast applications in various aspects, such as social networks, power grids, and biological networks. Most previous work focuses on metrics of vertex importance and methods for identifying powerful vertices, while related work for edges is much lesser, especially for weighted networks, due to the computational challenge. In this paper, we propose to use the well-known Kirchhoff index as the measure of edge centrality in weighted networks, called θ-Kirchhoff edge centrality. The Kirchhoff index of a network is defined as the sum of effective resistances over all vertex pairs. The centrality of an edge e is reflected in the increase of Kirchhoff index of the network when the edge e is partially deactivated, characterized by a parameter θ. We define two equivalent measures for θ-Kirchhoff edge centrality. Both are global metrics and have a better discriminating power than commonly used measures, based on local or partial structural information of networks, e.g. edge betweenness and spanning edge centrality. Despite the strong advantages of Kirchhoff index as a centrality measure and its wide applications, computing the exact value of Kirchhoff edge centrality for each edge in a graph is computationally demanding. To solve this problem, for each of the θ-Kirchhoff edge centrality metrics, we present an efficient algorithm to compute its -approximation for all the m edges in nearly linear time in m. The proposed θ-Kirchhoff edge centrality is the first global metric of edge importance that can be provably approximated in nearly-linear time. Moreover, according to the θ-Kirchhoff edge centrality, we present a θ-Kirchhoff vertex centrality measure, as well as a fast algorithm that can compute -approximate Kirchhoff vertex centrality for all the n vertices in nearly linear time in m.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Kirchhoff Index as a Measure of Edge Centrality in Weighted Networks: Nearly Linear Time Algorithms

Li¹,

Zhang²

2018

Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms

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“…These stories can be shared with friends with access control. [8] that by increasing num_iter, we get a family of charts with increasing distance from the original social chart.…”

Section: Social Networkmentioning

confidence: 99%

Social Recommendation Model with User Trust and Item Ratings Using Collaborative Filtering Technique in Hotel Application

Ramya²,

Revathy³

2020

IJAMECS2

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A trust-based recommendation model is regularized with user trust and item ratings called TrustSVD. Trust networks are large-world networks where many users are socially linked, suggesting the assumption of trust in recommendation systems. An item rating downloaded from the OSN Server can be viewed by the user. If the information is accessible on the server, all the adjacent devices are enabled and a peer to peer mode of communication is initiated. User reviews from a graphical forum are shown. It focuses on the rating prediction role in the current framework and has shown that integrating user social confidence data will boost the output of recommendations. The strategy builds on the SVD++ state-of-the-art model. The data sparsity and cold start issues are resolved in the friend of friend recommendation model used. The mining method generates the user's overall rating in graphical representations and illustrates the overall rating. This model increases the utility of data by exchanging neighborhoods to protect security and privacy issues. One of the most common techniques for implementing a recommendation scheme is Collaborative filtering (CF).

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“…In the survey work of Bernardes et al [7], authors conclude that the field of social Recommenders Systems (RS) built on implicit social networks seems particularly promising, propose a social filtering formalism, and with their experiments on music and movie preference datasets, they find that one has to test and try a full repertoire of candidate RS, fine-tune parameters and select the best RS for the performance indicator he/she cares for. Authors in [32] study the efficiency of social recommender networks merging the social graph with the co-rating graph and consider several variations by altering the graph topology and edge weights. With experiments on the Yelp dataset, they conclude that social network can improve the recommendations produced by collaborative filtering algorithms when a user makes more than one connection.…”

Section: Related Workmentioning

confidence: 99%

Social recommendations for personalized fitness assistance

Dharia

Eirinaki

Jain

et al. 2017

Pers Ubiquit Comput

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Wearable technology allows users to monitor their activity and pursue a healthy lifestyle through the use of embedded sensors. Such wearables usually connect to a mobile application that allows them to set their profile, and keep track of their goals. However, due to the relatively high maintenance of such applications, where a significant amount of user feedback is expected, users who are very busy, or not as self-motivated, stop using them after a while. It has been shown that accountability improves commitment to an exercise routine. In this work we present the PROFit framework, a personalized fitness assistant aiming at engaging users in fitness activities, incorporating a social element. The PROFit architecture collects information from activity tracking devices and automatically classifies their activity type. Moreover, the framework incorporates a social recommender system. Using collaborative filtering on user profile and activity data PROFit generates personalized fitness schedules based on their availability and wellbeing goals. We also incorporate the social network community of the application's users and identify different tie strengths based on the user's connections and location. The output of the recommendation process is twofold, as both new activities, as well as fitness buddies, are being recommended to each user.

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On the Efficiency of Social Recommender Networks

Cited by 23 publications

References 25 publications

Kirchhoff Index as a Measure of Edge Centrality in Weighted Networks: Nearly Linear Time Algorithms

Kirchhoff Index as a Measure of Edge Centrality in Weighted Networks: Nearly Linear Time Algorithms

Social Recommendation Model with User Trust and Item Ratings Using Collaborative Filtering Technique in Hotel Application

Social recommendations for personalized fitness assistance

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