Improving Matching Process in Social Network
Lin Chen,Richi Nayak,Yue Xu +2 more
- pp 305-311
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TLDR
This work proposes a methodology by using clustering, SimRank to recommend matching candidates to users in an online dating network and the performance is improved by double.Abstract:
Online dating networks, a type of social network, are gaining popularity. With many people joining and being available in the network, users are overwhelmed with choices when choosing their ideal partners. This problem can be overcome by utilizing recommendation methods. However, traditional recommendation methods are ineffective and inefficient for online dating networks where the dataset is sparse and/or large and two-way matching is required. We propose a methodology by using clustering, SimRank to recommend matching candidates to users in an online dating network. Data from a live online dating network is used in evaluation. The success rate of recommendation obtained using the proposed method is compared with baseline success rate of the network and the performance is improved by double.read more
Citations
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Journal ArticleDOI
Predicting mobile wallet resistance: A two-staged structural equation modeling-artificial neural network approach
TL;DR: This study addressed the inhibitors of m-wallet innovation adoption through the lens of innovation resistance theory (IRT) by applying a sophisticated two-staged structural equation modeling-artificial neural network (SEM-ANN) approach and successfully extended the IRT by integrating socio-demographics and perceived novelty.
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Exploiting matrix factorization to asymmetric user similarities in recommendation systems
TL;DR: A novel user similarity measure aimed at providing a valid similarity measurement between users with very few ratings is proposed and applied to the user similarity matrix in order to discover the similarities between users who have rated different items.
Posted Content
Online Dating Recommendations: Matching Markets and Learning Preferences
TL;DR: In this article, a two-side matching framework was proposed for online dating recommendations and an LDA model was designed to learn the user preferences from the observed user messaging behavior and user profile features.
Journal ArticleDOI
Reciprocal Recommender Systems: Analysis of state-of-art literature, challenges and opportunities towards social recommendation
Iván Palomares,Carlos Porcel,Luiz Augusto Pizzato,Ido Guy,Enrique Herrera-Viedma,Enrique Herrera-Viedma +5 more
TL;DR: A snapshot-style analysis of the extant literature is presented that summarizes the state-of-the-art RRS research to date, focusing on the algorithms, fusion processes and fundamental characteristics of RRS, both inherited from conventional user-to-item recommendation models and those inherent to this emerging family of approaches.
References
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Proceedings ArticleDOI
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