Recommendation and Search in Social Networks

Ulusoy, Özügr; Tansel, Abdullah Uz; Arkun, Erol

doi:10.1007/978-3-319-14379-8

Cited by 6 publications

(1 citation statement)

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“…Generally, SNR falls into collaborative filtering, content-based filtering and hybrid recommendation, and most of the existing social network recommendation methods are based on friendship and other social information fused collaborative filtering [31][32][33][34]. In recent years, with the widespread use of social network applications, a variety of famous recommendation models such as social graph based recommendation method, matrix factorization based recommendation model and factor decomposition machine model are proposed successively [35].…”

Section: Social Network Recommendationmentioning

confidence: 99%

Study on Tag, Trust and Probability Matrix Factorization Based Social Network Recommendation

Liu¹,

Zhong²

2018

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In recent years, social network related applications such as WeChat, Facebook, Twitter and so on, have attracted hundreds of millions of people to share their experience, plan or organize, and attend social events with friends. In these operations, plenty of valuable information is accumulated, which makes an innovative approach to explore users' preference and overcome challenges in traditional recommender systems. Based on the study of the existing social network recommendation methods, we find there is an abundant information that can be incorporated into probability matrix factorization (PMF) model to handle challenges such as data sparsity in many recommender systems. Therefore, the research put forward a unified social network recommendation framework that combine tags, trust between users, ratings with PMF. The uniformed method is based on three existing recommendation models (SoRecUser, SoRecItem and SoRec), and the complexity analysis indicates that our approach has good effectiveness and can be applied to large-scale datasets. Furthermore, experimental results on publicly available Last.fm dataset show that our method outperforms the existing state-of-art social network recommendation approaches, measured by MAE and MRSE in different data sparse conditions.

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