Friend recommendation in social networks based on multi-source information fusion

Cheng, Shulin; Zhang, Bofeng; Zou, Guobing; Huang, Mingqing; Zhu, Zexuan

doi:10.1007/s13042-017-0778-1

Cited by 40 publications

(24 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This algorithm performs better than other recommendation algorithms that do not use such information. Heterogeneous information network recommendations with different architectures have the following applications: for meta-structures, they are used for citation recommendations [12]; for meta-paths, they are used for e-commerce recommendations [13] and Top-N recommendations [14]. The embedding algorithms based on meta-structures and metapaths have been applied in different fields; however, there remain some shortcomings, such as the accuracy of the recommendation results not being high and the ability of knowledge expression between nodes.…”

Section: Related Workmentioning

confidence: 99%

“…Since the rating prediction task is only concerned with the user and the item, it only needs to learn the embedding vectors of the user and the item. Therefore, after mapping the fusion functions, the vectors of users and items of different meta-paths can be integrated to get the final embedded vectors of users and items, which can be expressed by the following two equations, where e u (U) and e i (I) are the final vectors of user u and item i, respectively, as shown in Equations (13) and (14).…”

Section: Personalized Nonlinear Fusion Functionmentioning

confidence: 99%

“…i are the vector outputs of the user and item from Equations (13) and (14), respectively, γ i , respectively, and α and β are the hyperparameters regulating the three expressions. Their values lie between (0,1).…”

mentioning

confidence: 99%

See 2 more Smart Citations

Recommendation Model Based on a Heterogeneous Personalized Spacey Embedding Method

et al. 2021

View full text Add to dashboard Cite

The traditional heterogeneous embedding method based on a random walk strategy does not focus on the random walk fundamentally because of higher-order Markov chains. One of the important properties of Markov chains is stationary distributions (SDs). However, in large-scale network computation, SDs are not feasible and consume a lot of memory. So, we use a non-Markovian space strategy, i.e., a heterogeneous personalized spacey random walk strategy, to efficiently get SDs between nodes and skip some unimportant intermediate nodes, which allows for more accurate vector representation and memory savings. This heterogeneous personalized spacey random walk strategy was extended to heterogeneous space embedding methods in combination with vector learning, which is better than the traditional heterogeneous embedding methods for node classification tasks. As an excellent embedding method can obtain more accurate vector representations, it is important for the improvement of the recommendation model. In this article, recommendation algorithm research was carried out based on the heterogeneous personalized spacey embedding method. For the problem that the standard random walk strategy used to compute the stationary distribution consumes a large amount of memory, which may lead to inefficient node vector representation, we propose a meta-path-based heterogenous personalized spacey random walk for recommendation (MPHSRec). The meta-path-based heterogeneous personalized spacey random walk strategy is used to generate a meaningful sequence of nodes for network representation learning, and the learned embedded vectors of different meta-paths are transformed by a nonlinear fusion function and integrated into a matrix decomposition model for rating prediction. The experimental results demonstrate that MPHSRec not only improves the accuracy, but also reduces the memory cost compared with other excellent algorithms.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Personalized Nonlinear Fusion Functionmentioning

confidence: 99%

See 1 more Smart Citation

Recommendation Model Based on a Heterogeneous Personalized Spacey Embedding Method

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Cheng et al proposed a friend recommendation framework in social networks, where multiple sources have been integrated, including personal features, network structure features and social features. The framework is based on D-S evidence theory, which embodies the minimal conflicts among evidences [20].…”

Section: Related Workmentioning

confidence: 99%

A Social Collaborative Filtering Method to Alleviate Data Sparsity Based on Graph Convolutional Networks

Xie¹,

Fan²,

Xiao

2020

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

Nowadays recommender systems (RS) keep drawing attention from academia, and collaborative filtering (CF) is the most successful technique for building RS. To overcome the inherent limitation, which is referred to as data sparsity in CF, various solutions are proposed to incorporate additional social information into recommendation processes, such as trust networks. However, existing methods suffer from multi-source data integration (i.e., fusion of social information and ratings), which is the basis for similarity calculation of user preferences. To this end, we propose a social collaborative filtering method based on novel trust metrics. Firstly, we use Graph Convolutional Networks (GCNs) to learn the associations between social information and user ratings while considering the underlying social network structures. Secondly, we measure the direct-trust values between neighbors by representing multi-source data as user ratings on popular items, and then calculate the indirect-trust values based on trust propagations. Thirdly, we employ all trust values to create a social regularization in user-item rating matrix factorization in order to avoid overfittings. The experiments on real datasets show that our approach outperforms the other state-of-the-art methods on usage of multi-source data to alleviate data sparsity.

show abstract

“…Zhao et al 32 adopted the D‐S evidence theory method combined with the credibility of multisource data to improve the alarm recognition rate of the security situation assessment model. Cheng et al 33 proposed a recommendation framework based on the improved D‐S evidence theory that integrates multiple information sources and minimizes the conflicts among the evidence. Wang et al 34 combined the particle swarm optimization algorithm and evidence theory, where the particle swarm optimization algorithm was used to obtain the index weight of multiple evidence and the simplified alarm as evidence fusion.…”

Section: Introductionmentioning

confidence: 99%

Analyzing host security using D‐S evidence theory and multisource information fusion

Yao

Zhang

et al. 2020

Int J Intell Syst

View full text Add to dashboard Cite

Friend recommendation in social networks based on multi-source information fusion

Cited by 40 publications

References 58 publications

Recommendation Model Based on a Heterogeneous Personalized Spacey Embedding Method

Recommendation Model Based on a Heterogeneous Personalized Spacey Embedding Method

A Social Collaborative Filtering Method to Alleviate Data Sparsity Based on Graph Convolutional Networks

Analyzing host security using D‐S evidence theory and multisource information fusion

Contact Info

Product

Resources

About