Graph-Based Friend Recommendation in Social Networks Using Artificial Bee Colony

Akbari, Fatemeh; Tajfar, Amir Hooshang; Nejad, Akbar Farhoodi

doi:10.1109/dasc.2013.108

Cited by 15 publications

(11 citation statements)

References 11 publications

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“…ABC algorithm is used to learn the graph weights by optimizing four parameters. The method is compared to classic machine learning algorithms such as K-nearest Neighbor, Support Vector Machine and Multilayer perceptron, and it yielded an accuracy of 77% [11] .…”

Section: Related Workmentioning

confidence: 99%

“…A degree of a vertex represents the number of edges connected to the vertex. Finally, the density of the graph can be calculated using equation (11) because the initial graph is undirected. The density is computed by dividing the number of existent edges (379776) by the total number of vertices (n * (n-1)), which is 1538840, with a result that equals to 0.247.…”

Section: Figure 7-pcc Between Cosine Similarity and Euclideanmentioning

confidence: 99%

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Dual-Stage Social Friend Recommendation System Based on User Interests

Qader

Abbas

2020

eijs

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The use of online social network (OSN) has become essential to humans' lives whether for entertainment, business or shopping. This increasing use of OSN motivates designing and implementing special systems that use OSN users' data to provide better user experience using machine learning and data mining algorithms and techniques. One system that is used extensively for this purpose is friend recommendation system (FRS) in which it recommends users to other users in professional or entertaining online social networks. For this purpose, this study proposes a novel friend recommendation system, namely Hybrid Friend Recommendation (FR) model. The Hybrid model applies dual-stage methodology on unlabeled data of 1241 users collected from OSN users via our online survey platform featuring user interests and activities based upon which users with similar social behavioral patterns are recommended to each other. The model employs a variety of techniques including user-based collaborative filtering (UBCF) approach and graph-based approach friend-of-friend recommendation (FOF). The model offers unique solutions to common problems of FRS such as data sparsity by using dimensionality technique called non-negative matrix factorization (NMF) to create a dense representation of the collected data and reduce its sparsity as well as providing seamless integration with other FRSs. The evaluation of the hybrid FR model shows positive correlation of Pearson correlation coefficient (PCC) compared to the baseline used.

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Section: Related Workmentioning

confidence: 99%

Section: Figure 7-pcc Between Cosine Similarity and Euclideanmentioning

confidence: 99%

Dual-Stage Social Friend Recommendation System Based on User Interests

Qader

Abbas

2020

eijs

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“…The ABC selects the appropriate auxiliary materials in terms of their difficulty, number of 'likes', and course topics. That in [12] developed a friend recommendation system using an ABC exploiting structural properties and topological features of social networks. It first generates a subgraph of the network of the user and then suggests new links considering the parameters mentioned by optimizing the relative importance of the weights for these parameters using ABC.…”

Section: Pcc Ejc Vcsmentioning

confidence: 99%

“…The work in [11] optimized clusters of users with ABC to avoid local optima. Similarly, topological features of social networks were exploited using the ABC algorithm for friend recommendations in [12]. More approaches are detailed in Section 2.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary approaches for weight optimization in collaborative filtering-basedrecommender systems

Sert¹,

Ar²,

Bostancı³

2019

Turk J Elec Eng & Comp Sci

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Collaborative filtering is one of the widely adopted approaches in recommender systems used for e-commerce applications, stating that users having similar tastes will have similar preferences in the future. The literature presents a number of similarity metrics such as the extended Jaccard coefficient to quantify these preference similarities. This paper aims to improve prediction accuracy by optimizing the similarity values computed using these metrics by adopting two biologically inspired approaches, namely artificial bee colony and genetic algorithms, with a bottom-up approach, suggesting that any improvement on a single-user basis will reflect on the overall prediction accuracy. Detailed statistical analysis was performed using the t-test, analysis of variance, and McNemar's test to see whether there were performance differences. The results show that statistically significant differences exist with high confidence levels.

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“…However it cant be used friend recommendation. Traditional personalized friend recommendation always uses static attribute information or topological structure information [1,2]. Those kinds of information are static and they are not sufficient conditions for friend recommendation.…”

Section: Introductionmentioning

confidence: 99%

The Private Recommendation Based on the Analysis of User Dynamic Behavior

Hong-yan¹,

Li²

2015

Proceedings of the 2015 International Conference on Education Technology, Management and Humanities Science

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The traditional private recommendation system always ignores user dynamic behaviors. In consideration of the problem, the private recommendation based on the analysis of user dynamic behavior is provoked. This method recommends interested users in their virtual communities which are identified in the dynamic behavior network. The dynamic behavior network is built and made up of micro blog hot topics, users and participation behavior relationships. Meanwhile, this method not only considers the short-term dynamic interest, but also takes long-term stability interest into account. In order to get the weighted similarity of interest, establish the long-term interest model and short-term interest model, and trade off their contribution rate. Finally, experiment is done on a micro blog data set, The results show that this method has good effect.

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Graph-Based Friend Recommendation in Social Networks Using Artificial Bee Colony

Cited by 15 publications

References 11 publications

Dual-Stage Social Friend Recommendation System Based on User Interests

Dual-Stage Social Friend Recommendation System Based on User Interests

Evolutionary approaches for weight optimization in collaborative filtering-basedrecommender systems

The Private Recommendation Based on the Analysis of User Dynamic Behavior

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