Optimized fuzzy-based group recommendation with parallel computation

Natarajan, Sivaramakrishnan; Subramaniyaswamy, V.; Ravi, Logesh

doi:10.3233/jifs-169977

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…Computing the partition with maximum modularity has been proved as the NP-complete problem [ 13 ]. Even if we consider the solution with high computation performance, e.g., the cloud computing [ 35 , 36 ] and the parallel computing [ 37 ], to compute the partitions for maximizing the modularity, it still requires huge computation resource. Therefore, we propose a GA-based approach to get the near-optimal solution with minimum computation.…”

Section: Allocate Node Weight By Genetic Algorithmsmentioning

confidence: 99%

Detecting Overlapping Communities in Modularity Optimization by Reweighting Vertices

Tsung

Chen

et al. 2020

Entropy

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On the purpose of detecting communities, many algorithms have been proposed for the disjointed community sets. The major challenge of detecting communities from the real-world problems is to determine the overlapped communities. The overlapped vertices belong to some communities, so it is difficult to be detected using the modularity maximization approach. The major problem is that the overlapping structure barely be found by maximizing the fuzzy modularity function. In this paper, we firstly introduce a node weight allocation problem to formulate the overlapping property in the community detection. We propose an extension of modularity, which is a better measure for overlapping communities based on reweighting nodes, to design the proposed algorithm. We use the genetic algorithm for solving the node weight allocation problem and detecting the overlapping communities. To fit the properties of various instances, we introduce three refinement strategies to increase the solution quality. In the experiments, the proposed method is applied on both synthetic and real networks, and the results show that the proposed solution can detect the nontrivial valuable overlapping nodes which might be ignored by other algorithms.

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Section: Allocate Node Weight By Genetic Algorithmsmentioning

confidence: 99%

Detecting Overlapping Communities in Modularity Optimization by Reweighting Vertices

Tsung

Chen

et al. 2020

Entropy

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“…A Hesitant Fuzzy Set is basically an extension of the concept of fuzzy sets. A fuzzy set is one that maps a given value to a probabilistic value between 0 and 1 (limits inclusive) [3,8]. This is known as the relational degree of the input value.…”

Section: Hfs (Hesitant Fuzzy Set)mentioning

confidence: 99%

An Adaptive Hesitant Fuzzy Sets Based Group Recommendation System

Jayaraman,

Subramaniyaswamy,

Ravi

2020

MJCS

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Accurate group movie recommendation systems are a need in society today. We find that people tend to watch movies in groups rather than by themselves. However, the groups of people that tend to watch movies together are very diverse. In the existing methods, the characteristics of individual users are simply aggregated to obtain the group’s attributes and most of the time useful data is not utilized. This can be improved upon by ensuring the utilization of all the data that we are presented with from the scenario. The method proposed in this paper is termed integrated as we weighed in the individual traits of each user in the group when predicting the group’s rating for a movie. We used the concept of Hesitant Fuzzy Sets (HFS) in order to keep track of the characteristics of each of the users individually. The method we proposed in this paper employs Matrix Factorisation (MF) based Collaborative Filtering (CF) along with hesitant fuzzy sets. The way we performed MF based CF for a group is that we found the factors first and then formed the groups. The ratings were then predicted for these groups. The groups we have considered are of three sizes - 3 users, 5 users, and 10 users.

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“…C. Solnon [29] introduced the ACO in solving Constraint Satisfaction Problems. Some of the other optimization solving techniques are local search [30][31][32][33][34][35], large neighbourhood search [27,28,[36][37][38][39], etc. The theme of ACO can be used to solve as the search in a graph for finding minimum cost to travel the path has been found out in [40][41][42] [43] which was been devised by the same author to solve the care sequencing problem.…”

Section: Literature Surveymentioning

confidence: 99%

Experimental Analysis of Ant System on Travelling Salesman Problem Dataset TSPLIB

Thirugnanasambandam¹,

R.S²,

Saravanan³

et al. 2019

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Traveling Salesman Problem (TSP) is one of the vast research areas and has been considered as sub problems in many fields apart from computer science and also in the field of computer science. OBJECTIVES: This paper deals with the comparison of Ant System Ant System (AS) which is a variant of Ant Colony Optimization. METHODS: The performance of the Ant System is analysed by applying it on the Travelling Salesman Problem (TSP). The optimal results found on TSP using AS has been analysed with the elapsed time taken to find the optimal results, its mean, median, variance and the standard deviation. RESULTS: And also, the quality of solutions has been made by calculating the percentage of the optimality and the deviation of the solutions from the TSPLIB provides best known solutions. For instances, TSPLIB data sets have been used. CONCLUSION: Totally, 7 instances have been executed with three different set of parameters for AS and the results are analysed in terms of different parameter settings and performance metrics on each of it. The role of parameters has also been discussed along with the experimental results.

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Optimized fuzzy-based group recommendation with parallel computation

Cited by 6 publications

References 20 publications

Detecting Overlapping Communities in Modularity Optimization by Reweighting Vertices

Detecting Overlapping Communities in Modularity Optimization by Reweighting Vertices

An Adaptive Hesitant Fuzzy Sets Based Group Recommendation System

Experimental Analysis of Ant System on Travelling Salesman Problem Dataset TSPLIB

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