Fuzzy family tree similarity based effective e-learning recommender system

Perumal, Sankar Pariserum; Kannan, A.; Ganapathy, Sannasi

doi:10.1109/icoac.2017.7951760

Cited by 9 publications

(1 citation statement)

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“…Recommendations made through most reliable social networks play a major role in the e-commerce systems. Moreover, the web-based recommendation systems [2] are helpful to the users for purchasing preferred items in the e-commerce applications, manufacturing the relevant products by manufacturers for sales based on the demand in the market, provision of suitable learning materials to the learners in e-learning systems and the recommendation of preferred medical service centres and hospitals in the healthcare applications. In such a scenario, the application of soft computing techniques [3] will help in building better and effective recommendation systems.…”

Section: Introductionmentioning

confidence: 99%

FIRMACA-Fuzzy intelligent recommendation model using ant clustering algorithm for social networking

Perumal¹,

Ganapathy

Kannan

2020

SN Appl. Sci.

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Error rate is a challenging parameter to be met for most of the recommendation systems where there is a high probability of item movement among the data clusters. In social networking applications, it is often necessary to produce apt user recommendations where ant-based clustering techniques provide best optimal solutions to the clustering problems. However, the existing ant clustering algorithms lack in efficient local search procedures. Also, they are in need of intelligent fuzzy rule improvements for effective cluster partitioning. Therefore, a new model named Fuzzy Intelligent Recommendation Model using Ant Clustering Algorithm (FIRMACA) is proposed in this paper where a combination of biased exploration and fuzzy rules is used for ant clustering. Experimental analysis shows improvement in recommendation metrics-precision, recall, normalized discounted cumulative gain (NDCG) (≤ 5%) and significant reduction (≤ 1%) in misclassification error rate (MER) compared to contemporary ACO-based algorithms. The selected clusters are optimized globally and locally to pull out the best possible redefined clusters.

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Section: Introductionmentioning

confidence: 99%