التفاصيل البيبلوغرافية
| العنوان: |
Recommendations based on Integrated Matrix Time Decomposition and Clustering Optimization. |
| المؤلفون: |
Kumar Raja, D. R., Hemanth Kumar, G., Basha, Syed Muzamil, Ahmed, Syed Thouheed |
| المصدر: |
International Journal of Performability Engineering; Apr2022, Vol. 18 Issue 4, p298-306, 9p |
| مصطلحات موضوعية: |
MATRIX decomposition, RECOMMENDER systems, INFORMATION overload, PARTICLE swarm optimization, K-means clustering |
| مستخلص: |
The prompt progression of web data and the number of web visitors creates a latent information overload problem and complicates data mining to select the right items on the web. E-commerce websites and applications manage information overload using several information filtering techniques such as personalized recommendation systems. The recommendation system creates a list of products to helps users. The proposed NOMINATE methodology offers characteristic values for definite elements from the LOD source as input for matrix factorization. This helps NOMINATE to retrieve user-specific functions. Subsequently studying the functions, the proposed approach produces an enhanced cluster of consumers by the weight of every user above the desired values of the element characteristics. For this, the NOMINATE methodology uses the particle swarm optimization (PSO) algorithm and k-means clustering algorithm. Using clustered results, the NOMINATE approach estimatiesthe centrality of proximity among other users in the cluster and identifies an experienced user for each cluster. In addition, the proposed recommendation scheme produces a set of characteristic values for each user based on the adept information of the users in the respective clusters. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
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