Hybrid Cohort Rating Prediction Technique to leverage Recommender System

被引：0

作者：

Dhanalakshmi, R. ^{[1
]}

Sinha, B. B. ^{[2
]}

机构：

[1] Natl Inst Technol Puducherry, Dept Comp Sci & Engn, Karaikal 609609, India

[2] Natl Inst Technol Nagaland, Dept Comp Sci & Engn, Dimapur 797103, India

来源：

JOURNAL OF SCIENTIFIC & INDUSTRIAL RESEARCH | 2019年 / 78卷 / 07期

关键词：

Recommender System; Pearson Correlation; Adjusted Cosine similarity; Collaborative filtering; MAE; RMSE;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The long tail of diverse consumption of resources online by the customers raises a challenge for the e-commerce websites and service providers. Recommender system offers a vigorous way to cope up with the aforementioned challenge. In this paper, we have proposed a hybrid cohort rating prediction technique which relies on high cohort users and high cohort items to make predictions. Our model significantly improves the retention of recommender system showing encouraging results when compared with existing traditional recommender systems.

引用

页码：411 / 414

页数：4

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