Izvorni znanstveni članak

https://doi.org/10.31341/jios.45.2.10

Hybrid Recommendation Using Temporal Data for Accuracy Improvement in Item Recommendation

Desabandhu Parasuraman ; School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
Sathiyamoorthy Elumalai ; School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India

Sažetak

Recommender systems have become a vital entity to the business world in form of software tools to make decisions. It estimates the overloaded information and provides the suitable decisions in any kind of business work through online. Especially in the area of e-commerce, recommender systems provide suggestions to users on the items that are likely based upon user’s true interest. Collaborative Filtering and Content Based Filtering are the main techniques of recommender systems. Collaborative Filtering is considered to be the best in all domains and always outperforms Content Based filtering. But, both the techniques have some limitations like data sparsity, cold start, gray sheep and scalability issues. To overcome these limitations, Hybrid Recommender Systems are used by combining Collaborative Filtering and Content Based Filtering. This paper proposes such kind of hybrid system by combining Collaborative Filtering and Content Based Filtering using time variance and machine learning algorithm.

Ključne riječi

Recommendation Systems (RS); Collaborative Filtering (CF); Content Based Filtering (CBF); Hybrid Recommender System (HRS, Machine Learning (ML)

Hrčak ID:

270714

URI

https://hrcak.srce.hr/270714

Datum izdavanja:

15.12.2021.

Posjeta: 392 *