Open AccessHybrid Recommendation Using Temporal Data for Accuracy Improvement in Item Recommendation
Author(s) -
Desabandh Parasuraman,
Sathiyamoorthy Elumalai
Publication year - 2021
Publication title -
journal of information and organizational sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.146
H-Index - 13
eISSN - 1846-9418
pISSN - 1846-3312
DOI - 10.31341/jios.45.2.10
Subject(s) - collaborative filtering , recommender system , computer science , scalability , data mining , variance (accounting) , information retrieval , cold start (automotive) , information filtering system , machine learning , database , accounting , business , engineering , aerospace engineering
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.