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A Literature Review of Empirical Studies of Recommendation Systems

Nikhat Akhtar, Devendera Agarwal. Published in Information Sciences

International Journal of Applied Information Systems
Year of Publication: 2015
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Nikhat Akhtar, Devendera Agarwal
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  1. Nikhat Akhtar and Devendera Agarwal. Article: A Literature Review of Empirical Studies of Recommendation Systems. International Journal of Applied Information Systems 10(2):6-14, December 2015. BibTeX

    	author = "Nikhat Akhtar and Devendera Agarwal",
    	title = "Article: A Literature Review of Empirical Studies of Recommendation Systems",
    	journal = "International Journal of Applied Information Systems",
    	year = 2015,
    	volume = 10,
    	number = 2,
    	pages = "6-14",
    	month = "December",
    	note = "Published by Foundation of Computer Science (FCS), NY, USA"


In the last twelve years, the number of web user increases, so intensely leading to intense advancement in web services which leads to enlargement the usage data at higher rates. The purpose of a recommender System is to generate meaningful recommendations to a collection of users for items or products that might interest them. Recommender systems differ in the way they analyze these data sources to develop notions of congeniality between users and items which can be used to identify well-matched pairs. The recommender system technology intentions to help users in finding items that match their personal interests. It has a successful usage in e-commerce applications to deal with problems related to information overload proficiently. In this paper, we will extensively present a survey of six existing recommendation system. The Collaborative Filtering systems analyze historical interactions alone, while Content-Based Filtering systems are based on profile attributes, Hybrid Techniques attempt to combine both of these designs, Demographic Based Recommender systems aim to categorize the user based on personal attributes and make recommendations based on demographic classes, while Knowledge-Based Recommendation attempts to suggest objects based on inferences about a user’s needs and preferences, and Utility-Based Recommender systems make recommendations based on the computation of the utility of each item for the user. In this paper, we have recognized 60 research papers on recommender systems, which were published between 1971 and 2014. Finally, few research papers had an influence on research paper recommender systems in practice. We also recognized a lack of authority and long term research interest in the field, 78% of the authors published no more than one paper on research paper recommender systems, and there was miniature cooperation among different co-author groups.


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Recommendations System, Utility Based, Collaborative Filtering, Contents Based Methods, Demographic Based, Knowledge Based, Hybrid Methods, Knowledge Sources.