UGC Approved Journal no 63975(19)

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Published in:

Volume 7 Issue 10
October-2020
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2010449


Registration ID:
302886

Page Number

3454-3465

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Title

A Comparative Study of Algorithms for Recommender Systems

Abstract

Recommender Systems have become a very useful tool for a large variety of domains. Researchers have been attempting to improve their algorithms in order to issue better predictions to the users. However, one of the current challenges in the area refers to how to properly evaluate the predictions generated by a recommender system. In the extent of offline evaluations, some traditional concepts of evaluation have been explored, such as accuracy, Root Mean Square Error and Mean Absolute Error for top-N recommendations. In recent years, more research have proposed some new concepts such as novelty, diversity and coverage. These concepts have been addressed with the goal to satisfy the users’ requirements. Numerous definitions and metrics have been proposed in previous work. On the absence of a specific summarization on evaluations of recommendation combining traditional metrics and recent progresses, this paper surveys and organizes the main research that present definitions about concepts and propose metrics or strategies to evaluate recommendations. In addition, this survey also settles the relationship between the concepts, categorizes them according to their objectives and suggests potential future topics on user satisfaction. We compare five experimental methodologies, broadly covering the variants reported in the literature. In our experiments with three state-of-the-art recommenders, four of the evaluation methodologies are consistent with each other and differ from error metrics, in terms of the comparative recommenders’ performance measurements. The other procedure aligns with RMSE, but shows a heavy bias towards known relevant items, considerably overestimating performance.

Key Words

RMSE, MAE, Matrix factorization, Recommender System, Cosine Similarity Metric, ARHR

Cite This Article

"A Comparative Study of Algorithms for Recommender Systems ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 10, page no.3454-3465, October-2020, Available :http://www.jetir.org/papers/JETIR2010449.pdf

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2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"A Comparative Study of Algorithms for Recommender Systems ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 10, page no. pp3454-3465, October-2020, Available at : http://www.jetir.org/papers/JETIR2010449.pdf

Publication Details

Published Paper ID: JETIR2010449
Registration ID: 302886
Published In: Volume 7 | Issue 10 | Year October-2020
DOI (Digital Object Identifier):
Page No: 3454-3465
Country: visakhapatnam, AP, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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