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    Performance comparison of top N recommendation algorithms

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    Authors
    Mustafa, Ghulam
    Frommholz, Ingo
    Affiliation
    University of Bedfordshire
    Issue Date
    2015-10-26
    Subjects
    Memory Based CF
    Recommender System
    ROC Curve
    Collaborative Filtering
    Singular Value Decomposition (SVD)
    Principle Component Analysis (PCA)
    Model Based CF
    
    Metadata
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    Abstract
    In traditional recommender systems, services/items are recommended to the user based on the initial ratings while the results comes from the predicted rating values are not considered which further refers to top N recommendations. In top N recommendation algorithms, recommendation process is further enhanced by predicting the missing ratings where the basic objective is to find the items that might be interest of a user. Performance comparison and evaluation of different top N recommendation algorithms is quite challenging for large datasets where selection of an appropriate algorithm can help to improve the recommendation process by predicting missing ratings. Therefore, in this paper we analyse and evaluate the 6 different top N recommendation algorithms using accuracy metrics such as precision and recall on Movie-lense 100K dataset from the Group-lens. Our main finding is the selection of Top N recommendation algorithm that perform significantly better than other recommender algorithms in pursuing the top-N recommendation process.
    Citation
    Mustafa G, Frommholz I (2015) 'Performance comparison of top N recommendation algorithms', 2015 Fourth International Conference on Future Generation Communication Technology (FGCT) - Luton, Institute of Electrical and Electronics Engineers Inc..
    Publisher
    Institute of Electrical and Electronics Engineers Inc.
    URI
    http://hdl.handle.net/10547/624258
    DOI
    10.1109/FGCT.2015.7300256
    Additional Links
    https://ieeexplore.ieee.org/document/7300256
    Type
    Conference papers, meetings and proceedings
    Language
    en
    ISBN
    9781479982660
    ae974a485f413a2113503eed53cd6c53
    10.1109/FGCT.2015.7300256
    Scopus Count
    Collections
    Computing

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