Comparative analysis of recommender systems and its enhancements

  • Authors

    • Immidi Kali Pradeep
    • M Jaya Bhaskar
    https://doi.org/10.14419/ijet.v7i3.29.19181
  • Recommender System, Information Retrieval, Similarity Measure, Contextual Parameters

  • Abstract

    Recommenders are being used in many applications and circumstances to make ease of social life by generating categorized and personalized recommendations to the individuals. These categories may be chosen by the users to get recommendations for movies, songs, products and various services etc. One of the challenges of a recommender system is to generate recommendations in real time to many people by analyzing huge amount of data. In this paper, authors considered traditional recommender and hybrid recommender techniques to generate recommendations. Traditional recommender systems include similarity measure, matrix factorization, co-clustering and slope-one approach, where as the second type of recommender system consists of the role of hybridization techniques and contextual parameters with traditional recommenders. Here, authors worked on movie lens dataset with above mentioned recommender systems and observed that SVD approach has less RMSE and MAE values comparing with other models.

     

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  • How to Cite

    Kali Pradeep, I., & Jaya Bhaskar, M. (2018). Comparative analysis of recommender systems and its enhancements. International Journal of Engineering & Technology, 7(3.29), 304-310. https://doi.org/10.14419/ijet.v7i3.29.19181

    Received date: 2018-09-07

    Accepted date: 2018-09-07