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A survey on data mining techniques in recommender systems

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Abstract

Recommender systems have been regarded as gaining a more significant role with the emergence of the first research article on collaborative filtering (CF) in the mid-1990s. CF predicts the interests of an active user based on the opinions of users with similar interests. To extract information on the preference of users for a set of items and evaluate the performance of the recommender system’s techniques and algorithms, a critical analysis can be conducted. This study therefore employs a critical analysis on 131 articles in CF area from 36 journals published between the years 2010 and 2016. This analysis seems to be the exclusive survey which supports and motivates the community of researchers and practitioners. It is done by using the applications of users’ activities and intelligence computing and data mining techniques on CF recommendation systems. In addition, it provides a classification of the literature on academic database according to the benchmark recommendation databases, two users’ feedbacks (explicit and implicit feedbacks) which reflect their activities and categories of intelligence computing and data mining techniques. Eventually, this study provides a road map to guide future direction on recommender systems research and facilitates the accumulated and derived knowledge on the application of intelligence computing and data mining techniques in CF recommendation systems.

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Acknowledgements

The authors would like to thank the Research Management Centre of—Universiti Teknologi MARA (UiTM) and the Malaysian Ministry of Education for their support and cooperation including researches and other individuals who are either directly or indirectly involved in this study.

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  1. Advanced Analytics Engineering Centre (AAEC), Universiti Teknologi MARA (UiTM), Shah Alam, Selangor, Malaysia

    Maryam Khanian Najafabadi & Azlinah Hj. Mohamed

  2. Advanced Informatics School (AIS), Universiti Teknologi Malaysia (UTM), Kuala Lumpur, Malaysia

    Mohd Naz’ri Mahrin

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  1. Maryam Khanian Najafabadi
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Correspondence to Maryam Khanian Najafabadi.

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Maryam Khanian Najafabadi, Azlinah Hj. Mohamed and Mohd Naz’ri Mahrin declare that they have no conflict of interest.

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Communicated by V. Loia.

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Najafabadi, M.K., Mohamed, A.H. & Mahrin, M.N. A survey on data mining techniques in recommender systems. Soft Comput 23, 627–654 (2019). https://doi.org/10.1007/s00500-017-2918-7

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