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A Survey on Techniques and Methods of Recommender System

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Computational Intelligence in Data Science (ICCIDS 2022)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 654))

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Abstract

As prevalence is growing for social media, the value of its content is becoming paramounting. This data can reveal about a person’s personal and professional life. The behaviors done on social media either frequent or periodic can comprehend fondness and attentiveness of users on certain matters. But accompanying it, diverse data from multiple sources with high volumes sets its foot in. Here becomes operational the usage of recommender systems. These are capable of providing customized assistance to users based on respective quondam behavior and preferences. Machine Learning and deep learning methods have proven to be a boon in these tasks of predictions with notable accuracy. This paper discusses existing techniques with its fors and againsts, concerns and issues, extrapolating the results and solutions, which in turn can help in better interpretation of current developments to pave a path for pioneering researches.

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Authors and Affiliations

  1. Computer Engineering Department, GSET, GTU, Ahmedabad, India

    Aanal Raval

  2. E.C. Engineering Department, GSET, GTU, Ahmedabad, India

    Komal Borisagar

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  1. Aanal Raval
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Correspondence to Aanal Raval .

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Editors and Affiliations

  1. Sri Sivasubramaniya Nadar College of Engineering, Chennai, India

    Lekshmi Kalinathan

  2. Sri Sivasubramaniya Nadar College of Engineering, Chennai, India

    Priyadharsini R.

  3. Sri Sivasubramaniya Nadar College of Engineering, Chennai, India

    Madheswari Kanmani

  4. Sri Sivasubramaniya Nadar College of Engineering, Chennai, India

    Manisha S.

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Raval, A., Borisagar, K. (2022). A Survey on Techniques and Methods of Recommender System. In: Kalinathan, L., R., P., Kanmani, M., S., M. (eds) Computational Intelligence in Data Science. ICCIDS 2022. IFIP Advances in Information and Communication Technology, vol 654. Springer, Cham. https://doi.org/10.1007/978-3-031-16364-7_8

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  • DOI: https://doi.org/10.1007/978-3-031-16364-7_8

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