%0 Conference Proceedings
%T A Survey on Techniques and Methods of Recommender System
%+ Gujarat Technological University (GTU)
%A Raval, Aanal
%A Borisagar, Komal
%< avec comité de lecture
%@ 978-3-031-16363-0
%( IFIP Advances in Information and Communication Technology
%B 5th International Conference on Computational Intelligence in Data Science (ICCIDS)
%C Virtual, India
%Y Lekshmi Kalinathan
%Y Priyadharsini R.
%Y Madheswari Kanmani
%Y Manisha S.
%I Springer International Publishing
%3 Computational Intelligence in Data Science
%V AICT-654
%P 97-114
%8 2022-03-24
%D 2022
%R 10.1007/978-3-031-16364-7_8
%K Machine learning
%K Recommender systems
%K Deep learning
%K Reinforcement learning
%K Soft computing techniques
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%Z TC 12
%2 https://inria.hal.science/hal-04381289/document
%2 https://inria.hal.science/hal-04381289/file/526570_1_En_8_Chapter.pdf
%L hal-04381289
%U https://inria.hal.science/hal-04381289
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC12
%~ IFIP-ICCIDS
%~ IFIP-AICT-654