Evolution of recommender paradigm optimization over time Bam Bahadur Sinha a,⇑ , R. Dhanalakshmi b a National Institute of Technology Nagaland, Chumukedima– 797103, Dimapur, India b National Institute of Technology Puducherry, Thiruvettakudy, Karaikal 609 609, India article info Article history: Received 9 April 2019 Revised 17 June 2019 Accepted 19 June 2019 Available online xxxx Keywords: Content filtering Collaborative filtering Hybrid filtering Optimization Similarity measures abstract In the past few decades recommender system has reshaped the way of information filtering between websites and the users. It helps in identifying user interest and generates product suggestions for the active users. This paper presents an enlightening analysis of various recommender system such as content-based, collaborative-based and hybrid recommendation techniques along with few optimization models that has been applied to improvise the parameters being considered by the aforementioned tech- niques. We explored 125 articles published from 1992 to 2019 in order to discuss the problems associ- ated with the existing models. Various advantages and disadvantages of each recommendation model including the input methods has been elaborated. Critical review on research problems based on the explored techniques and future directions has also been covered. Ó 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Contents 1. Introduction .......................................................................................................... 00 2. Three -Tier origination of recommender system ............................................................................. 00 3. Classification of recommender system ..................................................................................... 00 3.1. Content-based system............................................................................................. 00 3.2. Collaborative-based system ........................................................................................ 00 3.2.1. Memory based collaborative filtering ......................................................................... 00 3.2.2. Model based collaborative filtering ........................................................................... 00 3.3. Hybrid recommender system ....................................................................................... 00 3.4. Other personalized services ........................................................................................ 00 4. Parameters of recommender system....................................................................................... 00 4.1. Similarity measures used by recommender system ..................................................................... 00 4.1.1. Distance based similarity ................................................................................... 00 4.1.2. Correlation based similarity ................................................................................. 00 4.2. Evaluation metrics of recommender system ........................................................................... 00 4.2.1. Mean absolute error (MAE) ................................................................................. 00 4.2.2. Root mean square error (RMSE) ............................................................................. 00 4.2.3. Precision ................................................................................................ 00 4.2.4. Recall ................................................................................................... 00 4.2.5. F 1 score & accuracy........................................................................................ 00 4.3. Optimization of recommendation parameters.......................................................................... 00 https://doi.org/10.1016/j.jksuci.2019.06.008 1319-1578/Ó 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). ⇑ Corresponding author. E-mail address: bambahadur@nitnagaland.ac.in (B.B. Sinha). Peer review under responsibility of King Saud University. Production and hosting by Elsevier Journal of King Saud University – Computer and Information Sciences xxx (xxxx) xxx Contents lists available at ScienceDirect Journal of King Saud University – Computer and Information Sciences journal homepage: www.sciencedirect.com Please cite this article as: B. B. Sinha and R. Dhanalakshmi, Evolution of recommender paradigm optimization over time, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.06.008