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IJSTR >> Volume 9 - Issue 10, October 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Products And Movie Recommendation System For Social Networking Sites

[Full Text]



Debajit Datta, T. M. Navamani, Rajvardhan Deshmukh



Collaborative Filtering, Content-Based Filtering, Classification, Clustering, Movie, Product, Recommendation.



Recommendation systems are an integral part of information filtering system in data science, that are widely used in order to identify the pattern a user would likely choose on the basis of the previous choices of the user as well as from studying the pattern in which others have chosen. For a fact, the recommendation can never be a cent percent correct at providing recommendations to the user but can be close enough to please them to a certain extent. Thus, the same is widely used in the industries these days to get higher profit and have a good hold in the market. The data scientists of every company design some algorithm that studies the information from the social network and clusters the data. There can be a single algorithm for classification like k-Means clustering or Hidden Markov model or can be done by bagging and boosting techniques. With this technique of displaying the movies or products into the profile of a particular customer, they not only increase their business but also enhances the customer experiences but there are several issues related to the standard techniques like the cold start problem, shrill attack, etc. thereby increasing the scope of research in this field. This work deals with both Collaborative Filtering and Content-Based Filtering to form a product and movie recommendation system for the social networking sites that shows the effectiveness of collaborative filtering and portrays the challenges faced by content-based filtering.



[1] Nandagawali, Priyanka A., and Jaikumar M. Patil. "Community based recommendation system based on products." 2014 International Conference on Power, Automation and Communication (INPAC).
[2] Kaur, Jatinder, Rajeev Kumar Bedi, and S. K. Gupta. "Product Recommendation Systems a Comprehensive Review." (2018).
[3] Eklaspur, Namrata M., and Anand S. Pashupatimath. "A friend recommender system for social networks by life style extraction using probabilistic method-friendtome." International Journal of Computer Science Trends and Technology (IJCST) 3.3 (2015).
[4] Haruna, Khalid, et al. "A collaborative approach for research paper recommender system." PloS one 12.10 (2017): e0184516.
[5] Kumar, Manoj, et al. "A movie recommender system: Movrec." International Journal of Computer Applications 124.3 (2015).
[6] Cui, Bei-Bei. "Design and implementation of movie recommendation system based on Knn collaborative filtering algorithm." ITM web of conferences. Vol. 12. EDP Sciences, 2017.
[7] Hande, Rupali, et al. "MOVIEMENDER-A movie recommender system." International journal of engineering sciences & research technology (IJESRT) 5.11 (2016): 686.
[8] Sharma, Pooja Mr Bhupender. "Movie Recommendation System: A Review Report." Journal for Research| Volume 4.01 (2018).
[9] Chen, Vito Xituo, and Tiffany Y. Tang. "Incorporating singular value decomposition in user-based collaborative filtering technique for a movie recommendation system: A comparative study." Proceedings of the 2019 the International Conference on Pattern Recognition and Artificial Intelligence. 2019.
[10] Lin, Chu-Hsing, and Hsuan Chi. "A novel movie recommendation system based on collaborative filtering and neural networks." International Conference on Advanced Information Networking and Applications. Springer, Cham, 2019.
[11] Zhang, Richong, and Yongyi Mao. "Movie Recommendation via Markovian Factorization of Matrix Processes." IEEE Access 7 (2019): 13189-13199.
[12] Tewari, Anand Shanker, and Ashu Mainwal. "Tag based product recommendation system using rating variance." Proceedings of 2nd International Conference on Advanced Computing and Software Engineering (ICACSE). 2019.
[13] Reddy, S. R. S., et al. "Content-based movie recommendation system using genre correlation." Smart Intelligent Computing and Applications. Springer, Singapore, 2019. 391-397.
[14] Lops, Pasquale, et al. "Trends in content-based recommendation." User Modeling and User-Adapted Interaction 29.2 (2019): 239-249.
[15] Dhawan, Sanjeev. "Comparision of Recommendation System Approaches." 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). IEEE, 2019.
[16] "Recommender Systems (Implementing In Octave) - UPSCFEVER." Upscfever.com. Web. 19 June 2020. .
[17] "9.5.2. The Cosine Similarity Algorithm - 9.5. Similarity Algorithms." Neo4j.com. Web. 23 June 2020. .
[18] Brownlee, Jason. "Classification Accuracy Is Not Enough: More Performance Measures You Can Use." Machine Learning Mastery. N.p., 2019. Web. 10 June 2020. .
[19] Natarajan, Senthilselvan, et al. "Resolving data sparsity and cold start problem in collaborative filtering recommender system using linked open data." Expert Systems with Applications 149 (2020): 113248.
[20] Datta, Debajit, et al. "Comparison of Performance of Parallel Computation of CPU Cores on CNN model." 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE). IEEE, 2020.
[21] Aljunid, Mohammed Fadhel, and Manjaiah Dh. "An Efficient Deep Learning Approach for Collaborative Filtering Recommender System." Procedia Computer Science 171 (2020): 829-836.
[22] Alhijawi, Bushra. "Improving Collaborative Filtering Recommender System Results using Optimization Technique." Proceedings of the 2019 3rd International Conference on Advances in Artificial Intelligence. 2019.
[23] Datta, Debajit, and Dheeba J. "Exploration of Various Attacks and Security Measures Related to the Internet of Things." International Journal of Recent Technology and Engineering (IJRTE) 9.2 (2020): 175-184.
[24] Mohammadpour, Touraj, et al. "Efficient clustering in collaborative filtering recommender system: Hybrid method based on genetic algorithm and gravitational emulation local search algorithm." Genomics 111.6 (2019): 1902-1912.
[25] Mokarrama, Miftahul Jannat, Sumi Khatun, and Mohammad Shamsul Arefin. "A content-based recommender system for choosing universities." Turkish Journal of Electrical Engineering & Computer Sciences 28.4 (2020): 2128-2142.
[26] Deldjoo, Yashar, Markus Schedl, and Mehdi Elahi. "Movie genome recommender: A novel recommender system based on multimedia content." 2019 International Conference on Content-Based Multimedia Indexing (CBMI). IEEE, 2019.
[27] Anand, Poonam Bhatia, and Rajender Nath. "Content-Based Recommender Systems." Recommender System with Machine Learning and Artificial Intelligence: Practical Tools and Applications in Medical, Agricultural and Other Industries (2020): 167.
[28] Loboda, Olga, et al. "Content-based Recommender Systems for Heritage: Developing a Personalised Museum Tour." Proceedings DSRS-Turing’19. London, 21-22nd Nov, 2019 (2019).
[29] Datta, Debajit et al. "Neural Machine Translation Using Recurrent Neural Network." International Journal of Engineering and Advanced Technology (IJEAT) 9.4 (2020): 1395-1400.
[30] Cami, Bagher Rahimpour, Hamid Hassanpour, and Hoda Mashayekhi. "User preferences modeling using dirichlet process mixture model for a content-based recommender system." Knowledge-Based Systems 163 (2019): 644-655.