MULTIMODEL RECOMMEND SYSTEM
Main Article Content
Abstract
From the last few decades Recommender System has tremendous rise in many of the webservices. Now -a-days most of the people uses e-commerce sites or online advertisements and the famous websites like Netflix, You-tube makes use of recommendation systems. It is a field of increasing importance with intense potential. Recommender System are created to solve the immense issues of the customer to make a best and easiest decision by analysing information and provides the more relevant and personalized information according to the user’s choice.Most of the recommend system works by considering the feedback from the customers or content of items. Multimodal machine learning aims to build model that can process and relate the information from multiple modalities.Recommend System makes use multi- modality where we have very different types of inputs such as image, text, speech, graph etc, which are modalities and processed by the same machine learning model. Our paper focuses on recommendation methods that can be used in Multimodal Recommend System. A recommender system compels information filtering system running on machine learning (ML) algorithms that can predict a customer’s ratings or preferences for a product.
Downloads
Metrics
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
Garanayak M, Sahoo S, Mohanty SN, Jagadev AK (2020) An automated recommender system for educational institute in India. EAI Endorsed Trans Scalable Inf Syst 20(26):1-13.
Kuanr M, Rath BK, Mohanty SN (2018) Crop recommender system for the farmers using mamdani fuzzy inference model. Int. J Eng Technol7(4.15):277-280.
Garanayak M, Mohanty SN, Jagadev AK, Sahoo S (2019) Recommender System using item based collaborative filtering (CF) and K-means. Int J Knowl-based Intell Eng Syst23(2):93-101.
Mubbashir AM, Ghazanfar MA, Mehmood Z, Alyoubi KH, Alfakeeh AS (2019) Unifying user similarity and social trust to generate powerful recommendations for smart cities using collaborative filteringbased recommender systems. Springer, Berlin.
Moran Beladev, Lior Rokach, and Bracha Shapira. 2016. Recommender systems for product bundling. Knowledge-Based Systems 111 (2016), 193–206.
Sonia Bergamaschi, Laura Po, and Serena Sorrentino. 2014. Comparing Topic Models for a Movie Recommendation System..In WEBIST (2). 172–183. [3] David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent dirichlet allocation. Journal of machine Learning research 3, Jan (2003), 993–1022.
Tiago Cunha, Carlos Soares, and André CPLF Carvalho. 2017. Metalearning for Context-aware Filtering: Selection of Tensor Factorization Algorithms. In Proceedings of the Eleventh ACM Conference on Recommender Systems. ACM, 14–22.
Giorgio Gallo, Peter L Hammer, and Bruno Simeone. 1980. Quadratic knapsack problems. In Combinatorial optimization. Springer, 132–149.
Robert Garfinkel, Ram Gopal, Arvind Tripathi, and Fang Yin. 2006. Design of a shopbot and recommender system for bundle purchases. Decision Support Systems 42, 3 (2006), 1974–1986.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770–778. for heterogeneous item recommendation. In Data Mining (ICDM), 2016 IEEE 16th International.