What are some emerging techniques in recommendation systems?

Sep 17, 2021 ... A variety of machine learning applications and software use recommender systems that are empowered by machine learning techniques and tools for ... In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF ... Dec 29, 2022 ... The collaborative filtering (CF) mechanism was used in this work. Singhal et al. [3] focused on the role of deep learning in the e-Learning ... Aug 14, 2019 ... Brownlee, “A Gentle Introduction to Matrix Factorization for Machine Learning,” Linear Algebra, 2018. . G. Li and Q. Chen, “Exploiting ... May 17, 2023 ... Abstract. This paper introduces a deep learning approach that uses multiple models to enhance the accuracy and diversity of collaborative ... Collaborative filtering is the de facto standard for analyzing users' activities and building recommendation systems for items. Collaborative filtering is fully data-driven since it does not require any extra information other than test outcomes while logistic regression is applicable ... The fact is that the recommendation has to be based on user and item reviews, and a machine learning model instead of deep learning model predictions. The ... There is a general solution to this problem, called collaborative filtering, which works like this: look at what products the current user has used or liked. Proceedings of the Ninth Asian Conference on Machine Learning, PMLR 77:224-239, 2017. Abstract. Collaborative Filtering (CF), a well-known approach in ...