Explain the concept of serendipity in recommender systems.

Aug 14, 2019 ... Brownlee, “A Gentle Introduction to Matrix Factorization for Machine Learning,” Linear Algebra, 2018. . G. Li and Q. Chen, “Exploiting ... Collaborative filtering; deep learning; generative adversarial networks; recommender systems. Issue Date: 2019-03. Publisher: IEEE-INST ELECTRICAL ELECTRONICS ... Feb 12, 2017 ... Recommender System, Collaborative Filtering, Deep Learning. Abstract. Collaborative filtering (CF) is a widely used approach in recommender ... In this paper we describe an alternate approach to IRT parameter estimation and goodness-of-fit motivated by machine learning. Our approach springs from an ... based on similar users using collaborative filtering by Machine learning algorithms and finally the location factor that facilitates smart navigation in the ... Aug 5, 2014 ... These items are atypical for the artist in question, so they aren't good recommendations. Collaborative filtering algorithms will not pick up on ... Jun 5, 2014 ... Like most machine learning algorithms, the most important input that a collaborative filtering algorithm needs is data -- lots of it. So if you ... 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 ... Nov 5, 2020 ... The authors proposed a multi-model deep learning (MMDL) approach by integrating user and item functions to construct a hybrid RS and significant improvement. Collaborative filtering is fully data-driven since it does not require any extra information other than test outcomes while logistic regression is applicable ...