Recommendation systems: Principles, methods and evaluation
TLDR
The different characteristics and potentials of different prediction techniques in recommendation systems are explored in order to serve as a compass for research and practice in the field of recommendation systems.About:
This article is published in Egyptian Informatics Journal.The article was published on 2015-11-01 and is currently open access. It has received 861 citations till now. The article focuses on the topics: Recommender system & Information filtering system.read more
Citations
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Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
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Emotion Based Music Recommendation System Using Wearable Physiological Sensors
TL;DR: An emotion based music recommendation framework that learns the emotion of a user from the signals obtained via wearable physiological sensors that can be integrated to any recommendation engine.
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Recommendation system based on deep learning methods: a systematic review and new directions
TL;DR: This paper is the first SLR specifically on the deep learning based RS to summarize and analyze the existing studies based on the best quality research publications and indicated that autoencoder models are the most widely exploited deep learning architectures for RS followed by the Convolutional Neural Networks and the Recurrent Neural Networks.
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Revealing customers’ satisfaction and preferences through online review analysis: The case of Canary Islands hotels
Ali Ahani,Mehrbakhsh Nilashi,Elaheh Yadegaridehkordi,Louis Sanzogni,A. Rashid Tarik,Kathy Knox,Sarminah Samad,Othman Ibrahim +7 more
TL;DR: In this article, the authors identify the important factors for hotel selection based on previous travelers' reviews on TripAdvisor and develop a new method for the use of Multi-Criteria Decision-Making (MCDM) and soft computing approaches.
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An effective collaborative movie recommender system with cuckoo search
Rahul Katarya,Om Prakash Verma +1 more
TL;DR: A novel recommender system has been discussed which makes use of k-means clustering by adopting cuckoo search optimization algorithm applied on the Movielens dataset and may provide high performance regarding reliability, efficiency and delivers accurate personalized movie recommendations when compared with existing methods.
References
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Book
Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Journal ArticleDOI
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
TL;DR: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches.
Journal ArticleDOI
Matrix Factorization Techniques for Recommender Systems
TL;DR: As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.