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Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey

Mohamed Hussein Abdi, George Onyango Okeyo, Ronald Waweru Mwangi
2018 Computer and Information Science  
The main contribution of this paper is a survey of Matrix Factorization techniques for Context-aware Collaborative Filtering Recommender Systems.  ...  We conducted a focused review of literature in the areas of Context-aware Recommender Systems utilizing Matrix Factorization approaches.  ...  The extension of standard Matrix Factorization has incorporated context information such as Time-Aware Matrix Factorization (Liu, Cao, Zhao, & Yang, 2010) , Context-aware Matrix Factorization (Baltrunas  ... 
doi:10.5539/cis.v11n2p1 fatcat:vyyrbt7exba2bhufdwoyrad3fa

Context Aware Recommender Systems: A Novel Approach Based on Matrix Factorization and Contextual Bias

Mario Casillo, Brij B. Gupta, Marco Lombardi, Angelo Lorusso, Domenico Santaniello, Carmine Valentino
2022 Electronics  
Among the possible exploited information, the context is widely used in literature and leads to the definition of the Context-Aware Recommender System.  ...  This paper proposes a Context-Aware Recommender System based on the concept of embedded context. This technique has been tested on different datasets to evaluate its accuracy.  ...  [62] propose a Contextual Modeling approach defined Context-Aware Matrix Factorization (CAMF).  ... 
doi:10.3390/electronics11071003 fatcat:gpeeyo2fs5apppkzamqmyulw2i

Context-Aware Recommendation Methods

Tosin Agagu, Thomas Tran
2018 International Journal of Intelligent Systems and Applications  
We also compared our work to other context-aware recommendation approaches. Our results show that grouping ratings by context and jointly factorizing with common factors improves prediction accuracy.  ...  A context-aware recommender system attempts to generate better recommendations using contextual information.  ...  The context-aware matrix factorization (CAMF) methods in [6] . We compare our methods with CAMF-C and CAMF-CI in [6] .  ... 
doi:10.5815/ijisa.2018.09.01 fatcat:r57c7r4dwnclldlbtn7ycvrkiy

CBPF: leveraging context and content information for better recommendations [article]

Zahra Vahidi Ferdousi, Dario Colazzo, Elsa Negre
2018 arXiv   pre-print
Among these, context-aware recommender systems aim at personalizing as much as possible the recommendations based on the context situation in which the user is.  ...  In this paper we present an approach integrating contextual information into the recommendation process by modeling either item-based or user-based influence of the context on ratings, using the Pearson  ...  context-aware matrix factorization (CAMF) [5] with its several derived model: CAMF-C, CAMF-CI, CAMF-CC and CAMF-CU ; contextual sparse linear method (CSLIM) [25] ; the similarity-based approaches of  ... 
arXiv:1810.00751v1 fatcat:z4hqvznosreqzdcc7m7ktrkhb4

A Comparative Analysis of Various Approaches for Incorporating Contextual Information into Recommender Systems

Quang-Hung Le, Son-Lam VU, Anh-Cuong Le
2022 Journal of Computer Science  
Additionally, we provide an in-depth analysis of the most notable studies to date and point out the strengths, weaknesses and application scenarios for each of the approaches.  ...  We also empirically evaluate the real-world datasets, analyzing distinct recommendation quality metrics and characteristics of the datasets.  ...  Baltrunas et al. (2011b) proposed a new method, CAMF (context-aware matrix factorization), which is based on matrix factorization and showed that with small size data and fewer parameters, it is still  ... 
doi:10.3844/jcssp.2022.187.203 fatcat:vdzq5tmglraprnvl2dtpzn5yce

Context-Aware Recommendation via Graph-Based Contextual Modeling and Postfiltering

Hao Wu, Kun Yue, Xiaoxin Liu, Yijian Pei, Bo Li
2015 International Journal of Distributed Sensor Networks  
Context-aware recommender systems generate more relevant recommendations by adapting them to the specific contextual situation of the user and have become one of the most active research areas in the recommender  ...  To assist the development and use of context-aware recommendation capabilities, we propose a graph-based framework to model and incorporate contextual information into the recommendation process in an  ...  , 2014FA023), the Program for Innovative Research Team in Yunnan University (XT412011), and the National Natural Science Foundation of China (61472345).  ... 
doi:10.1155/2015/613612 fatcat:jp6cwspmkjhszbsskd2idzntme

Exploiting the Semantic Similarity of Contextual Situations for Pre-filtering Recommendation [chapter]

Victor Codina, Francesco Ricci, Luigi Ceccaroni
2013 Lecture Notes in Computer Science  
Context-aware recommender systems aim at outperforming traditional context-free recommenders by exploiting information about the context under which the users' ratings are acquired.  ...  We show that it outperforms state-of-the-art context-aware recommendation techniques.  ...  Factorization (MF) method [15] and other state-of-the-art context-aware recommendation techniques.  ... 
doi:10.1007/978-3-642-38844-6_14 fatcat:nhzl4h7buzg2ljdafaxn5ueuym

Deep Learning-Based Context-Aware Recommender System Considering Contextual Features

Soo-Yeon Jeong, Young-Kuk Kim
2021 Applied Sciences  
In this paper, we propose a deep learning-based context-aware recommender system that considers the contextual features.  ...  A context-aware recommender system can make recommendations to users by considering contextual information such as time and place, not only the scores assigned to items by users.  ...  We selected CAMF (Context-aware Matrix Factorization), ItemSplitting-BiasdMF, and CSLIM (Contextual Sparse Linear Method) as state-of-the-art context-aware recommendations to compare with the proposed  ... 
doi:10.3390/app12010045 fatcat:si2xy6qdujbhnhrkwqhmcpzo6u

Kernel Context Recommender System (KCR): A Scalable Context-Aware Recommender System Algorithm

Misbah Iqbal, Mustansar Ali Ghazanfar, Asma Sattar, Muazzam Maqsood, Salabat Khan, Irfan Mehmood, Sung Wook Baik
2019 IEEE Access  
INDEX TERMS Context, context-aware kernel mapping recommender systems, recommender system kernel.  ...  A context is a vast term that may consider various aspects; for example, a user's social circle, time, mood, location, weather, company, day type, an item's genre, location, and language.  ...  [44] proposed a Context Aware Matrix Factorization Algorithm (CAMF), which utilizes the interaction between users and contexts.  ... 
doi:10.1109/access.2019.2897003 fatcat:2yphnhkxtfhatchkx22iqxihae

Context-Aware Collaborative Filtering Using Context Similarity: An Empirical Comparison

Yong Zheng
2022 Information  
Context-aware recommender systems additionally consider context information and adapt the recommendations to different situations.  ...  A process of context matching, therefore, enables the system to utilize rating profiles in the matched contexts to produce context-aware recommendations.  ...  matrix factorization (CAMF) [13] .  ... 
doi:10.3390/info13010042 fatcat:5lltmj4lobc5bj7s5dou6o4msm

A Pre-filtering Approach for Incorporating Contextual Information into Deep Learning Based Recommender Systems

Isam Mashhour Al Jawarneh, Paolo Bellavista, Antonio Corradi, Luca Foschini, Rebecca Montanari, Javier Berrocal, Juan M. Murillo
2020 IEEE Access  
INDEX TERMS Deep learning, recommender systems, collaborative filtering, context awareness, apache spark.  ...  People seek relevant information, suggestions, and recommendations in an overloaded online world and through social ties regarding their daily activities, including places to visit and restaurants to try  ...  That is the following: • Context-Aware Matrix Factorization (CAMF) [22] , which is a method for incorporating contextualinformation into MF.  ... 
doi:10.1109/access.2020.2975167 fatcat:ynobpk4bv5fcln7zocyp546hze

Graph Neural Network and Context-Aware Based User Behavior Prediction and Recommendation System Research

Qian Gao, Pengcheng Ma, Akbar S. Namin
2020 Computational Intelligence and Neuroscience  
Due to the influence of context information on user behavior, context-aware recommendation system (CARS) has attracted extensive attention in recent years.  ...  Through modeling user behavior, we can explore user preferences in different context environments, so as to make personalized recommendations for users.  ...  Related Work Traditional Context-Aware Recommendation System.  ... 
doi:10.1155/2020/8812370 pmid:33312192 pmcid:PMC7721495 fatcat:tob4mjpsafch7oe646soak5nfm

An Intelligent Group Event Recommendation System in Social networks [article]

Guoqiong Liao, Xiaomei Huang, Neal N. Xiong, Changxuan Wan
2020 arXiv   pre-print
The importance of contexts has been widely recognized in recommender systems for individuals.  ...  In this paper, we propose an Attention-based Context-aware Group Event Recommendation model (ACGER) in EBSNs.  ...  Some studies work on contextualize Matrix Factorization (FM) approach. [28] presents Context-Aware Matrix Factorization (CAMF), which extends MF by considering the influence of contexts on items.  ... 
arXiv:2006.08893v1 fatcat:rcpu2gssjjeevd5pa4chjkp4lu

A Deep Learning Based Approach for Context-Aware Multi-Criteria Recommender Systems

Son-Lam VU, Quang-Hung LE
2023 Computer systems science and engineering  
Up to now, how to exploit context in MCRSs is still an open issue. This paper proposes a novel approach, which relies on deep learning for context-aware multi-criteria recommender systems.  ...  Context-aware recommender systems (CARSs) and multi-criteria recommender systems (MCRSs) are extensions of traditional recommender systems.  ...  Context-Aware Multi-Criteria Recommendation Problem Statement Context-aware multi-criteria recommender systems (CA-MCRSs) are an extension of MCRSs, giving recommendations to users and accounting for contextual  ... 
doi:10.32604/csse.2023.025897 fatcat:jpi52mgzj5dqvdp6efq353ss6m

Interpreting Contextual Effects By Contextual Modeling In Recommender Systems [article]

Yong Zheng
2017 arXiv   pre-print
Context-aware recommender systems (CARS) additionally take context information into considering in the recommendation process, since user's tastes on the items may vary from contexts to contexts.  ...  Several context-aware recommendation algorithms have been proposed and developed to improve the quality of recommendations.  ...  Context-aware matrix factorization (CAMF) [4] is the rst attempt as the deviation-based contextual modeling approach, where it replaces the P(u, t) by the predictive function in matrix factorization.  ... 
arXiv:1710.08516v1 fatcat:afft2h3cpjhh3hhnhvf5ki5wiu
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