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Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey
2018
Computer and Information Science
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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
2022
Electronics
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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
2018
International Journal of Intelligent Systems and Applications
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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]
2018
arXiv
pre-print
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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
2022
Journal of Computer Science
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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
2015
International Journal of Distributed Sensor Networks
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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]
2013
Lecture Notes in Computer Science
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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
2021
Applied Sciences
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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
2019
IEEE Access
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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
2022
Information
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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
2020
IEEE Access
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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
2020
Computational Intelligence and Neuroscience
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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]
2020
arXiv
pre-print
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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
2023
Computer systems science and engineering
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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]
2017
arXiv
pre-print
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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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