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Gaussian process factorization machines for context-aware recommendations

Trung V. Nguyen, Alexandros Karatzoglou, Linas Baltrunas
2014 Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval - SIGIR '14  
To address this limitation, we develop a novel and powerful non-linear probabilistic algorithm for context-aware recommendation using Gaussian processes.  ...  The method which we call Gaussian Process Factorization Machines (GPFM) is applicable to both the explicit feedback setting (e.g. numerical ratings as in the Netflix dataset) and the implicit feedback  ...  Gaussian Process Factorization Machines Having introduced GPs, we now describe the Gaussian Process Factorization Machines (GPFMs) for context-aware recommendations.  ... 
doi:10.1145/2600428.2609623 dblp:conf/sigir/NguyenKB14 fatcat:drmev4shgzd2veljcs6gq6k3dm

Gaussian Process Latent Variable Model Factorization for Context-aware Recommender Systems [article]

Wei Huang, Richard Yi Da Xu
2019 arXiv   pre-print
Context-aware recommender systems (CARS) have gained increasing attention due to their ability to utilize contextual information.  ...  Compared to traditional recommender systems, CARS are, in general, able to generate more accurate recommendations. Latent factors approach accounts for a large proportion of CARS.  ...  Recently, a GP-based factorization machine for context-aware recommender systems was proposed [21] , and it can outperform factorization machines and tensor factorization methods.  ... 
arXiv:1912.09593v1 fatcat:sef3xt4pmzh3npwqlebspwl3ru

Context Aware Recommender System Algorithms: State of the Art and Focus on Factorization Based Methods

Fatima Zahra Lahlou, Houda Benbrahim, Ismail Kassou
2017 Electronic Journal of Information Technology  
Then we study factorization models used for the Context Aware Recommendation task and suggest some possible research directions for developing more performing contextual modeling CARS algorithms.  ...  Context Aware Recommender Systems (CARS) have become an important research area since its introduction in 2001 by (Herlocker and Konstan, 2001) and (Adomavicius and Tuzhilin, 2001).  ...  (Shi et al., 2012) Tensor Factorization iTALS (Hidasi and Tikk, 2012) Tensor Factorization Gaussian Process Factorization Machines (Nguyen et al., 2014) Gaussian Process Contextual SLIM (Zheng  ... 
doaj:5a26071411994898b1c4c07928e7d0b8 fatcat:ufs7xpmpyfd5topui64ynqwfvu

Gradient boosting factorization machines

Chen Cheng, Fen Xia, Tong Zhang, Irwin King, Michael R. Lyu
2014 Proceedings of the 8th ACM Conference on Recommender systems - RecSys '14  
We refer to recommendation with auxiliary information as context-aware recommendation. Context-aware Factorization Machines (FM) is one of the most successful context-aware recommendation models.  ...  Thus, one important challenge for context-aware recommendation is how to effectively select "good" interaction features.  ...  Relation to Factorization Machines: Factorization Machines is a strong baseline method for context-aware recommendation [25] .  ... 
doi:10.1145/2645710.2645730 dblp:conf/recsys/ChengXZKL14 fatcat:t55dxa7ztfcphmfpn7qc3pnzla

A Review-based Context-Aware Recommender Systems: Using Custom NER and Factorization Machines

Rabie Madani, Abderrahmane Ez-zahout
2022 International Journal of Advanced Computer Science and Applications  
In this paper, we present a Context Aware Recommender System model, based on a Bidirectional Encoder Representations from Transformers (BERT) pretrained model to customize Named Entity Recognition (NER  ...  The model allows to automatically extract contextual information from reviews then insert extracted data into a Contextual Machine Factorization to compte and predict ratings.  ...  MACHINE FACTORIZATION FOR CONTEXT AWARE RECOMMENDER SYSTEMS Factorization machines (FM) proposed by Rendle [25] , a general-purpose supervised learning algorithm that could be used in regression and classification  ... 
doi:10.14569/ijacsa.2022.0130365 fatcat:u2yhfscydbgalbiw6nzoqeto6u

Context-Aware Recommendations with Random Partition Factorization Machines

Shaoqing Wang, Cuiping Li, Kankan Zhao, Hong Chen
2017 Data Science and Engineering  
Experimental results demonstrate that RPFM outperforms state-of-the-art context-aware recommendation methods.  ...  We propose a Random Partition Factorization Machines (RPFM) by adopting random decision trees to split the contexts hierarchically to better capture the local complex interplay.  ...  [16] developed a nonlinear probabilistic algorithm for context-aware recommendation using Gaussian processes which is called Gaussian Process Factorization Machines (GPFM).  ... 
doi:10.1007/s41019-017-0035-3 fatcat:4mzfjedborcu7o6yqd36ak2fsa

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.  ...  The biggest challenge for a recommender system is to produce meaningful recommendations by using contextual user-item rating information.  ...  [39] propose context aware predictor based on factorization machines [17] .  ... 
doi:10.1109/access.2019.2897003 fatcat:2yphnhkxtfhatchkx22iqxihae

Context computing for internet of things

Hector John T. Manaligod, Michael Joseph S. Diño, Supratip Ghose, Jungsoo Han
2019 Journal of Ambient Intelligence and Humanized Computing  
Context-aware computing is an ambient-intelligence environment for adapting to the situations around humans, to their surroundings, and their use of software and hardware.  ...  The central issue is to introduce selected research papers which includes trends in topics like context computing for the internet of things, context computing for networks, ambient embedded systems, context  ...  The fifth paper by Park et al. (2018) introduces an ambient-intelligence context aware-based intrusion detection system (IDS) using machine learning for a smart factory.  ... 
doi:10.1007/s12652-019-01560-3 fatcat:bfwo4fth6zfy3d4glttl6xujbq

Learning Context-aware Latent Representations for Context-aware Collaborative Filtering

Xin Liu, Wei Wu
2015 Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '15  
In this paper, we propose a generic framework to learn contextaware latent representations for context-aware collaborative filtering.  ...  Experiments conducted over three real-world datasets demonstrate that our model significantly outperforms not only the base model but also the representative context-aware recommendation models.  ...  A non-linear probabilistic algorithm for context-aware recommendation was proposed using Gaussian processes.  ... 
doi:10.1145/2766462.2767775 dblp:conf/sigir/LiuW15 fatcat:upkde666arafnlio7gfw3oy5xe

Points of Interest Recommendation Based on Context-aware

Fung Wong, Shijun Lee, Quanrui Wong
2015 International Journal of Hybrid Information Technology  
In this paper, a POIs recommendation model based on context-aware is built by combining the two ideas and it makes evaluation for RSI.  ...  Context-awareness Model Context-awareness is a novel technology that the elements of time, space, user's history behavior could be extracted and sorted by some spatial-temporal series rules.  ...  Therefore, in this paper the two kinds of thoughts are combined for POIs Recommendation based on Context-aware so that the accuracy rate of POIs Recommendation could be improved.  ... 
doi:10.14257/ijhit.2015.8.3.06 fatcat:tjkxg2i7o5a6bpoiena7ac4v3q

Towards Non-linear Social Recommendation Using Gaussian Process

Jiyong Zhang, Xin Liu, Xiaofei Zhou
2022 IEEE Access  
To handle these issues, we propose a novel, non-linear latent factor model for social recommendations leveraging Gaussian process.  ...  For instance, matrix factorization based models linearly combine latent factors of relevant users and items.  ...  GPFM [42] tries to improve context-aware recommendations by relaxing the assumption of linear combination of latent factors of users, items and contexts like in Factorization Machines [43] .  ... 
doi:10.1109/access.2022.3141795 fatcat:v22i5psz35gezfl2u3fyfdge7q

Factorization Machines for Data with Implicit Feedback [article]

Babak Loni, Martha Larson, Alan Hanjalic
2018 arXiv   pre-print
In this work, we propose FM-Pair, an adaptation of Factorization Machines with a pairwise loss function, making them effective for datasets with implicit feedback.  ...  We also propose how to apply FM-Pair effectively on two collaborative filtering problems, namely, context-aware recommendation and cross-domain collaborative filtering.  ...  [2014] introduced Gaussian Process Factorization Machines (GPFM), a non-linear adaptation of Factorization Machines.  ... 
arXiv:1812.08254v1 fatcat:krbtdxyx6jeghho3ijchwvpj4a

Gender In Gender Out: A Closer Look at User Attributes in Context-Aware Recommendation [article]

Manel Slokom, Özlem Özgöbek, Martha Larson
2022 arXiv   pre-print
In experiments with a conventional context-aware recommender system that leverages side information, we show that user attributes do not always improve recommendation.  ...  This information is a weak signal that could in the future be exploited for calibration or studied further as a privacy leak.  ...  For further examples of recent work, see [16, 26] . In this work, we choose to focus on Factorization Machines (FMs) [19] , classically used for context-aware recommendation.  ... 
arXiv:2207.14218v1 fatcat:vxzqb7pysfgqrlltgbp2b7xq3m

A Survey of Context-Aware Recommendation Schemes in Event-Based Social Networks

Xiaomei Huang, Guoqiong Liao, Naixue Xiong, Athanasios V. Vasilakos, Tianming Lan
2020 Electronics  
We begin by illustrating the concept of the term context and the paradigms of conventional context-aware recommendation process.  ...  To provide better service for users, Context-Aware Recommender Systems (CARS) in EBSNs have recently been singled out as a fascinating area of research.  ...  Figure 2 . 2 Process of context-aware recommendation in EBSNs. 3 gives the list of the techniques used in the literature.  ... 
doi:10.3390/electronics9101583 fatcat:hdapa7y4vfcefcprlngjnjn5ai

Exploring Temporal and Spatial Features for Next POI Recommendation in LBSNs

Miao Li, Wenguang Zheng, Yingyuan Xiao, Ke Zhu, Wei Huang
2021 IEEE Access  
In our model, we propose a unified approach to calculate context-aware similarities between different users by investigating the influences of both temporal and spatial features for the users.  ...  We also propose an approach to dynamically generate different POI recommendation lists for a particular user according to different current context information of the user.  ...  The algorithm for Context-aware similarity is shown in Algorithm 1.  ... 
doi:10.1109/access.2021.3061502 fatcat:u4yfksypvfbg7em5ph7fkdrtje
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