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Integrating Context Similarity with Sparse Linear Recommendation Model
[chapter]
2015
Lecture Notes in Computer Science
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We integrate context similarity with sparse linear recommendation model to build a similarity-learning model. ...
Most of the existing approaches to context-aware recommendation involve directly incorporating context into standard recommendation algorithms (e.g., collaborative filtering, matrix factorization). ...
We developed similarity-learning models in which contextual similarity is learned by optimizing the ranking score for top-N recommendations. ...
doi:10.1007/978-3-319-20267-9_33
fatcat:rdzugunxnjghzhjvpicsqarh64
Factorization models for context-/time-aware movie recommendations
2010
Proceedings of the Workshop on Context-Aware Movie Recommendation - CAMRa '10
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We suggest to use Pairwise Interaction Tensor Factorization (PITF), a method used for personalized tag recommendation, to model the temporal (week) context in Task 1 of the challenge. ...
In the scope of the Challenge on Context-aware Movie Recommendation (CAMRa2010), context can mean temporal context (Task 1), mood (Task 2), or social context (Task 3). ...
Thus we use the rank estimates for combining factor models with different dimensionality and regularization. ...
doi:10.1145/1869652.1869654
fatcat:nk3mfh2c3fcwfmxstufis5eyam
Rank-GeoFM
2015
Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '15
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In our model, POIs both with and without check-ins will contribute to learning the ranking and thus the data sparsity problem can be alleviated. ...
In the proposed model, we consider that the check-in frequency characterizes users' visiting preference and learn the factorization by ranking the POIs correctly. ...
with matrix factorization model. ...
doi:10.1145/2766462.2767722
dblp:conf/sigir/LiCLPK15
fatcat:tf5ndb3zknbknoaxfcn44h7s6q
CSLIM
2014
Proceedings of the 8th ACM Conference on Recommender systems - RecSys '14
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In recent years, context-aware matrix factorization (CAMF) has emerged as an extension of the matrix factorization technique that also incorporates contextual conditions. ...
Based on the experimental evaluations over several context-aware data sets, we demonstrate that CLSIM can be an effective approach for context-aware recommendations, in many cases outperforming state-of-the-art ...
Another approach, context-aware matrix factorization (CAM-F) [2] was developed to adapt to contextual recommendations by modeling contextual dependencies with the user or item dimensions. ...
doi:10.1145/2645710.2645756
dblp:conf/recsys/ZhengMB14
fatcat:76y5hxvebvcore62t5s4gf5exi
Modeling and Learning Context-Aware Recommendation Scenarios Using Tensor Decomposition
2011
2011 International Conference on Advances in Social Networks Analysis and Mining
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Recently, multivariate models like matrix factorization have become popular to combine the advantages of both perspectives. ...
While offering good predictive performance, so far those models do not exploit possibly available rich semantic context. ...
CONTEXT-AWARE RECOMMENDATION TENSOR DECOMPOSITION Our goal is to model an entity together with multiple contexts. ...
doi:10.1109/asonam.2011.56
dblp:conf/asunam/WermserRT11
fatcat:v4t7z7kebrdnldnnbsjfo5nuqy
Deviation-based and similarity-based contextual SLIM recommendation algorithms
2014
Proceedings of the 8th ACM Conference on Recommender systems - RecSys '14
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For example, differential context modeling (DCM) was modified based on traditional neighborhood collaborative filtering (NBCF), context-aware matrix factorization (CAMF) coupled contextual dependency with ...
the matrix factorization technique (MF), and tensor factorization directly models contexts as additional dimensions in the multi-dimensional space, etc. ...
Extensions of standard matrix factorization (MF) approach have incorporated contextual variables too, such as time-aware MF [3] and context-aware matrix factorization (CAMF) [2] . ...
doi:10.1145/2645710.2653368
dblp:conf/recsys/Zheng14
fatcat:ztalldzm7rhftlb3vjse33ewgi
TFMAP
2012
Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval - SIGIR '12
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TFMAP uses tensor factorization to model implicit feedback data (e.g., purchases, clicks) with contextual information. ...
We propose TFMAP, a model that directly maximizes Mean Average Precision in aim of creating an optimally ranked list of items for individual users under a given context. ...
In this paper we present a generic CF model that is based on a generalization of matrix factorization to address context-aware recommendations. ...
doi:10.1145/2348283.2348308
dblp:conf/sigir/ShiKBLHO12
fatcat:idfktcbd3zbzro5ktfdvqtqgmy
Context-aware tensor decomposition for relation prediction in social networks
2012
Social Network Analysis and Mining
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There are applications, where models with n-ary relations with n > 3 need to be considered, which is the topic of this paper. ...
While the first approach, the Context-Aware Recommendation Tensor Decomposition (CARTD), proposes an efficient optimization criterion and decomposition ...
in the factorization matrix V I,C k , which models the interaction of the kth context with the respective entity. ...
doi:10.1007/s13278-012-0069-5
fatcat:ysogtuva65eovhcvvjbuescgsq
DeepCARSKit: A Demo and User Guide
2022
User Modeling, Adaptation, and Personalization
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With the development of deep learning based recommendation techniques, the neural network models have also been utilized to improve the quality of the context-aware recommendations. ...
It provides a unified platform for implementing and evaluating context-aware recommendation models based on neural networks. ...
, and context-aware matrix factorization [2] or tensor factorization [5] , as well as ranking algorithms based on sparse linear methods [18] . ...
doi:10.1145/3511047.3536417
dblp:conf/um/Zheng22
fatcat:uhqkxoxilbhctf2u4awzoh3jrm
Deviation-Based Contextual SLIM Recommenders
2014
Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management - CIKM '14
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One approach to CARS that has been shown to be particularly effective is Context-Aware Matrix Factorization (CAMF). ...
Context-aware recommender systems (CARS) help improve the effectiveness of recommendations by adapting to users' preferences in different contextual situations. ...
in a more accurate ranking model for context-aware recommendations. ...
doi:10.1145/2661829.2661987
dblp:conf/cikm/ZhengMB14
fatcat:b2hsklm6vjbijmntnk5gxx5kte
Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey
2018
Computer and Information Science
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We conducted a focused review of literature in the areas of Context-aware Recommender Systems utilizing Matrix Factorization approaches. ...
The main contribution of this paper is a survey of Matrix Factorization techniques for Context-aware Collaborative Filtering Recommender Systems. ...
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
Leverage Implicit Feedback for Context-aware Product Search
[article]
2020
arXiv
pre-print
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Our results also show that our proposed model is more effective than word-based context-aware models. ...
Furthermore, we propose an end-to-end context-aware embedding model which can capture long-term and short-term context dependencies. ...
Baselines We compare our short-term context-aware embedding model (SCEM) with four groups of baseline, retrieval model without using context, long-term, short-term and long-short-term context-aware models ...
arXiv:1909.02065v2
fatcat:o2ceae6g5bav3ioddwjbjgxsgy
Context-Aware User Modeling Strategies for Journey Plan Recommendation
[chapter]
2015
Lecture Notes in Computer Science
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We describe two different strategies for context-aware user modeling in the journey planning domain. ...
This paper shows how we applied context-aware recommendation technologies in an existing journey planning mobile application to provide personalized and context-dependent recommendations to users. ...
Another popular example following the contextual modeling paradigm is Context-Aware Matrix Factorization (CAMF) [2] . ...
doi:10.1007/978-3-319-20267-9_6
fatcat:xifpevhasbamrgcrf73g7qdxue
An Intelligent Group Event Recommendation System in Social networks
[article]
2020
arXiv
pre-print
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In this paper, we propose an Attention-based Context-aware Group Event Recommendation model (ACGER) in EBSNs. ...
In these models, the influence of contexts on groups is considered but simply defined in a manual way, which cannot model the complex and deep interactions between contexts and groups. ...
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
Factorization Machines for Data with Implicit Feedback
[article]
2018
arXiv
pre-print
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as BPR-MF (BPR with Matrix Factorization model). ...
We also propose how to apply FM-Pair effectively on two collaborative filtering problems, namely, context-aware recommendation and cross-domain collaborative filtering. ...
They also introduced a pairwise optimization model for GPFM for datasets with implicit feedback and used it for context-aware recommendations. ...
arXiv:1812.08254v1
fatcat:krbtdxyx6jeghho3ijchwvpj4a
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