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Integrating Context Similarity with Sparse Linear Recommendation Model [chapter]

Yong Zheng, Bamshad Mobasher, Robin Burke
2015 Lecture Notes in Computer Science  
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

Zeno Gantner, Steffen Rendle, Lars Schmidt-Thieme
2010 Proceedings of the Workshop on Context-Aware Movie Recommendation - CAMRa '10  
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

Xutao Li, Gao Cong, Xiao-Li Li, Tuan-Anh Nguyen Pham, Shonali Krishnaswamy
2015 Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '15  
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

Yong Zheng, Bamshad Mobasher, Robin Burke
2014 Proceedings of the 8th ACM Conference on Recommender systems - RecSys '14  
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

Hendrik Wermser, Achim Rettinger, Volker Tresp
2011 2011 International Conference on Advances in Social Networks Analysis and Mining  
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

Yong Zheng
2014 Proceedings of the 8th ACM Conference on Recommender systems - RecSys '14  
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

Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, Martha Larson, Alan Hanjalic, Nuria Oliver
2012 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval - SIGIR '12  
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

Achim Rettinger, Hendrik Wermser, Yi Huang, Volker Tresp
2012 Social Network Analysis and Mining  
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

Yong Zheng
2022 User Modeling, Adaptation, and Personalization  
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

Yong Zheng, Bamshad Mobasher, Robin Burke
2014 Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management - CIKM '14  
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

Mohamed Hussein Abdi, George Onyango Okeyo, Ronald Waweru Mwangi
2018 Computer and Information Science  
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]

Keping Bi, Choon Hui Teo, Yesh Dattatreya, Vijai Mohan, W. Bruce Croft
2020 arXiv   pre-print
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]

Victor Codina, Jose Mena, Luis Oliva
2015 Lecture Notes in Computer Science  
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]

Guoqiong Liao, Xiaomei Huang, Neal N. Xiong, Changxuan Wan
2020 arXiv   pre-print
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]

Babak Loni, Martha Larson, Alan Hanjalic
2018 arXiv   pre-print
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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