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Leveraging tagging for neighborhood-aware probabilistic matrix factorization

Le Wu, Enhong Chen, Qi Liu, Linli Xu, Tengfei Bao, Lei Zhang
2012 Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12  
Generally, two kinds of approaches to CF, the local neighborhood methods and the global matrix factorization models, have been widely studied.  ...  To that end, in this paper, by leveraging the extra tagging data, we propose a novel unified two-stage recommendation framework, named Neighborhood-aware Probabilistic Matrix Factorization(NHPMF).  ...  Obviously, there are complementary advantages for local neighborhood methods and the global matrix factorization models, and for making better recommendation it is necessary to combine the local information  ... 
doi:10.1145/2396761.2398531 dblp:conf/cikm/WuCLXBZ12 fatcat:tmlqqf7eizglhgjv2xkwdf3s7e

Probabilistic Matrix Factorization with Personalized Differential Privacy [article]

Shun Zhang, Laixiang Liu, Zhili Chen, Hong Zhong
2018 arXiv   pre-print
Probabilistic matrix factorization (PMF) plays a crucial role in recommendation systems.  ...  In this paper, we mainly propose a probabilistic matrix factorization recommendation scheme with personalized differential privacy (PDP-PMF).  ...  PDP-PMF scheme This section is to propose a probabilistic matrix factorization recommendation scheme with personalized differential privacy.  ... 
arXiv:1810.08509v1 fatcat:s3wrde57jrfe3m7wqnxh5w6c4e

Locus recommendation using probabilistic matrix factorization techniques

Rachna Behl, Indu Kashyap
2021 Ingeniería solidaria  
Methodology: Matrix factorization is a model-based collaborative technique for recommending new items to the users.  ...  Originality: A rigorous analysis of Probabilistic Matrix Factorization techniques has been performed on popular LBSNs and the best technique for location recommendation has been identified by comparing  ...  Decomposition [32, 33] , Probabilistic Matrix Factorization (PMF) [25, 34] , and Non-Negative Matrix Factorization [35] .  ... 
doi:10.16925/2357-6014.2021.01.10 fatcat:zxtpbrytdbdkxcfadwgdjoza4e

Learning Personalized Preference of Strong and Weak Ties for Social Recommendation

Xin Wang, Steven C.H. Hoi, Martin Ester, Jiajun Bu, Chun Chen
2017 Proceedings of the 26th International Conference on World Wide Web - WWW '17  
Matrix Factorization model that incorporates the distinction of strong and weak ties for improving recommendation performance.  ...  preferences of strong and weak ties for social recommendation.  ...  PERSONALIZED TIE PREFERENCE MATRIX FACTORIZATION FOR SO-CIAL RECOMMENDATION In this section, we present the proposed new model of Personalized Tie Preference Matrix Factorization (PTPMF) for social recommendation  ... 
doi:10.1145/3038912.3052556 dblp:conf/www/WangHEBC17 fatcat:j3wtqzgfnnhhzawej2ozh5z2du

Probabilistic factor models for web site recommendation

Hao Ma, Chao Liu, Irwin King, Michael R. Lyu
2011 Proceedings of the 34th international ACM SIGIR conference on Research and development in Information - SIGIR '11  
In this paper, aiming at providing accurate personalized Web site recommendations for Web users, we propose a novel probabilistic factor model based on dimensionality reduction techniques.  ...  We also extend the proposed method to collective probabilistic factor modeling, which further improves model performance by incorporating heterogeneous data sources.  ...  The authors also would like to thank the reviewers for their helpful comments.  ... 
doi:10.1145/2009916.2009955 dblp:conf/sigir/MaLKL11 fatcat:egn663hcefa33ek5j3gbnqr4lq

Friends Recommendation with Rating Side Information

Xiang Hu, Wengdong Wang, Xiangyang Gong, Bai Wang, Xirong Que, Hongke Xia
2015 Open Cybernetics and Systemics Journal  
This article proposes a new matrix factorization based method for friends recommendation.  ...  Therefore, friends recommendation becomes a critical function for various online social networks.  ...  To overcome such limitations, this paper has proposed a Gaussian Process based Probabilistic Matrix Factorization (GPPMF) for friends recommendation.  ... 
doi:10.2174/1874110x01509010807 fatcat:ylarmniwaffazjsriro7v23a7m

Point-of-Interest Recommendation in Location Based Social Networks with Topic and Location Awareness [chapter]

Bin Liu, Hui Xiong
2013 Proceedings of the 2013 SIAM International Conference on Data Mining  
Then, a Topic and Location-aware probabilistic matrix factorization (TL-PMF) method is proposed for POI recommendation.  ...  Finally, experiments on real-world LBSNs data show that the proposed recommendation method outperforms state-of-the-art probabilistic latent factor models with a significant margin.  ...  A Topic and Location Aware Probabilistic Matrix Factorization (TL-PMF) Model Since the POI recommendation is personalized, location-aware, and context depended, we introduce a Topic and Location-aware  ... 
doi:10.1137/1.9781611972832.44 dblp:conf/sdm/LiuX13 fatcat:mmpwpj36araovcq2yfuugsws4u

Advances in Algorithms for Time-Dependent Recommender Systems

Pavlos Kefalas, Yannis Manolopoulos
2014 2014 9th International Workshop on Semantic and Social Media Adaptation and Personalization  
Moreover, we examine whether they provide personalized recommendations or not, the recommendation type they support, the data factors/features they use, the preferred methodology with which they model  ...  The main factor is time that refines the final recommendation revealing relations among entities, which can increase accuracy of the proposals.  ...  Matrix-based (MB) models are very popular in recommender systems, known as Matrix Factorization techniques.  ... 
doi:10.1109/smap.2014.36 dblp:conf/smap/KefalasM14 fatcat:zvdibiximfh7nflluv3ussgb7y

A Geographical Factor of Interest Recommended Strategies in Location Based Social Networks

Bulusu Rama, K Sai Prasad, Ayesha Sultana, K Shekar
2018 International Journal of Engineering & Technology  
First, we quickly introduce the shape and records traits of LBSNs, then we current a formalization of user modeling for POI suggestions in LBSNs.  ...  This paper centers on evaluating the scientific classification of client displaying for POI proposals through the information investigation of LBSNs.  ...  proposed a personalized ranking metric that embeds a model for the next new POI recommendation.  ... 
doi:10.14419/ijet.v7i3.27.17649 fatcat:xbhdedvfkbe6tgacryx7fbwyke

Modeling Group Dynamics for Personalized Group-Event Recommendation [chapter]

Sanjay Purushotham, C. -C. Jay Kuo
2015 Lecture Notes in Computer Science  
., for personalized group-event recommendations.  ...  These EBSNs provide rich online and offline user interactions, and rich event content information which can be leveraged for personalized group-event recommendations.  ...  for personalization of group recommenders.  ... 
doi:10.1007/978-3-319-16268-3_51 fatcat:w7u3yoqi7bhyflc3blk72x4ttu

Personalized Recommendations of Locally Interesting Venues to Tourists via Cross-Region Community Matching

Yi-Liang Zhao, Liqiang Nie, Xiangyu Wang, Tat-Seng Chua
2014 ACM Transactions on Intelligent Systems and Technology  
local venue recommendations.  ...  You want to know all about the exciting sights, food outlets, and cultural venues that the locals frequent, in particular those that suit your personal interests.  ...  Matrix Factorization Model A simple approach to extract the latent social dimensions is to use probabilistic matrix factorization (PMF) [Salakhutdinov and Mnih 2008b] , where the underlying assumption  ... 
doi:10.1145/2532439 fatcat:zhof6q32tbb3xd2rxp66g5nwqm

Personalized news recommendation via implicit social experts

Chen Lin, Runquan Xie, Xinjun Guan, Lei Li, Tao Li
2014 Information Sciences  
On the other hand, probabilistic matrix factorization reveals associations among user feedbacks.  ...  To address this problem, model-based collaborative filtering (i.e. matrix factorization, probabilistic matrix factorization) [20, 36] is most commonly adopted to reduce dimensions and consequently reduce  ...  Model based collaborative filtering, including matrix factorization [20] (MF) and probabilistic matrix factorization [36] (PMF) have revealed to be superior in reducing dimension.  ... 
doi:10.1016/j.ins.2013.08.034 fatcat:lrx7gvofavhotiohm2xfr4rw2i

SoRec

Hao Ma, Haixuan Yang, Michael R. Lyu, Irwin King
2008 Proceeding of the 17th ACM conference on Information and knowledge mining - CIKM '08  
Following the intuition that a person's social network will affect personal behaviors on the Web, this paper proposes a factor analysis approach based on probabilistic matrix factorization to solve the  ...  In view of the exponential growth of information generated by online social networks, social network analysis is becoming important for many Web applications.  ...  The authors appreciate the anonymous reviewers for their extensive and informative comments for the improvement of this paper.  ... 
doi:10.1145/1458082.1458205 dblp:conf/cikm/MaYLK08a fatcat:nyclpnhw25hyzok7xqmsa4cqrm

PRemiSE

Chen Lin, Runquan Xie, Lei Li, Zhenhua Huang, Tao Li
2012 Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12  
In this paper, we investigate the feasibility of integrating content-based methods, collaborative filtering and information diffusion models by employing probabilistic matrix factorization techniques.  ...  A variety of news recommender systems based on different strategies have been proposed to provide news personalization services for online news readers.  ...  Recently, matrix factorization [12] (MF) and probabilistic matrix factorization [20] (PMF) have revealed to be superior than traditional k -nearest neighbor collaborative filtering methods in other  ... 
doi:10.1145/2396761.2398482 dblp:conf/cikm/LinXLHL12 fatcat:lf5fuyax7natljflbq2ydaer6m

Preference Modeling by Exploiting Latent Components of Ratings [article]

Junhua Chen and Wei Zeng and Junming Shao and Ge Fan
2017 arXiv   pre-print
Finally, all latent factor models are combined linearly to estimate predictive ratings for users.  ...  Uncovering the latent components of user ratings is thus of significant importance for learning user interests.  ...  for short) and Bayesian probabilistic matrix factorization (BPMF for short).  ... 
arXiv:1710.07072v1 fatcat:msmpngmxbjd5dfn6aeh6ll5flu
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