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Personalized Tag Recommendation through Nonlinear Tensor Factorization Using Gaussian Kernel
2015
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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On the other hand, mod- els based on Canonical Decomposition can run in linear time and are more feasible for online recommendation. ...
Models based on Tucker Decomposition can achieve good performance but require a lot of computation power. ...
Acknowledgements We would like to thank the many referees of the previous version of this paper for their extremely useful suggestions and comments. ...
doi:10.1609/aaai.v29i1.9214
fatcat:zt3opneabne27bwf2fmbqz2jmm
Regularising Factorised Models for Venue Recommendation using Friends and their Comments
2016
Proceedings of the 25th ACM International on Conference on Information and Knowledge Management - CIKM '16
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similar venues based on their comments. ...
This paper argues for a combined regularisation model, where the venues suggested for a user are influenced by friends with similar tastes (as defined by their comments). ...
: R ≈ U T V Next, the two latent factor matrices U and V are optimised by minimising the regularised square error on a set of observed ratings using decomposition technique such as singular value decomposition ...
doi:10.1145/2983323.2983889
dblp:conf/cikm/ManotumruksaMO16
fatcat:3ipafngbwrdjvnuppobfv3mfu4
Connecting comments and tags
2013
Proceedings of the sixth ACM international conference on Web search and data mining - WSDM '13
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However, previous methods typically model user behavior based only on a log of prior tags, neglecting other behaviors and information in social tagging systems, e.g., commenting on items and connecting ...
We tackle these problems by using a generalized latent factor model and fully Bayesian treatment. To evaluate performance, we test on two real-world data sets from Flickr and Bibsonomy. ...
[33] propose a method based on Higher-Order-Singular-Value-Decomposition (HOSVD), which corresponds to a Tucker Decomposition (TD) model optimized for square-loss where all not observed values are learned ...
doi:10.1145/2433396.2433466
dblp:conf/wsdm/YinGC0AB13
fatcat:t7u7hvft5fapdkp5ugupps7wki
Recommender System for China Braille Library
2017
DEStech Transactions on Computer Science and Engineering
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We have designed a recommender system for China Braille Library to aid the disabled person getting personalized recommending books. ...
By this way, the effect of information overload is largely reduced for visually impaired persons. ...
To alleviate the sparsity problem, many matrix factorization (MF) models [5,6] are adopted, such as Singular Value Decomposition (SVD), Non-negative Matrix Factorization (NMF), Maximum Margin Matrix factorization ...
doi:10.12783/dtcse/aics2016/8218
fatcat:wwrbhibnw5bw7cfra2pwn4uvxm
EigenRec: generalizing PureSVD for effective and efficient top-N recommendations
2018
Knowledge and Information Systems
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2017 pre-print arXiv:1511.06033v2 fatcat:pfsiz2hq6jg5nkd4qpxllsbiqeOther Versions
We introduce EigenRec; a versatile and efficient Latent-Factor framework for Top-N Recommendations that includes the well-known PureSVD algorithm as a special case. ...
A comprehensive set of experiments on the MovieLens and the Yahoo datasets based on widely applied performance metrics, indicate that EigenRec outperforms several state-of-the-art algorithms, in terms ...
truncated singular value decomposition on the rating matrix. ...
doi:10.1007/s10115-018-1197-7
fatcat:iay6j5r755hahlha6ewchurl5e
Improved Recommendation System Using Friend Relationship in SNS
[chapter]
2015
Lecture Notes in Computer Science
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Because of massive users, the potential commercial value of Chinese SNS is still a great mining space. However, a relatively large defects is the precipitation and accumulation on content. ...
We have improved the existing models, and conduct experiments to validate it and compare it with previous methods. ...
Deerwester proposed using singular value decomposition (SVD) technique to find potential factors in the document. ...
doi:10.1007/978-3-662-49017-4_2
fatcat:eyxlsdkytfcfdoenktmeyrhawe
Item cold-start recommendations
2014
Proceedings of the 8th ACM Conference on Recommender systems - RecSys '14
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We present a learning algorithm based on multiplicative update rules that are efficient and easy to implement. ...
six state-of-theart methods for item cold-start. ...
Many collaborative filtering systems approximate the collaborative matrix by applying techniques such as Singular Value Decomposition (SVD) or UV decomposition [20] . ...
doi:10.1145/2645710.2645751
dblp:conf/recsys/SaveskiM14
fatcat:2kloqgxcevgstpiy2r6f6vycsy
Is Simple Better? Revisiting Non-Linear Matrix Factorization for Learning Incomplete Ratings
2018
2018 IEEE International Conference on Data Mining Workshops (ICDMW)
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2017 pre-print arXiv:1710.05613v2 fatcat:pqknabkxora3blhufkgp6nrwd4Other Versions
Matrix factorization techniques have been widely used as a method for collaborative filtering for recommender systems. ...
Firstly, we learn latent factors for representations of users and items from the designed multilayer nonlinear Semi-NMF approach using explicit ratings. ...
ACKNOWLEDGMENT The authors gratefully acknowledge Dijana Tolic for useful directions and comments regarding NMF and Deep Semi-NMF approach. ...
doi:10.1109/icdmw.2018.00183
dblp:conf/icdm/KrishnaGA18
fatcat:fpimimjpufa33bpyywkf33moxu
Attributes Coupling based Item Enhanced Matrix Factorization Technique for Recommender Systems
[article]
2014
arXiv
pre-print
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Recently, based on the intuition that additional information provides useful insights for matrix factorization techniques, several recommendation algorithms have utilized additional information to improve ...
Matrix factorization technique is one of the most widely employed collaborative filtering techniques in the research of recommender systems due to its effectiveness and efficiency in dealing with very ...
ACKNOWLEDGMENTS The authors would like to thank the anonymous referees and the editor for their helpful comments and suggestions. ...
arXiv:1405.0770v1
fatcat:oqz3oxxlffctfg5ybflyif2hmy
ConvSVD++: A Hybrid Deep CF Recommender Model using Convolutional Neural Network
2020
Journal of Computer Science
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It proposes a hybrid deep CF recommender model called ConvSVD++ that tightly integrates Convolution Neural Network (CNN) and Singular Value Decomposition (SVD++). ...
The proposed model is evaluated and all baseline models based on Movielens-1M datasets. ...
Acknowledgment The author(s) recieved no specific funding for this work.
Author's Contributions Mohamed Grida: Designed the research plan, coordinated the data analysis and organized the study. ...
doi:10.3844/jcssp.2020.1697.1708
fatcat:s4wifrdvuzghbpfccnvsyc5equ
A novel collaborative filtering model based on combination of correlation method with matrix completion technique
2012
The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012)
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This challenge leads to a decrease in the accuracy level of recommendations for new users. The exact matrix completion technique tries to predict unknown values in data matrices. ...
These systems rely on historical rating data and preferences of users and items in order to propose appropriate recommendations for active users. ...
Besides, the operation of the currently proposed approach in hybrid personalized recommender systems might be investigated in the future. ...
doi:10.1109/aisp.2012.6313742
fatcat:c7h6mahtxvaz7nv3gcvsukhlxu
Tensors for Data Mining and Data Fusion
2016
ACM Transactions on Intelligent Systems and Technology
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Tensors and tensor decompositions are very powerful and versatile tools that can model a wide variety of heterogeneous, multiaspect data. ...
As a result, tensor decompositions, which extract useful latent information out of multiaspect data tensors, have witnessed increasing popularity and adoption by the data mining community. ...
Kolda for comments on earlier versions of this manuscript and identifying several additional references. The majority of this work was carried out while E. ...
doi:10.1145/2915921
fatcat:annpad5w2jcvnb4d3e5imiemlu
Personalized POI Recommendation Based on Subway Network Features and Users' Historical Behaviors
2018
Wireless Communications and Mobile Computing
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Current recommender systems often take fusion factors into consideration to realize personalize point-of-interest (POI) recommendation. ...
Historical behavior records and location factors are two kinds of significant features in most of recommendation scenarios. ...
It contains a large number of users, restaurants, and ratings for restaurant. ...
doi:10.1155/2018/3698198
fatcat:lpptebh4c5ey3hkloawtnajzd4
Special issue on advances in web intelligence
2012
Neurocomputing
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best papers from Web Intelligence 2010 and gave valuable feedback on the extended work in the papers of this special issue within a very short time-frame; the journal staff for a professional production ...
Acknowledgements The guest editors wish to thank the Editor-in-Chief Tom Heskes for giving us the opportunity to present the Advances in Web Intelligence; the reviewers, who have helped to select the very ...
and singular value decomposition. ...
doi:10.1016/j.neucom.2011.07.006
fatcat:mkonlpjpbrcothduyrsg677o5u
Deep Collaborative Filtering: A Recommendation Method for Crowdfunding Project Based on the Integration of Deep Neural Network and Collaborative Filtering
2022
Mathematical Problems in Engineering
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collaborative filtering algorithm for modeling the linear interaction of users and items and combines the two methods for recommendation. ...
In real recommendation systems, implicit feedback data is more common and easier to obtain, and recommendation algorithms based on such data will be more applicable. ...
For the sake of reducing the impact of sparse data on recommendation, the matrix decomposition technology represented by Singular Value Decomposition (SVD) decomposed the user's rating of the item into ...
doi:10.1155/2022/4655030
fatcat:6nadmi32g5hrzaurn3bukqazzi
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