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Collaborative Tag Recommendations [chapter]

Leandro Balby Marinho, Lars Schmidt-Thieme
2008 Studies in Classification, Data Analysis, and Knowledge Organization  
Being the scenario similar to traditional recommender systems where nearest neighbor algorithms, better known as collaborative filtering, were extensively and successfully applied, the application of the  ...  comparing different tag recommender algorithms on real data.  ...  Acknowledgments This work is supported by CNPq, an institution of Brazilian Government for scientific and technologic development.  ... 
doi:10.1007/978-3-540-78246-9_63 fatcat:7o6y756fifhmlg3bio3awdy7bi

Networked Collaborative Recommendation Architecture

Ville Ollikainen
2019 Zenodo  
The technology presented in this paper enables a recommendation engine network, in which all parties own and are able to administer their own data, challenging centralized models of today.  ...  A major challenge in recommendation systems is that they are either domain-specific or need a substantial amount of data.  ...  Therefore, advertising is a self-evident application area for recommendations. Recommenders are commonly classified into two basic categories: content-based and collaborative.  ... 
doi:10.5281/zenodo.3891006 fatcat:lbybgreztzezdaaap7i3yttuvq

Collaborative Filtering Recommender Systems

Michael D. Ekstrand
2011 Foundations and Trends® in Human–Computer Interaction  
and current best practices for addressing these issues.  ...  Nearly two decades of research on collaborative filtering have led to a varied set of algorithms and a rich collection of tools for evaluating their performance.  ...  A recommender system must interact with the user, both to learn the user's preferences and provide recommendations; these concerns pose challenges for user interface and interaction design.  ... 
doi:10.1561/1100000009 fatcat:34jiby3txreetnp2n4ew5q7swu

Neural Hybrid Recommender: Recommendation needs collaboration [article]

Ezgi Yıldırım, Payam Azad, Şule Gündüz Öğüdücü
2019 arXiv   pre-print
After its rising success on these challenging areas, it has been studied on recommender systems as well, but mostly to include content features into traditional methods.  ...  This framework named NHR, short for Neural Hybrid Recommender allows us to include more elaborate information from the same and different data sources.  ...  Acknowledgements This study is part of the research project (Project No:5170032) supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK).  ... 
arXiv:1909.13330v1 fatcat:a3z2hdzdovgfnmaebovus5ymui

Cross-domain collaboration recommendation

Jie Tang, Sen Wu, Jimeng Sun, Hang Su
2012 Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '12  
We propose the Cross-domain Topic Learning (CTL) model to address these challenges.  ...  : cross-domain collaborators often have different expertise and interest; 3) topic skewness: cross-domain collaboration topics are focused on a subset of topics.  ...  Prototype System We have developed and deployed a web application for crossdomain recommendation based on the proposed CTL method 5 .  ... 
doi:10.1145/2339530.2339730 dblp:conf/kdd/TangWSS12 fatcat:xv7kgpqrl5havjla5qshlzswoa

Improved recommendations via (more) collaboration

Rubi Boim, Haim Kaplan, Tova Milo, Ronitt Rubinfeld
2010 Procceedings of the 13th International Workshop on the Web and Databases - WebDB '10  
To substantiate this claim, we present C2F (Collaborative CF), a recommender system that retains the simplicity and efficiency of classical CF, while allowing distinct organizations to collaborate and  ...  We consider in this paper a popular class of recommender systems that are based on Collaborative Filtering (CF for short).  ...  organizations to collaborate and boost their recommendations.  ... 
doi:10.1145/1859127.1859143 dblp:conf/webdb/BoimKMR10 fatcat:2yuo3gls7vgt5bwcxsicvh4coe

Item-based collaborative filtering recommendation algorithms

Badrul Sarwar, George Karypis, Joseph Konstan, John Reidl
2001 Proceedings of the tenth international conference on World Wide Web - WWW '01  
The tremendous growth in the amount of available information and the number of visitors to Web sites in recent years poses some key challenges for recommender systems.  ...  In this paper we analyze different item-based recommendation generation algorithms.  ...  Acknowledgments Funding for this research was provided in part by the National Science Foundation under grants IIS 9613960, IIS 9734442, and IIS 9978717 with additional funding by Net Perceptions Inc.  ... 
doi:10.1145/371920.372071 dblp:conf/www/SarwarKKR01 fatcat:qd6ygbmmsvg2hp7skfg4rrwr6u

Evaluating collaborative filtering recommender systems

Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, John T. Riedl
2004 ACM Transactions on Information Systems  
One of the most successful technologies for recommender systems, called collaborative filtering, has been developed and improved over the past decade to the point where a wide variety of algorithms exist  ...  Clearly identifying the best algorithm for a given purpose has proven challenging, in part because researchers disagree on which attributes should be measured, and on which metrics should be used for each  ...  We'd also like to thank our many colleagues in the recommender systems community with whom we've had fruitful discussions over the years.  ... 
doi:10.1145/963770.963772 fatcat:zr4ridbllzgarj2ifehe7ehgwe

Recommender Systems and Collaborative Filtering

Fernando Ortega, Ángel González-Prieto
2020 Applied Sciences  
Recommender Systems (RSs) have become an essential tool for the information society [...]  ...  and relevant recommendations.  ...  Conclusions In this Special Issue we have pushed the boundary of knowledge in CF-based RSs both from a theoretical and a practical viewpoint.  ... 
doi:10.3390/app10207050 fatcat:xygbadmqdbhcfmv2vevtmm3uwi

Collaborative Nowcasting for Contextual Recommendation

Yu Sun, Nicholas Jing Yuan, Xing Xie, Kieran McDonald, Rui Zhang
2016 Proceedings of the 25th International Conference on World Wide Web - WWW '16  
Inspired by the nowcasting practice in meteorology and macroeconomics, we propose an innovative collaborative nowcasting model to effectively resolve these challenges.  ...  Traditional recommendation models cannot directly apply to proactive experiences because they fail to tackle the above challenges.  ...  In practice, there can be hundreds of series in a panel and the monitoring granularity can range from minutes to hours depending on the application.  ... 
doi:10.1145/2872427.2874812 dblp:conf/www/SunYXMZ16 fatcat:t7mzjjxhrnbptk57ebfzfvzxr4

An Improved Collaborative Filtering Recommendation Algorithm and Recommendation Strategy

Xiaofeng Li, Dong Li
2019 Mobile Information Systems  
The collaborative filtering recommendation technology is a successful application of personalized recommendation technology.  ...  The experimental results show that the improved collaborative filtering algorithm is superior to other two collaborative recommendation algorithms for MAE and RMSE performance.  ...  recommendation system to solve the problems of new users and new items [4] . e collaborative filtering recommendation technique has both practical values and shortcomings.  ... 
doi:10.1155/2019/3560968 fatcat:f56ahm6ozfhobexp6aysgroy44

Collaborative future event recommendation

Einat Minkov, Ben Charrow, Jonathan Ledlie, Seth Teller, Tommi Jaakkola
2010 Proceedings of the 19th ACM international conference on Information and knowledge management - CIKM '10  
Because direct feedback does not exist for events that have not taken place, we recommend them based on individuals' preferences for past events, combined collaboratively with other peoples' likes and  ...  We show that the collaborative ranking predictions of future events are more effective than pure content-based recommendation.  ...  Overall, our results show that it is possible to achieve good results on the task of scientific talk recommendation, which may be sufficient for practical applications.  ... 
doi:10.1145/1871437.1871542 dblp:conf/cikm/MinkovCLTJ10 fatcat:tt6dur4rcvdllleqwoumaivyyy

Personal recommendation via modified collaborative filtering

Run-Ran Liu, Chun-Xiao Jia, Tao Zhou, Duo Sun, Bing-Hong Wang
2009 Physica A: Statistical Mechanics and its Applications  
Based on a benchmark database, we demonstrate the great improvement of algorithmic accuracy for both user-based MCF and object-based MCF.  ...  Substituting this new definition of similarity for the standard Person correlation, we propose a modified collaborative filtering (MCF).  ...  Jianguo Liu for valuable discussion and GroupLens Research Group for providing us the data set MovieLens.  ... 
doi:10.1016/j.physa.2008.10.010 fatcat:lriwxmbdejdovijhpvhymlybcq

Collaborative Recommendation of Photo-Taking Geolocations

Thomas Phan, Jiayu Zhou, Shiyu Chang, Junling Hu, Juhan Lee
2014 Proceedings of the 3rd ACM Multimedia Workshop on Geotagging and Its Applications in Multimedia - GeoMM '14  
We apply collaborative recommendation algorithms to photography in order to produce personalized suggestions for locations in the geocoordinate space where mobile users can take photos.  ...  four different algorithms (one memorybased and three model-based).  ...  Recommendation Model Recommender algorithms usually fall into one of two categories [15] : content-based and collaborative filtering.  ... 
doi:10.1145/2661118.2661121 dblp:conf/mm/PhanZCHL14 fatcat:3mkuvzkqv5hhzfqf6i7xt2dece recommendations: item-to-item collaborative filtering

G. Linden, B. Smith, J. York
2003 IEEE Internet Computing  
E-commerce recommendation algorithms often operate in a challenging environment.  ...  Recommendation Algorithms Most recommendation algorithms start by finding a set of customers whose purchased and rated items overlap the user's purchased and rated items. 2 The algorithm aggregates items  ...  Unlike other algorithms, item-to-item collaborative filtering is able to meet this challenge.  ... 
doi:10.1109/mic.2003.1167344 fatcat:wdzvpfw7xzekvox7ikcmwlwlwq
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