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Positive Example Learning for Content-Based Recommendations: A Cost-Sensitive Learning-Based Approach

Yen-Hsien Lee, Paul Jen-Hwa Hu, Tsang-Hsiang Cheng, Ya-Fang Hsieh
2009 International Conference on Information Systems  
To address the limitations inherent in existing single-class learning techniques, we develop COst-sensitive Learning-based Positive Example Learning (COLPEL), which constructs an automated classifier from  ...  Existing supervised learning techniques can support product recommendations but are ineffective in scenarios characterized by single-class learning; i.e., training samples consisted of some positive examples  ...  COst-Sensitive Learning-Based Positive Example Learning Approach (COLPEL) Positive Example-Based Learning Unlabeled Examples Positive Examples Positive Examples Rough Classifier for Strong Negative Identification  ... 
dblp:conf/icis/LeeHCH09 fatcat:gde5up4aarbnbabgiqjhc4ildq

Recommender Systems [chapter]

Prem Melville, Vikas Sindhwani
2017 Encyclopedia of Machine Learning and Data Mining  
. • Content-based recommending: These approaches recommend items that are similar in content to items the user has liked in the past, or matched to pre-defined attributes of the user. • Hybrid approaches  ...  An alternative to IR approaches, is to treat recommending as a classification task, where each example represents the content of an item, and a user's past ratings are used as labels for these examples  ...  ROC Curve The ROC curve is a plot depicting the trade-off between the true positive rate and the false positive rate for a classifier under varying decision thresholds. See ROC Analysis.  ... 
doi:10.1007/978-1-4899-7687-1_964 fatcat:3voghk7xz5cindlgj4pwek7r6u

Application of Statistical Relational Learning to Hybrid Recommendation Systems [article]

Shuo Yang, Mohammed Korayem, Khalifeh AlJadda, Trey Grainger, Sriraam Natarajan
2016 arXiv   pre-print
In this paper, we proposed a way to adapt the state-of-the-art in SRL learning approaches to construct a real hybrid recommendation system.  ...  Statistical Relational Learning (SRL) provides a straightforward way to combine the two approaches.  ...  Cost Sensitive Learning with RFGB Following the work of Yang et al. (Yang et al. 2014) , we propose to construct a hybrid job recommendation system by learning a cost-sensitive RDN.  ... 
arXiv:1607.01050v1 fatcat:psaoxuaklzezjnvyfheefu227m

Robust Cost-Sensitive Learning for Recommendation with Implicit Feedback [article]

Peng Yang, Peilin Zhao, Xin Gao, Yong Liu
2017 arXiv   pre-print
A cost-sensitive learning model is embedded into the framework. Specifically, this model exploits different costs in the loss function for the observed and unobserved instances.  ...  Even though many recommendation approaches are designed based on implicit feedback, they attempt to project the U-I matrix into a low-rank latent space, which is a strict restriction that rarely holds  ...  Recommender Systems Generally speaking, recommender systems can be categorized by two different strategies: content-based approach and collaborative filtering: (1) Existing content-based approaches make  ... 
arXiv:1707.00536v2 fatcat:w5iryo4x4vgh5np62o5gpv6ux4

Ask the GRU

Trapit Bansal, David Belanger, Andrew McCallum
2016 Proceedings of the 10th ACM Conference on Recommender Systems - RecSys '16  
Performance is further improved by multi-task learning, where the text encoder network is trained for a combination of content recommendation and item metadata prediction.  ...  This enables recommendations for new, unseen content, and may generalize better, since the factors for all items are produced by a compactly-parametrized model.  ...  Since the total number of negative examples is much larger than the positive examples for each user, stochastically sampling only one negative per positive example implicitly down-weights the negatives  ... 
doi:10.1145/2959100.2959180 dblp:conf/recsys/BansalBM16 fatcat:gubzk443kvfndf3nsjvhew2q64

Personalized News Recommendation: Methods and Challenges [article]

Chuhan Wu, Fangzhao Wu, Yongfeng Huang, Xing Xie
2022 arXiv   pre-print
We first review the techniques for tackling each core problem in a personalized news recommender system and the challenges they face.  ...  Instead of following the conventional taxonomy of news recommendation methods, in this paper we propose a novel perspective to understand personalized news recommendation based on its core problems and  ...  For example, IGNN [129] learns content-based user representations using the average embedding of clicked news, and learns graph-based user representations from the user-news graph via a graph neural  ... 
arXiv:2106.08934v3 fatcat:iagqsw73hrehxaxpvpydvtr26m

Fairness in Recommendation: A Survey [article]

Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, Juntao Tan, Shuchang Liu, Yongfeng Zhang
2022 arXiv   pre-print
After that, the survey will introduce fairness in recommendation with a focus on the taxonomies of current fairness definitions, the typical techniques for improving fairness, as well as the datasets for  ...  This motivates us to provide a systematic survey of existing works on fairness in recommendation. This survey focuses on the foundations for fairness in recommendation literature.  ...  [186] propose a fairness-aware approach based on decomposed adversarial learning for news recommendation to mitigate the unfairness brought by the biases of user sensitive features.  ... 
arXiv:2205.13619v4 fatcat:t7ycrw3vbjdyjbg53zphru6kbi

Explainable Recommendation: A Survey and New Perspectives [article]

Yongfeng Zhang, Xu Chen
2020 arXiv   pre-print
In recent years, a large number of explainable recommendation approaches -- especially model-based methods -- have been proposed and applied in real-world systems.  ...  In this survey, we provide a comprehensive review for the explainable recommendation research.  ...  Acknowledgements We sincerely thank the reviewers for providing the valuable reviews and constructive suggestions. The work is partially supported by National Science Foundation (IIS-1910154).  ... 
arXiv:1804.11192v10 fatcat:scsd3htz65brbiae35zd3nixe4

A Survey on Trustworthy Recommender Systems [article]

Yingqiang Ge, Shuchang Liu, Zuohui Fu, Juntao Tan, Zelong Li, Shuyuan Xu, Yunqi Li, Yikun Xian, Yongfeng Zhang
2022 arXiv   pre-print
personalization, just to name a few.  ...  All of these create an urgent need for Trustworthy Recommender Systems (TRS) so as to mitigate or avoid such adverse impacts and risks.  ...  [132] propose a content-based filtering system for music recommendation using a decision tree, which enables users to edit the learned profiles on the tree.  ... 
arXiv:2207.12515v1 fatcat:lsnuwdtl5rboznmhhux2n5y5om

Personalized News Recommendation: Methods and Challenges

Chuhan Wu, Fangzhao Wu, Yongfeng Huang, Xing Xie
2022 ACM Transactions on Information Systems  
Instead of following the conventional taxonomy of news recommendation methods, in this paper we propose a novel perspective to understand personalized news recommendation based on its core problems and  ...  We first review the techniques for tackling each core problem in a personalized news recommender system and the challenges they face.  ...  For example, IGNN [161] learns content-based user representations using the average embedding of clicked news, and learns graph-based user representations from the user-news graph via a graph neural  ... 
doi:10.1145/3530257 fatcat:xzghh6cut5ahhgxz4mkzgy74ja

Data Mining Methods for Recommender Systems [chapter]

Xavier Amatriain, Josep M. Pujol
2015 Recommender Systems Handbook  
We also present association rules and related algorithms for an efficient training process.  ...  In this chapter, we give an overview of the main Data Mining techniques used in the context of Recommender Systems.  ...  An explicit example of this is Tiemann and Pauws' music recommender, in which they use ensemble learning methods to combine a social and a content-base RS [70] .  ... 
doi:10.1007/978-1-4899-7637-6_7 fatcat:7iyo7szhjffuxfvpjb5jr4oflu

Multi-Criteria Recommender Systems [chapter]

Gediminas Adomavicius, YoungOk Kwon
2015 Recommender Systems Handbook  
Then, it focuses on the category of multi-criteria rating recommenders -techniques that provide recommendations by modelling a user's utility for an item as a vector of ratings along several criteria.  ...  A review of current algorithms that use multicriteria ratings for calculating predictions and generating recommendations is provided.  ...  Manouselis was funded with support by the European Commission and more specifically, the project ECP-2006-EDU-410012 "Organic.Edunet: A Multilingual Federation of Learning Repositories with Quality Content  ... 
doi:10.1007/978-1-4899-7637-6_25 fatcat:sqvrygjkarci3iugg6dn6n5xym

Real-time top-n recommendation in social streams

Ernesto Diaz-Aviles, Lucas Drumond, Lars Schmidt-Thieme, Wolfgang Nejdl
2012 Proceedings of the sixth ACM conference on Recommender systems - RecSys '12  
Our novel approach follows a selective sampling strategy to perform online model updates based on active learning principles, that closely simulates the task of identifying relevant items from a pool of  ...  RMFX is particularly suitable for large scale applications and experiments on the 476 million Twitter tweets dataset show that our online approach largely outperforms recommendations based on Twitter's  ...  For example, RMFX with a Acknowledgments We would like to thank Zeno Gantner and Stefan Siersdorfer for fruitful discussions during the genesis of this paper.  ... 
doi:10.1145/2365952.2365968 dblp:conf/recsys/Diaz-AvilesDSN12 fatcat:pzemwfmflndn5iqe245p6mwpru

Swarming to rank for recommender systems

Ernesto Diaz-Aviles, Mihai Georgescu, Wolfgang Nejdl
2012 Proceedings of the sixth ACM conference on Recommender systems - RecSys '12  
Our experimental evaluation, on a real world online radio dataset, indicates that our approach is able to find ranking functions that significantly improve the performance of the system for the Top-N recommendation  ...  Our approach, which is well-founded in a Particle Swarm Optimization framework, learns a ranking function by optimizing the combination of unique characteristics (i.e., features) of users, items and their  ...  Technische Hochschule) for IT Ecosystems.  ... 
doi:10.1145/2365952.2366001 dblp:conf/recsys/Diaz-AvilesGN12 fatcat:zrpo7ubrvfaudieamg6zv4hkua

Robust Cost-Sensitive Learning for Recommendation with Implicit Feedback [chapter]

Peng Yang, Peilin Zhao, Yong Liu, Xin Gao
2018 Proceedings of the 2018 SIAM International Conference on Data Mining  
To minimize the asymmetric cost of error from different classes, a cost-sensitive learning model is embedded into the framework.  ...  Even though many recommendation approaches are designed based on implicit feedback, they attempt to project the U-I matrix into a low-rank latent space, which is a strict restriction that rarely holds  ...  Recommender Systems Generally speaking, recommender systems can be categorized by two different strategies: content-based approach and collaborative filtering: (1) Existing content-based approaches make  ... 
doi:10.1137/1.9781611975321.70 dblp:conf/sdm/YangZLG18 fatcat:fwlvzgwo4vgzlaygbwlgy4cdg4
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