Filters








73,009 Hits in 4.2 sec

List-wise learning to rank with matrix factorization for collaborative filtering

Yue Shi, Martha Larson, Alan Hanjalic
2010 Proceedings of the fourth ACM conference on Recommender systems - RecSys '10  
A ranking approach, ListRank-MF, is proposed for collaborative filtering that combines a list-wise learning-to-rank algorithm with matrix factorization (MF).  ...  of low complexity and is analytically shown to be linear with the number of observed ratings for a given user-item matrix.  ...  ACKNOWLEDGEMENTS The research leading to these results was carried out within the PetaMedia Network of Excellence and has received funding from the European Commission's 7th Framework Program under grant  ... 
doi:10.1145/1864708.1864764 dblp:conf/recsys/ShiLH10 fatcat:k6xrpdtqgbc75aqftf6g47mkwy

LEARNING TO RANK FOR COLLABORATIVE FILTERING
english

2007 Proceedings of the Ninth International Conference on Enterprise Information Systems   unpublished
We suggest different directions to further explore our ranking based approach for collaborative filtering.  ...  Up to now, most contributions to collaborative filtering rely on rating prediction to generate the recommendations. We, instead, try to correctly rank the items according to the users' tastes.  ...  ACKNOWLEDGEMENTS The authors would like to thank Trang Vu for her helpful comments.  ... 
doi:10.5220/0002396301450151 fatcat:m6xl62ve35bxrjvgnksxejzssi

Discovering and learning sensational episodes of news events

Xiang Ao, Ping Luo, Chengkai Li, Fuzhen Zhuang, Qing He
2018 Information Systems  
Experimental results show that the collaborative similarity measure is effective for dataset interlinking, and the learning to rank based framework can significantly increase the performance.  ...  We consider this problem from the perspective of information retrieval in this paper, thus propose a learning to rank based framework, which combines various similarity measures to retrieve the relevant  ...  Specifically, inspired by the idea of collaborative filtering, a simple but effective measure called collaborative similarity is proposed and combined with other measures by a learning to rank framework  ... 
doi:10.1016/j.is.2018.05.003 fatcat:5ehjmfeo6bf33dawhyc3zt4l7q

Discovering and learning sensational episodes of news events

Xiang Ao, Ping Luo, Chengkai Li, Fuzhen Zhuang, Qing He, Zhongzhi Shi
2014 Proceedings of the 23rd International Conference on World Wide Web - WWW '14 Companion  
Experimental results show that the collaborative similarity measure is effective for dataset interlinking, and the learning to rank based framework can significantly increase the performance.  ...  We consider this problem from the perspective of information retrieval in this paper, thus propose a learning to rank based framework, which combines various similarity measures to retrieve the relevant  ...  Specifically, inspired by the idea of collaborative filtering, a simple but effective measure called collaborative similarity is proposed and combined with other measures by a learning to rank framework  ... 
doi:10.1145/2567948.2577290 dblp:conf/www/AoLLZHS14 fatcat:f5zcqnjmnneedalfit2qksaml4

Deep Learning and Collaborative Filtering-Based Methods for Students' Performance Prediction and Course Recommendation

Jinyang Liu, Chuantao Yin, Yuhang Li, Honglu Sun, Hong Zhou, Yinghui Ye
2021 Wireless Communications and Mobile Computing  
In order to help students solve this problem, this paper proposed a hybrid prediction model based on deep learning and collaborative filtering.  ...  First, we use a user-based collaborative filtering model to give a list of recommended courses by calculating the similarity between users.  ...  Then, collaborative filtering is applied to the data cluster to predict the rating for the course. In [40] , collaborative filtering is combined with students' online learning style.  ... 
doi:10.1155/2021/2157343 fatcat:mbj6tghe4fhfhdyzneda36wi2y

Improving Re-ranking of Search Results Using Collaborative Filtering [chapter]

U Rohini, Vamshi Ambati
2006 Lecture Notes in Computer Science  
In this paper we present an effective re-ranking strategy that compensates for the sparsity in a user's profile, by applying collaborative filtering algorithms.  ...  Re-ranking of these results to reflect the most relevant results to the user, using a user profile built from the relevance feedback has proved to provide good results.  ...  Acknowledgements We would like to thank Dr. Vasudeva Varma, for dicussions on collaborative filtering and re ranking of search results.  ... 
doi:10.1007/11880592_16 fatcat:y2fnmk57dzcf7d5cfhjamffmhe

Learning Similarity From Collaborative Filters

Brian McFee, Luke Barrington, Gert R. G. Lanckriet
2010 Zenodo  
Our main contribution in this paper is a method for optimizing content-based similarity by learning from a collaborative filter.  ...  Metric learning to rank Metric Learning to Rank (MLR) [20] is an extension of Structural SVM [13] .  ... 
doi:10.5281/zenodo.1416198 fatcat:52kdtot5prfjpc5kxcjlmqqf4i

Quantitative analysis of Matthew effect and sparsity problem of recommender systems [article]

Hao Wang, Zonghu Wang, Weishi Zhang
2019 arXiv   pre-print
Understanding the underlying mechanism of collaborative filtering is crucial for further optimization.  ...  Collaborative filtering is a simple benchmark ubiquitously adopted in the industry as the baseline for recommender system design.  ...  In future research, we would like to explore quantitative analysis of other recommender system models such as matrix factorization , learning to rank and deep learning.  ... 
arXiv:1909.12798v1 fatcat:2jlkacinpjc5xgglkfo43fqui4

Neural Network-Based Collaborative Filtering for Question Sequencing [article]

Lior Sidi, Hadar Klein
2020 arXiv   pre-print
In this paper, we used the Neural Collaborative Filtering (NCF) model to generate question sequencing and compare it to a pair-wise memory-based question sequencing algorithm - EduRank.  ...  The NCF model showed significantly better ranking results than the EduRank model with an Average precision correlation score of 0.85 compared to 0.8.  ...  Recommendation Systems in E-learning A common approach to generate item rankings is to use Collaborative Filtering (CF) methods.  ... 
arXiv:2004.12212v1 fatcat:zzitx6zl6fevbgpwdur5rj4w7y

Multidirectional Product Support System for Decision Making In Textile Industry Using Collaborative Filtering Methods

Senthil Kumar
2013 IOSR Journal of Computer Engineering  
The proposed algorithm is Multidirectional Rank Prediction (MDRP) decision making algorithm in order to take an effective strategic decision at all the levels of data extraction, used a real time textile  ...  In the information technology ground people are using various tools and software for their official use and personal reasons.  ...  To overcome these situations, identified a technique multidirectional rank prediction similarity collaborative filtering.  ... 
doi:10.9790/0661-1421316 fatcat:gzz4jn7k65ha5p475azdu4t5t4

Towards real-time collaborative filtering for big fast data

Ernesto Diaz-Aviles, Wolfgang Nejdl, Lucas Drumond, Lars Schmidt-Thieme
2013 Proceedings of the 22nd International Conference on World Wide Web - WWW '13 Companion  
However, given the deluge of data items, it is a challenge for individuals to find relevant and appropriately ranked information at the right time.  ...  In this paper, we review our recently proposed online collaborative filtering algorithms and outline potential research directions.  ...  Our online setting for collaborative filtering captures: "what is interesting to me right now within the social media stream", going beyond existing one-size-fits-all solutions.  ... 
doi:10.1145/2487788.2488044 dblp:conf/www/Diaz-AvilesNDS13 fatcat:tqvscm56afhwteilaeuso5djey

Financial Inclusion: An Application of Machine Learning in Collaborative Filtering Recommender Systems

2020 International journal of recent technology and engineering  
For financial inclusion systems, machine learning has become a commonly used method. The result takes into the ATMs, Banks and BCs ranking in different districts of Odisha.  ...  We used the k-Nearest Neighbor's machine learning methodology classification algorithm to characterize the recommendation system based on users of the mentioned populations.  ...  Here we used the data set and construct the layout using the k-NN algorithm for collaborative filtering. We have selected the said data set and, given the weightage of label ranking.  ... 
doi:10.35940/ijrte.f9361.038620 fatcat:zjstpqp5anewnjz3cogk53swlm

Intelligent Digital Learning

Imène Brigui-Chtioui, Philippe Caillou, Elsa Negre
2017 Proceedings of the 9th International Conference on Machine Learning and Computing - ICMLC 2017  
The main idea is to provide them with appropriate support in order to make their learning experience more effective.  ...  In the context of intelligent digital learning, we propose an agentbased recommender system that aims to help learners overcome their gaps by suggesting relevant learning resources.  ...  considering different learners' behavior to better adapt recommendations to different learning styles, (iii) generalizing our results to other types of learning platforms.  ... 
doi:10.1145/3055635.3056592 dblp:conf/icmlc2/Brigui-ChtiouiC17 fatcat:eoxgq4xxhnarhch2uiwcrubpgy

Neural Personalized Ranking via Poisson Factor Model for Item Recommendation

Yonghong Yu, Li Zhang, Can Wang, Rong Gao, Weibin Zhao, Jing Jiang
2019 Complexity  
In this paper, we propose a neural personalized ranking model for collaborative filtering with the implicit frequency feedback.  ...  However, most of existing methods simplify the implicit frequency feedback to binary values, which make collaborative filtering unable to accurately learn the latent user and item features.  ...  (ii) We propose a neural personalized ranking model for collaborative filtering with implicit frequency feedback.  ... 
doi:10.1155/2019/3563674 fatcat:rc4kaow6fzg5dpppucsjdcewsy

Learning Content Similarity for Music Recommendation

Brian McFee, Luke Barrington, Gert Lanckriet
2012 IEEE Transactions on Audio, Speech, and Language Processing  
In this paper, we propose a method for optimizing content-based similarity by learning from a sample of collaborative filter data.  ...  To combat this problem, practitioners rely on content-based similarity, which naturally extends to novel items, but is typically outperformed by collaborative filter methods.  ...  The idea to learn similarity from a collaborative filter follows from a series of positive results in music applications.  ... 
doi:10.1109/tasl.2012.2199109 fatcat:zetnwmr3mrfmlk74sjltngiu4y
« Previous Showing results 1 — 15 out of 73,009 results