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Filters
Neighbor Selection and Weighting in User-Based Collaborative Filtering
2014
ACM Transactions on the Web
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versions of a user-based collaborative filtering algorithm. ...
User-based collaborative filtering systems suggest interesting items to a user relying on similar-minded people called neighbors. ...
A PERFORMANCE PREDICTION FRAMEWORK FOR NEIGHBOR SELECTION AND WEIGHTING
Unifying Neighbor Selection and Weighting in User-Based Collaborative Filtering From the observation that most of the methods for ...
doi:10.1145/2579993
fatcat:w6jpcoqxg5bebcxm2mccu7ioe4
A Performance Prediction Approach to Enhance Collaborative Filtering Performance
[chapter]
2010
Lecture Notes in Computer Science
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A predictor is proposed and introduced in a kNN CF algorithm to produce a dynamic variant where neighbor ratings are weighted based on their predicted performance. ...
We investigate the adaptation of clarity-based query performance predictors to predict neighbor performance in CF. ...
This work was supported by the Spanish Ministry of Science and Innovation (TIN2008-06566-C04-02) and the Ministry of Industry, Tourism and Commerce (CENIT-2007-1012). ...
doi:10.1007/978-3-642-12275-0_34
fatcat:lrhy4g5pffh3fgeaw36rna2zqm
Predicting yield performance of parents in plant breeding: A neural collaborative filtering approach
2020
PLoS ONE
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In this paper, we present a collaborative filtering method which is an ensemble of matrix factorization method and a neural network to solve this problem. ...
were planted in 280 locations between 2016 and 2018 and asked participants to predict the yield performance of cross combinations of inbreds and testers that have not been planted based on the historical ...
Acknowledgments We thank Syngenta and the Analytics Society of INFORMS for organizing the Syngenta Crop Challenge and providing the valuable datasets. ...
doi:10.1371/journal.pone.0233382
pmid:32437473
fatcat:6hfkvlphrbb2heb6eqmq5f4aiu
Next-Term Student Performance Prediction: A Recommender Systems Approach
[article]
2016
arXiv
pre-print
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Application of a novel feature selection technique is key to the predictive success and interpretability of the FM. ...
To further this goal, we develop a system to predict students' grades in the courses they will enroll in during the next enrollment term by learning patterns from historical transcript data coupled with ...
The data for this study was made available through a collaborative effort spearheaded by Office of Institutional Research and Reporting and we would like to acknowledge Kris Smith, Kathryn Zora, Angela ...
arXiv:1604.01840v1
fatcat:cz27yygejjaxxgnioudgksdws4
Next-Term Student Performance Prediction: A Recommender Systems Approach
2016
Zenodo
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Application of a novel feature selection technique is key to the predictive success and interpretability of the FM. ...
To further this goal, we develop a system to predict students' grades in the courses they will enroll in during the next enrollment term by learning patterns from historical transcript data coupled with ...
The data for this study was made available through a collaborative effort spearheaded by Office of Institutional Research and Reporting and we would like to acknowledge Kris Smith, Kathryn Zora, Angela ...
doi:10.5281/zenodo.3554603
fatcat:pumd64tp2nav5ohilnt2zal734
A Learning Fuzzy Cognitive Map (LFCM) Approach to Predict Student Performance
2021
Journal of Information Technology Education
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Aim/Purpose: This research aims to present a brand-new approach for student performance prediction using the Learning Fuzzy Cognitive Map (LFCM) approach. ...
Recommendations for Practitioners: Academic institutions can use the results and approach developed in this paper to identify students' performance antecedents, predict the performance, and establish action ...
CONCLUSION This research proposes a new approach for student performance prediction using the LFCM approach. ...
doi:10.28945/4760
fatcat:75lpwcmivveylnwyhokw6fyhta
Delay-tolerant collaborative filtering
2009
Proceedings of the 7th ACM international symposium on Mobility management and wireless access - MobiWAC '09
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For this purpose, the presented algorithm is based on a delay-tolerant broadcasting mechanism on top of a weighted cluster topology. ...
Recommender systems using collaborative filtering are a wellestablished technique to overcome information overload in today's digital society. ...
An approach to collaborative filtering in a mobile tourist information system for visitors of a festival based on spatiotemporal proximity in social contexts is proposed in [5] . ...
doi:10.1145/1641776.1641795
dblp:conf/mobiwac/GratzL09
fatcat:ovuv5x7rwrhlpk7lazgfjojezq
Collaborative Filtering Algorithm based on Data Mixing and Filtering
2019
International Journal of Performability Engineering
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The recommendations are then made on the populated user-item score matrix through a user-based collaborative filtering approach. ...
In order to solve this problem, an improved collaborative filtering algorithm is proposed, which gathers a variety of single numerical filling methods and selects a more appropriate filling method according ...
Acknowledgements This work is sponsored by the National Natural Science Foundation of China (No. 61662017, 61862019, and 61262075,) , and Guilin Science and Technology Project Fund (No. 2016010408). ...
doi:10.23940/ijpe.19.08.p27.22672276
fatcat:lcsjhuyihveezctc7hm473wuau
Scalable collaborative filtering using cluster-based smoothing
2005
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '05
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Memory-based approaches for collaborative filtering identify the similarity between two users by comparing their ratings on a set of items. ...
In our approach, clusters generated from the training data provide the basis for data smoothing and neighborhood selection. ...
Collaborative Filtering
Memory-based Approaches The memory-based approaches [4] are among the most popular prediction techniques in collaborative filtering. ...
doi:10.1145/1076034.1076056
dblp:conf/sigir/XueLYXZYC05
fatcat:htk5uk33ijbdfafzmk5ptbtuoa
Predicting Neighbor Goodness in Collaborative Filtering
[chapter]
2009
Lecture Notes in Computer Science
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The proposed predictors are introduced in a memory-based CF algorithm to produce a dynamic variant where neighbor ratings are weighted based on their predicted performance. ...
Performance prediction has gained increasing attention in the IR field since the half of the past decade and has become an established research topic in the field. ...
This work was supported by the Spanish Ministry of Science and Innovation (TIN2008-06566-C04-02) and the Ministry of Industry, Tourism and Commerce (CENIT-2007-1012). ...
doi:10.1007/978-3-642-04957-6_52
fatcat:wtz77nq2pbadtl57bbzjw3qdnq
Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System
2015
International Journal of Computer Applications
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This paper provides an overview of recommender systems that include collaborative filtering, content-based filtering and hybrid approach of recommender system. ...
Recommender systems have formulated in parallel with the web. Initially Recommender systems were based on demographic, content-based filtering and collaborative filtering. ...
In collaborative filtering recommendation system recommended objects are selected on the basis of past evaluations of a large group of users. ...
doi:10.5120/19308-0760
fatcat:om2d4qpj4zda7bhb5kxvyo55wm
Hybrid Collaborative Filtering and Content-Based Filtering for Improved Recommender System
[chapter]
2004
Lecture Notes in Computer Science
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Experimental results indicate the hybrid collaborative filtering and content-based filtering better than collaborative, content-based, and combined filtering approach. ...
Hybrid components from collaborative filtering and content-based filtering, a hybrid recommender system can overcome traditional shortcomings. ...
In this paper, we have shown how hybrid collaborative filtering and content-based filtering performs significantly better than collaborative, content-based, and combined filtering approach. ...
doi:10.1007/978-3-540-24685-5_37
fatcat:cvkv35ka5jcjxn6mieqkgeww4e
Item-based collaborative filtering recommendation algorithms
2001
Proceedings of the tenth international conference on World Wide Web - WWW '01
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These systems, especially the k-nearest neighbor collaborative filtering based ones, are achieving widespread success on the Web. ...
Finally, we experimentally evaluate our results and compare them to the basic k-nearest neighbor approach. ...
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
A Robust Predicted Performance Analysis Approach for Data-Driven Product Development in the Industrial Internet of Things
2018
Sensors
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Then a dimension reduction approach based on mutual information (MI) and outlier detection is proposed. ...
A metamodel based on least squares support vector regression (LSSVR) is established to conduct performance prediction process. ...
And then a performance prediction approach based on LSSVR is applied to explore the implicit performance meta-model. ...
doi:10.3390/s18092871
pmid:30200296
pmcid:PMC6164570
fatcat:of7x5ywfyna3bjllewl2k42bhi
A Machine Learning Approach for Tracking and Predicting Student Performance in Degree Programs
2017
IEEE Journal on Selected Topics in Signal Processing
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Although there is a rich literature on predicting student performance when solving problems or studying for courses using data-driven approaches, predicting student performance in completing degrees (e.g ...
First, a bilayered structure comprising of multiple base predictors and a cascade of ensemble predictors is developed for making predictions based on students' evolving performance states. ...
They develop a collaborative filtering algorithm, which is used in recommender systems to recommend items to users based on user similarity, to predict student performance based on student similarity. ...
doi:10.1109/jstsp.2017.2692560
fatcat:xjw6zcb4s5gfbafmlp7ve4aw2y
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