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Neighbor Selection and Weighting in User-Based Collaborative Filtering

Alejandro Bellogín, Pablo Castells, Iván Cantador
2014 ACM Transactions on the Web  
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]

Alejandro Bellogín, Pablo Castells
2010 Lecture Notes in Computer Science  
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

Saeed Khaki, Zahra Khalilzadeh, Lizhi Wang, Le Hoang Son
2020 PLoS ONE  
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]

Mack Sweeney, Huzefa Rangwala, Jaime Lester, Aditya Johri
2016 arXiv   pre-print
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

Mack Sweeney, Jaime Lester, Huzefa Rangwala, Aditya Johri
2016 Zenodo  
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

Taha Mansouri, Ahad ZareRavasan, Amir Ashrafi
2021 Journal of Information Technology Education  
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

Patrick Gratz, Tom Leclerc
2009 Proceedings of the 7th ACM international symposium on Mobility management and wireless access - MobiWAC '09  
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

Cheng Xiaohui, Feng Li, Gui Qiong
2019 International Journal of Performability Engineering  
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

Gui-Rong Xue, Chenxi Lin, Qiang Yang, WenSi Xi, Hua-Jun Zeng, Yong Yu, Zheng Chen
2005 Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '05  
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]

Alejandro Bellogín, Pablo Castells
2009 Lecture Notes in Computer Science  
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

Poonam B.Thorat, R. M. Goudar, Sunita Barve
2015 International Journal of Computer Applications  
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]

Kyung-Yong Jung, Dong-Hyun Park, Jung-Hyun Lee
2004 Lecture Notes in Computer Science  
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

Badrul Sarwar, George Karypis, Joseph Konstan, John Reidl
2001 Proceedings of the tenth international conference on World Wide Web - WWW '01  
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

Hao Zheng, Yixiong Feng, Yicong Gao, Jianrong Tan
2018 Sensors  
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

Jie Xu, Kyeong Ho Moon, Mihaela van der Schaar
2017 IEEE Journal on Selected Topics in Signal Processing  
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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