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Filters
Selecting relevant instances for efficient and accurate collaborative filtering
2001
Proceedings of the tenth international conference on Information and knowledge management - CIKM'01
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The proposed method reduces the training data set by selecting only highly relevant instances. ...
We introduce an information theoretic approach to measure the relevance of a consumer (instance) for predicting the preference for the given product (target concept). ...
A novel approach is proposed to actively select relevant instances (customers) for training to dramatically improve the efficiency and accuracy of memory-based collaborative filtering. ...
doi:10.1145/502624.502626
fatcat:likqbdge7zfhvbfof5wfet67cq
Selecting relevant instances for efficient and accurate collaborative filtering
2001
Proceedings of the tenth international conference on Information and knowledge management - CIKM'01
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The proposed method reduces the training data set by selecting only highly relevant instances. ...
We introduce an information theoretic approach to measure the relevance of a consumer (instance) for predicting the preference for the given product (target concept). ...
A novel approach is proposed to actively select relevant instances (customers) for training to dramatically improve the efficiency and accuracy of memory-based collaborative filtering. ...
doi:10.1145/502585.502626
dblp:conf/cikm/YuXEK01
fatcat:uyjubupwc5aehok2dhaelf4rfm
Similarity measure and instance selection for collaborative filtering
2003
Proceedings of the twelfth international conference on World Wide Web - WWW '03
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We adopt two techniques: a matrix conversion method for similarity measure and an instance selection method. ...
Collaborative filtering has been very successful in both research and applications such as information filtering and E-commerce. ...
The practical method is instance selection that selects appropriate instances from the whole database for the filtering algorithm. ...
doi:10.1145/775152.775243
dblp:conf/www/ZengXZ03
fatcat:krwnszkrb5ftjh3nqlpa4453be
Ensemble Learning Based Collaborative Filtering with Instance Selection and Enhanced Clustering
2022
Computers Materials & Continua
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This paper addresses sparsity, and scalability problems of model-based collaborative recommender system based on ensemble learning approach and enhanced clustering algorithm for movie recommendations. ...
The proposed model obtained 0.52 and 0.57 MAE value on Movielens 100k and 1M datasets. ...
Hence the proposed approach adopts an instance selection strategy to filter the relevant users than searching for the entire data set. ...
doi:10.32604/cmc.2022.019805
fatcat:yuhoivk6rnacfkomoj4cdikeuy
Social Plane for Recommenders in Network Performance Expectation Management
2018
IEEE Transactions on Network and Service Management
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The lack of a platform for sharing knowledge and working collaboratively makes it difficult to isolate and diagnose network-wide anomaly events quickly and accurately. ...
Our experimental results show that our measurements recommendation scheme has high precision, recall and accuracy, as well as efficiency in terms of the time taken for large volume measurement trace analysis ...
In addition, we showed how our collaborative filtering scheme finds similar anomaly issues efficiently and effectively. ...
doi:10.1109/tnsm.2017.2772905
fatcat:txgn2hn5ofcg3ip5uagut36ana
Personal Recommender System Based on Agglomerative Clustering together with User-based and Item-based Collaborative Filtering Methods
2020
Journal of Software
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Therefore, new techniques have been introduced and integrated with the recommender system in order to solve the problems and improve for greater recommender system efficiency. ...
Collaborative Filtering or Item-based Collaborative Filtering alone. ...
Then used as an experiment for a movie recommender system for users to get effective and accurate recommendations in an efficient time. ...
doi:10.17706/jsw.15.6.163-171
fatcat:p3bpr4jpnrbr7pbynpp3ft4bda
Instance Selection Techniques for Memory-Based Collaborative Filtering
[chapter]
2002
Proceedings of the 2002 SIAM International Conference on Data Mining
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Collaborative filtering (CF) has become an important data mining technique to make personalized recommendations for books, web pages or movies, etc. ...
The key idea is to generate prediction from a carefully selected set of relevant instances. We evaluate the techniques on the well-known EachMovie data set. ...
Conclusions and Future Work In this paper we have presented four novel instance selection methods, TURF1 -TURF4, to meet the challenge of efficiency for the widely used memory-based collaborative filtering ...
doi:10.1137/1.9781611972726.4
dblp:conf/sdm/YuXTEK02
fatcat:zhy5gl53prbvjhobfqwvjvdmce
Feature and Instance Joint Selection: A Reinforcement Learning Perspective
[article]
2022
arXiv
pre-print
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In addition, an interactive paradigm introduces prior selection knowledge to help agents for more efficient exploration. ...
Feature selection and instance selection are two important techniques of data processing. ...
., classical feature selection and instance filtering methods) to guide feature agent and instance agent to improve their learning efficiency. ...
arXiv:2205.07867v1
fatcat:ymyhacnjijhptiw6lebkncbi2q
Course Recommendation System
2020
International Journal of Computer Applications
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This work focuses on building an effective Course Recommendation System (CRS) for college students, suggesting the most relevant course based on their learning ability and their preferred choice. ...
Existing course recommendation systems suggest courses based on either collaborative or content based approach. ...
The author suggested a hybrid method using content based, collaborative filtering, and rule based filtering and Demographic based system. ...
doi:10.5120/ijca2020920823
fatcat:cwqvyv5ianbcfite7q3hdshnlm
User Profiling - A Short Review
2014
International Journal of Computer Applications
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This paper aims to give an overview on the user profiling and its related concepts, and discuss the pros and cons of current methods for the future service personalization. ...
Different methods, techniques and algorithms have been proposed in the literature for the user profiling process. ...
For instance, users book interest information may not be as relevant as income information of the user for personalized restaurant recommendations. ...
doi:10.5120/18888-0179
fatcat:pamvqbjoi5frpeueezuawksq5i
Institutional Research Management using an Integrated Information System
2013
Procedia - Social and Behavioral Sciences
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The system provides decision support mechanisms using graph metrics in combination with data envelopment analysis as a method for efficiency measurement. ...
Research constitutes a fundamental activity within Higher Education and, for many institutions, comprises a major revenue income stream. ...
performance in comparison with the selected instances. ...
doi:10.1016/j.sbspro.2013.02.085
fatcat:dktwlusbzvazfboxli6gkafoxi
Recommendation systems: Principles, methods and evaluation
2015
Egyptian Informatics Journal
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On the Internet, where the number of choices is overwhelming, there is need to filter, prioritize and efficiently deliver relevant information in order to alleviate the problem of information overload, ...
This paper explores the different characteristics and potentials of different prediction techniques in recommendation systems in order to serve as a compass for research and practice in the field of recommendation ...
Accurate models are indispensable for obtaining relevant and accurate recommendations from any prediction techniques. ...
doi:10.1016/j.eij.2015.06.005
fatcat:arp4euyhifhvppxf6z46rcuyqu
Personalized Recommender by Exploiting Domain based Expert for Enhancing Collaborative Filtering Algorithm :PReC
2019
International Journal of Advanced Computer Science and Applications
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Collaborative filtering is one of the most traditional and intensively used recommendation approaches for many commercial services. ...
Personalized Expert based collaborative filtering (PReC) approach is proposed to identify domain specific experts and the use of experts preference enhanced the performance of collaborative filtering recommender ...
The experiments proved that our proposed algorithms generate efficient and accurate predictions when compared to existing traditional User based Collaborative Filtering. ...
doi:10.14569/ijacsa.2019.0100313
fatcat:j54k25smrrgwjo56agrtefwodm
A Case Study on Various Recommendation Systems
2016
International Journal of Computer Applications
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It is an information filtering technique that assists users by filtering the redundant and unwanted data from a data chunk and delivers relevant information to the users. ...
The goal of a recommender system is to generate relevant recommendations for users. ...
Architecture of Recommender systems There are mainly two approaches for the information filtering in recommendation systems: Content-based filtering and collaborative filtering. ...
doi:10.5120/ijca2016908149
fatcat:zgysn7g3brcqrm7hthobupzhp4
How recommender systems could support and enhance computer-tailored digital health programs: A scoping review
2019
Digital Health
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Titles and abstracts of 1184 studies were screened for the full-text screening, in which two reviewers independently selected articles and systematically extracted data using a predefined extraction form ...
Incorporating a collaborative method with demographic filtering as a second step to knowledge-based filtering could potentially add value to traditional tailoring with regard to enhancing the user experience ...
Acknowledgements: We are indebted to Rixt Zijlstra for her valuable feedback on the first draft of the manuscript, and to Santiago Hors-Fraile for his input regarding the RS approaches. ...
doi:10.1177/2055207618824727
pmid:30800414
pmcid:PMC6379797
fatcat:g5sflknhmrasbexrmlqei7iusa
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