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A Comparative Evaluation of Top-N Recommendation Algorithms: Case Study with Total Customers

Idir Benouaret, Sihem Amer-Yahia
2020 2020 IEEE International Conference on Big Data (Big Data)  
In this experiments and analyses paper, we present an extensive experimental evaluation of various top-N collaborative filtering recommendation algorithms based on a real-world dataset of customer's purchase  ...  Our study aims to compare representative collaborative filtering approaches in practice and study the ones yielding the highest recommendation accuracy, with respect to wellestablished evaluation measures  ...  Evaluation Metrics For all test customers each algorithm outputs a sorted list of top-N products.  ... 
doi:10.1109/bigdata50022.2020.9378404 fatcat:2aiocbbsybb7hpuk3643xqdbqy

Item-based top-N recommendation algorithms

Mukund Deshpande, George Karypis
2004 ACM Transactions on Information Systems  
and provide recommendations with comparable or better quality.  ...  The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems-a personalized information filtering technology used to identify a set of N  ...  Experimental Results In this section we experimentally evaluate the performance of the item-based top-N recommendation algorithms and compare it against the performance of the user-based top-N recommendation  ... 
doi:10.1145/963770.963776 fatcat:mxq7xkpsyfcxjpoqei6tr7wzdu

Evaluation of Item-Based Top-N Recommendation Algorithms

George Karypis
2001 Proceedings of the tenth international conference on Information and knowledge management - CIKM'01  
The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems-a personalized information filtering technology used to identify a set of N  ...  Unfortunately, the computational complexity of these methods grows linearly with the number of customers that in typical commercial applications can grow to be several millions.  ...  Experimental Results In this section we experimentally evaluate the performance of the item-based top-N recommendation algorithms and compare it against the performance of the user-based top-N recommendation  ... 
doi:10.1145/502624.502627 fatcat:cxrewmdgzfgovjo2mhgjpjqax4

Evaluation of Item-Based Top-N Recommendation Algorithms

George Karypis
2001 Proceedings of the tenth international conference on Information and knowledge management - CIKM'01  
The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems-a personalized information filtering technology used to identify a set of N  ...  Unfortunately, the computational complexity of these methods grows linearly with the number of customers that in typical commercial applications can grow to be several millions.  ...  Experimental Results In this section we experimentally evaluate the performance of the item-based top-N recommendation algorithms and compare it against the performance of the user-based top-N recommendation  ... 
doi:10.1145/502585.502627 dblp:conf/cikm/Karypis01 fatcat:zdffxs544jaczjwu62uzdiq6dy

Do Metrics Make Recommender Algorithms?

Elica Campochiaro, Riccardo Casatta, Paolo Cremonesi, Roberto Turrin
2009 2009 International Conference on Advanced Information Networking and Applications Workshops  
We show, with case studies, that different evaluation methodologies lead to totally contrasting conclusions about the quality of recommendations.  ...  top-N recommendation task).  ...  This is known as the top-N recommendation task. With this work we want to make a critical analysis of different evaluation techniques for recommender systems.  ... 
doi:10.1109/waina.2009.127 dblp:conf/aina/CampochiaroCCT09 fatcat:rn7f3425dzccdjsldu2gl4nwc4

Intelligent Collaborative Recommender System by Crow Search Algorithm and K-Means algorithm

2019 International journal of recent technology and engineering  
In this study, we have developed a collaborative movie recommender system using crow search and K-means algorithm.  ...  Movie suggestion frameworks give a system to help customers in arranging customers with practically identical interests.  ...  of data objects Xi, I = 1, 2, 3 …N with D number of movie types as a features.  ... 
doi:10.35940/ijrte.b2950.078219 fatcat:fqfukcshyzdr5fs3hdz5bkcyv4

recommenderlab: An R Framework for Developing and Testing Recommendation Algorithms [article]

Michael Hahsler
2022 arXiv   pre-print
Recommender systems have a significant research community, and studying such systems is part of most modern data science curricula.  ...  While there is an abundance of software that implements recommendation algorithms, there is little in terms of supporting recommender system research and education.  ...  R> plot(results, "prec/rec", annotate=TRUE) Comparing recommender algorithms Comparing top-N recommendations The comparison of several recommender algorithms is one of the main functions of recommenderlab  ... 
arXiv:2205.12371v1 fatcat:3wcjwpwcynantlowmm3dmxnwti

Beyond evolutionary algorithms for search-based software engineering

Jianfeng Chen, Vivek Nair, Tim Menzies
2018 Information and Software Technology  
We evaluate this approach on multiple SE models, unconstrained as well as constrained, and compare its performance with standard evolutionary algorithms.  ...  Context: Evolutionary algorithms typically require a large number of evaluations (of solutions) to converge - which can be very slow and expensive to evaluate.Objective: To solve search-based software  ...  That said we see that SWAY is the top ranked optimizer in 4 4 cases and 3 4 with respect to Spread and Hypervolume respectively. • Section (c) of Figure 8 compares the number of evaluations required  ... 
doi:10.1016/j.infsof.2017.08.007 fatcat:224olsiukzf3baultjgq7gpgde

Top-N Recommender Systems Using Genetic Algorithm-Based Visual-Clustering Methods

Ukrit Marung, Nipon Theera-Umpon, Sansanee Auephanwiriyakul
2016 Symmetry  
The user-item clustering is based on the genetic algorithm (GA). The recommendation performance of the proposed methods was compared with that of traditional methods.  ...  The MSEIGS provides a comparable result with the other methods with smaller computation time.  ...  Acknowledgments: We would like to thank the Office of the Higher Education Commission, Thailand for supporting this study by grant fund under the Strategic Scholarships for Frontier Research Network for  ... 
doi:10.3390/sym8070054 fatcat:56jhxw5sfjaprm4ghkw2g7fuoa

Research on a Personalized Recommendation Algorithm

Min Yang, Yong Ma, Junlan Nie
2017 International Journal of Grid and Distributed Computing  
This paper studied the main personalized recommendation technology for current E-commerce. It proposed a hybrid recommendation algorithm based on opinion mining.  ...  Recommendation system is a new technology to recommend products for customers from huge amounts of products, which infers those objective users' preferences based on their personal information or online  ...  Comparative sentence and comparative study: comparison is a common way of assessment.  ... 
doi:10.14257/ijgdc.2017.10.1.12 fatcat:yhcu4rb4xfcs7jabeah4rm6k6a

SVD++ Recommendation Algorithm Based on Backtracking

Shijie Wang, Guiling Sun, Yangyang Li
2020 Information  
To address this limitation of the algorithm, this study proposes a novel method to accelerate the computation of the SVD++ algorithm, which can help achieve more accurate recommendation results.  ...  The algorithm is compared with the conventional CF algorithm in the FilmTrust, MovieLens 1 M and 10 M public datasets.  ...  Conflicts of Interest: The authors declare that there is no conflict of interest regarding the publication of this paper.  ... 
doi:10.3390/info11070369 fatcat:upji5z4o6fhnnpdmbtiw6iivz4

Topic Recommendation for Software Repositories using Multi-label Classification Algorithms [article]

Maliheh Izadi, Abbas Heydarnoori, Georgios Gousios
2021 arXiv   pre-print
Moreover, based on users' assessment, our approach is highly capable of recommending a correct and complete set of topics.  ...  It has also provided a set of featured topics, and their possible aliases carefully curated with the help of the community.  ...  Thus, P @n for a repository is the percentage of correctly predicted topics among the top-n recommended topics for that repository.  ... 
arXiv:2010.09116v4 fatcat:l7p6fde6wbfnxfktix4u35umga

A Big Data Based Cosmetic Recommendation Algorithm

2020 Journal of system and management sciences  
This study conducted a previous study on the algorithms that make up types and recommendation systems based on Big Data.  ...  This study has practical value to help customers and academic meaning of recommendation system using bigdata.  ...  desired condition, and recommends the top five products with high values.  ... 
doi:10.33168/jsms.2020.0203 fatcat:v3c5da6u25g5rbgxfq4sxbqrwy

A Method for Evaluating the Navigability of Recommendation Algorithms [chapter]

Daniel Lamprecht, Markus Strohmaier, Denis Helic
2016 Studies in Computational Intelligence  
In this paper, we propose a method to expand the repertoire of existing recommendation evaluation techniques with a method to evaluate the navigability of recommendation algorithms.  ...  Yet, the suitability of a recommendation algorithm to support these use cases cannot be comprehensively evaluated by any evaluation measures proposed so far.  ...  In the case of a top-N recommender system, users are generally only aware of the recommendations provided with the current item.  ... 
doi:10.1007/978-3-319-50901-3_20 fatcat:j2xcn4tqt5a2hpau626eleik5a

Comparison of group recommendation algorithms

Toon De Pessemier, Simon Dooms, Luc Martens
2013 Multimedia tools and applications  
These two grouping strategies, which convert traditional recommendation algorithms into group recommendation algorithms, are combined with five commonly used recommendation algorithms to calculate group  ...  Also the diversity, coverage, and serendipity of the group recommendations are to a large extent dependent on the used grouping strategy and recommendation algorithm.  ...  This study also compared the accuracy of these group recommendations with individual recommendations (i.e. recommendations for a single user).  ... 
doi:10.1007/s11042-013-1563-0 fatcat:qjzo6fhgurfsvp2im36fpl2ioq
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