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Filters
Recommendation System Using Bloom Filter in Mapreduce
2013
International Journal of Data Mining & Knowledge Management Process
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Collaborative filtering methods are vital component in recommender systems as they generate high-quality recommendations by influencing the likings of society of similar users. ...
The described algorithm of recommendation mechanism for mobile commerce is user based collaborative filtering using MapReduce which reduces scalability problem in conventional CF system. ...
Most commonly used algorithm for personalized recommendation in commercial recommendation system is Collaborative Filtering (CF). ...
doi:10.5121/ijdkp.2013.3608
fatcat:r2bwoc557ngzbl36ler6dle2oi
A Website Recommender System using Hadoop for Particular Domain
2015
International Journal of Engineering Research and
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Website Recommender system mainly uses HDFS (Hadoop Distributed File System) and Map-Reduce function. ...
A Website Recommender System using Hadoop" is implemented for recommending websites in particular domain. ...
Where this technique uses similarity based and function based approach. ...
doi:10.17577/ijertv4is040467
fatcat:dj2i6nag65fvllluowwt4zu3ei
A hybrid peer-to-peer recommendation system architecture based on locality-sensitive hashing
2014
Proceedings of 15th Conference of Open Innovations Association FRUCT
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Although centralized recommendations have several significant advantages, they also have two main drawbacks: single point of failure and the necessity for users to share their preferences. ...
The vast majority of current recommendation system approaches are centralized. ...
ACKNOWLEDGMENT The research was supported partly by projects funded by grants # 13-01-00271, # 13-07-13159, # 13-07-12095, # 13-07-00039, and # 14-07-00345 of the Russian Foundation for Basic Research, ...
doi:10.1109/fruct.2014.6872418
dblp:conf/fruct/SmirnovP14
fatcat:nfforxroyjgpdoqfa62jopupja
Parallel Implementation of the Slope One Algorithm for Collaborative Filtering
2012
2012 16th Panhellenic Conference on Informatics
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Recommender systems are mechanisms that filter information and predict a user's preference to an item. ...
This paper implements two parallel versions of the collaborative filtering algorithm Slope One, which has advantages such as its efficiency and the ability to update data dynamically. ...
INTRODUCTION Collaborative filtering based recommender systems introduce the users' opinion to the procedure of the recommendations generation. ...
doi:10.1109/pci.2012.34
dblp:conf/pci/KarydiM12
fatcat:cmt6fhr7evaclp27sjw7gx6qbe
Collaborative Filtering Recommendation Algorithm based on Spark
2019
International Journal of Performability Engineering
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Based on the Alternating Least Squares (ALS) collaborative filtering recommendation algorithm, this paper reduces the loss of the invisible factor item attribute information by merging the similarity of ...
the item on the loss function. ...
On the problem of similarity, the recommendation system has user similarity and item similarity. ...
doi:10.23940/ijpe.19.03.p22.930938
fatcat:4qg5rzvimnfwrdk3mksgd7bwy4
Promotive Effect of Psychological Intervention on English Vocabulary Teaching Based on Hybrid Collaborative Recommender Technology
2019
International Journal of Emerging Technologies in Learning (iJET)
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To further explore the relationship of psychological regulation with English vocabulary teaching, the hybrid collaborative recommender technology may be underlying to propose a hybrid algorithm for studying ...
how the English vocabulary teaching effect is subjected to the psychological emotion factors as parameters added in the original similarity calculation method in the psychological regulation environment ...
Acknowledgement Supported by "the Fundamental Research Funds for the Central Universities" (2018MS161). ...
doi:10.3991/ijet.v14i15.11185
fatcat:kyrfpha5nbbvtkvmyspy4ndr7q
Recommendation System using Keyword Base Approach
2015
IARJSET
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A variety of techniques are projected for activity recommendation, including content-based, collaborative, knowledge-based and different techniques. ...
Nowadays Web services are very widespread .Recommender systems represent user preferences for the aim of suggesting things to get or examine. ...
The output of the Reduce function is appended to a final output file for this reduces partition. 7) When all map tasks and reduce tasks have been completed, the master wakes up the user program. ...
doi:10.17148/iarjset.2015.2314
fatcat:kcov6yuyqzhavh6cojq5dplm54
Research on Collaborative Filtering Recommendation Algorithm based on User Interest for Cloud Computing
2017
International Journal of Grid and Distributed Computing
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Based on Map Reduce, we propose a collaborative filtering recommendation method to realize parallel recommendation. ...
Through computing the scene similarity based on mobile users, we find similar scenarios constructed target user set current situation, and then establish the item scoring scene and scoring matrix. ...
the score similarity matrix for collaborative recommendation, and improve the quality of the system. ...
doi:10.14257/ijgdc.2017.10.1.22
fatcat:5urwrmerlree7n5plachidtrpu
Collaborative filtering recommendation algorithm in cloud computing environment
2021
Computer Science and Information Systems
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A new recommendation algorithm can improve the accuracy of recommendation, and proposes a parallel collaborative filtering recommendation algorithm based on project. ...
The algorithm migrates the collaborative filtering detection technology and applies it to the cloud computing environment. It shortens the recommendation time by using the advantages of clustering. ...
Related Research
Collaborative Filtering Recommendation Technology The idea of collaborative filtering technology is simple and easy to understand, and it is recommended for individuals from the perspective ...
doi:10.2298/csis200119008t
fatcat:nx6qlewsvbaxzekg4p65a2dzxu
Collaborative Filtering Recommendation of Music MOOC Resources Based on Spark Architecture
2022
Computational Intelligence and Neuroscience
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the scores, and then the two algorithms are mixed to solve the optimal objective function to obtain the set of candidate recommendation data. ...
To improve the accuracy of the recommendation results of music MOOC resources, a mixed collaborative filtering recommendation algorithm based on Spark architecture is proposed. ...
At present, there are many research studies on personalized recommendation service for dynamic users by collaborative filtering algorithm. For example, Lim et al. ...
doi:10.1155/2022/2117081
pmid:35295283
pmcid:PMC8920684
fatcat:dlbpviysonhs5mastgntnpllla
A Distributed Phoenix++ Framework for Big Data Recommendation Systems
2014
International Journal of Intelligent Computing Research
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While many users utilize Hadoop-like MapReduce systems to implement recommendation systems, we utilize the highperformance shared-memory MapReduce system Phoenix++ to design a faster recommendation engine ...
The experiments on Amazon Elastic Compute Cloud (Amazon EC2) demonstrate that our new recommendation system can be faster than its Hadoop counterpart by up to 225% without losing recommendation quality ...
Item-based collaborative filtering (CF) algorithm The Collaborative Filtering (CF) algorithm is motivated by the idea that people often get the best recommendations from someone with a similar taste. ...
doi:10.20533/ijicr.2042.4655.2014.0054
fatcat:byxu7ubm3nbqhexdfq5q4zfule
Personalized Recommendation Method of Power Information Operation and Maintenance Knowledge Based on Spark
2016
Journal of Communications
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Finally, the personalized recommendation method, based on Spark, is applied to recommend power information operation and maintenance knowledge. ...
The traditional personalized recommendation method cannot meet the demand of personalized recommendation of power information maintenance knowledge in big data environment. ...
The collaborative filtering algorithm based on storage looks for neighboring users whose habits and preferences are similar to target user by analyzing historical rating data, and then recommends the knowledge ...
doi:10.12720/jcm.11.8.785-791
fatcat:fhxgok6shjamdgenme7k34xkvi
Research on Recommendation of Personalized Exercises in English Learning Based on Data Mining
2021
Scientific Programming
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; and finally, according to the clustering results, the similarity between students and the similarity between exercises are measured, and the collaborative filtering recommendation of personalized exercises ...
recommendation method for English learning based on data mining. ...
Collaborative Filtering Recommendation of Personalized Exercises for English Learning. ...
doi:10.1155/2021/5042286
fatcat:zb7uchdiyjecjikh4ovgo7yoq4
Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System
[article]
2019
arXiv
pre-print
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In the Federated Learning paradigm, a master machine learning model is distributed to user clients, the clients use their locally stored data and model for both inference and calculating model updates. ...
Empirical validation confirms a collaborative filter can be federated without a loss of accuracy compared to a standard implementation, hence enhancing the user's privacy in a widely used recommender application ...
The empirical evidence proved that the federated collaborative filter achieves statistically similar recommendation performance compared to the standard method. ...
arXiv:1901.09888v1
fatcat:77g4p7rhsbcktoojr6p4ji3acq
Hybrid Recommendation System with Clustering and Classification Method Based on Case of Clinic XYZ
2020
International Journal of Advanced Trends in Computer Science and Engineering
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The common use algorithm for product recommendation system is collaborative filtering and done directly to whole data. ...
This research applies 2 steps of algorithm, clustering, to separate the customer into different cluster based on the master data that available and classification to each cluster for product recommendation ...
The CF method uses preference ratings given by various customers to determine recommendations based on the opinions of other similar customers for a target client. ...
doi:10.30534/ijatcse/2020/69922020
fatcat:gj3qoigprjcdfngfnlgced2vmu
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