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Design of Pitch Control Software Infrastructure Based on Collaborative Filtering Algorithm

Gang Li, Panya Roongruang, Sheng Bin
2022 Scientific Programming  
Based on this, this project takes the improved collaborative filtering algorithm as the core algorithm to build a set of digital TV pitch control software system and realizes the verification of the algorithm  ...  Based on the research of traditional pitch control software, this project improves the collaborative filtering algorithm and reduces the range of nearest neighbour set of pitch samples by introducing clustering  ...  Acknowledgments e study was supported by the Hengyang Normal University Music Faculty.  ... 
doi:10.1155/2022/8340833 fatcat:vybjum4qjbaklju6r2pq4lecci

Graph based Recommendation for Distributed Systems

Vivek Pandey, Padma Bonde
2017 International Journal of Computer Applications  
Collaborative, Demographic and Hybrid systems.  ...  This paper presents a survey of the area of recommendation systems and illustrates the state of the art of the recommendation technique that are generally classified into three categories: Content based  ...  By combining User-based and Item-based Collaborative filtering, accuracy of the results gets improve.  ... 
doi:10.5120/ijca2017914376 fatcat:fgqvf25fabdtje76buvbanpnc4

Review of Recommendation System for Web Application

2017 International Journal of Science and Research (IJSR)  
Association Rule Mining and Classification that are used to increase interest of users for recommender systems which is based on ratings of users in less timing.  ...  We will use A priory Algorithm and k-nearest neighbor algorithm. We will use techniques i.e.  ...  Content based filtering Content based filtering are based on description of the item and profile of the user reference item recommends to user based on table content for choosing an item.  ... 
doi:10.21275/art20163898 fatcat:qulufq7mnjdnrh6fl4bmii2emy

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  
Recommender systems have formulated in parallel with the web. Initially Recommender systems were based on demographic, content-based filtering and collaborative filtering.  ...  This paper provides an overview of recommender systems that include collaborative filtering, content-based filtering and hybrid approach of recommender system.  ...  Item description and a profile of the user"s orientation play an important role in Content-based filtering. Content-based filtering algorithms try to recommend items based on similarity count [27] .  ... 
doi:10.5120/19308-0760 fatcat:om2d4qpj4zda7bhb5kxvyo55wm

Improved Collaborative Filtering Algorithm Using Topic Model

Liu Na, Lu Ying, Tang Xiao-Jun, Wang Hai-Wen, Xiao Peng, Li Ming-Xia
2016 2016 17th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)  
Collaborative filtering algorithms make use of interactions rates between users and items for generating recommendations.  ...  The experiments showed that the proposed algorithm achieved better performance compared the other state-of-the-art algorithms on Movie Lens data sets.  ...  Collaborative Filtering algorithm includes user-based and item-based. In order to identify our proposed algorithm, we take experiments on these two side.  ... 
doi:10.1109/pdcat.2016.079 dblp:conf/pdcat/LiuLTWXL16 fatcat:lgpylxjv6nbthl7nq3q2mqn25i

Similarity measure and instance selection for collaborative filtering

Chun Zeng, Chun-Xiao Xing, Li-Zhu Zhou
2003 Proceedings of the twelfth international conference on World Wide Web - WWW '03  
And then we present an improved collaborative filtering algorithm based on these two methods.  ...  Collaborative filtering has been very successful in both research and applications such as information filtering and E-commerce.  ...  AN IMPROVED COLLABORATIVE FILTERING ALGORITHM On the basis of these two methods mentioned above, we introduce an improved collaborative filtering algorithm, called class-based algorithm, as shown in Figure  ... 
doi:10.1145/775152.775243 dblp:conf/www/ZengXZ03 fatcat:krwnszkrb5ftjh3nqlpa4453be

Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm

Hui Ning, Qian Li, Wei Wang
2020 Complexity  
Based on the above improvements, the improved collaborative filtering recommendation algorithm in this paper has greatly improved the prediction accuracy.  ...  weights; finally, appropriate weights are used to combine user-based and item-based collaborative filtering recommendation results.  ...  Based on these advantages of the K-means algorithm, it has also been widely used in collaborative filtering. Its improved collaborative filtering algorithm process is shown in Algorithm 1.  ... 
doi:10.1155/2020/6643888 fatcat:cqfikrqo7nehneqztobifq5x3i

Content Feature Extraction-based Hybrid Recommendation for Mobile Application Services

Chao Ma, YinggangSun, Zhenguo Yang, Hai Huang, Dongyang Zhan, Jiaxing Qu
2022 Computers Materials & Continua  
Finally, the recommendation process is completed by combining the item-based collaborative filtering recommendation algorithm.  ...  algorithm based on Term Frequency-Inverse Document Frequency (TFIDF-WMD) is used to calculate the similarity of mobile application services.  ...  Shen proposed a recommendation algorithm based on the combination of content and collaborative filtering.  ... 
doi:10.32604/cmc.2022.022717 fatcat:oyjvilh3xrfp7jugogyumh3cj4

ICBCF: One item-classification-based collaborative filtering algorithm

Zilei Sun, Nianlong Luo, Wei Kuang
2011 2011 International Symposium on Innovations in Intelligent Systems and Applications  
To improve the regular collaborative filtering algorithms, which is inefficiency and less concerned about item classification, this paper proposes a new item-classification-based algorithm.  ...  We believe this algorithm, which has a better accuracy and lower computation complexity in experiments, is worth popularization and becoming a new research direction of collaborative filtering algorithm  ...  CONCLUSION To improve the efficiency of classic collaborative filtering algorithms and take the category information of items into consideration, we propose a new item-classification-based collaborative  ... 
doi:10.1109/inista.2011.5946051 fatcat:yk6gg6mxgvhbdnvkwr5p57crgq

Application of Improved Collaborative Filtering in the Recommendation of E-commerce Commodities

Dan Chang, Hao Yu Gui, Rui Fan, Ze Zhou Fan, Ji Tian
2019 International Journal of Computers Communications & Control  
Then we selected the commodities that have positive feedback to calculate the comprehensive grades of marks and comments. After that, we build SVM-based collaborative filtering algorithm.  ...  Problems such as low recommendation precision and efficiency often exist in traditional collaborative filtering because of the huge basic data volume.  ...  Author contributions The authors contributed equally to this work. Conflict of interest The authors declare no conflict of interest. Bibliography  ... 
doi:10.15837/ijccc.2019.4.3594 fatcat:r53rxtpbrrd3bj66nzvj7pbfrq

Recommendation Systems for E-Commerce: A Review

Priya S, Mansoor Hussain D
2017 IJARCCE  
the Web and Movie Websites.The improved modelling of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and facility of a  ...  Nowadays, there is a big variety of different approaches and algorithms for data filtering and recommendation giving.Recommendation techniques can be classified into three major divisions: Collaborative  ...  The task of recommender algorithm [2] concerns the prediction of the user"s rating for the target item that the user has rated, based on the users" ratings on observed items.  ... 
doi:10.17148/ijarcce.2017.6496 fatcat:657sncidxrcezfoczizqpdj5ye

A survey on recommender systems

Motlatsi Liphoto, Chunling Du, Seleman Ngwira
2016 2016 International Conference on Advances in Computing and Communication Engineering (ICACCE)  
Many algorithms try to improve the accuracy in predicting the top N items thereby improving the recommendation quality.  ...  The recommendations deal with predicting user's ratings that are yet to be consumed by users, based on the items already rated by them and recommend the top N items with the highest predicted ratings.  ...  Amazon.com uses an algorithm based on item-based collaborative filtering to make their recommendations [1] .  ... 
doi:10.1109/icacce.2016.8073761 fatcat:zz7qaam2tzenneebdgfmwsropy

Collaborative filtering recommendation algorithm towards intelligent community

Wei Fu, Jun Liu, Yirong Lai
2019 Discrete and Continuous Dynamical Systems. Series S  
actual user rating in the community application scenario, and improve the recommendation accuracy and recommendation speed, compared with the traditional collaborative filtering recommendation algorithm  ...  At the same time, considering that the residents are relatively fixed, the K-means clustering algorithm can be combined with the user-based collaborative filtering recommendation algorithm to improve the  ...  Collaborative filtering recommendation algorithm can be divided into two types: item-based collaborative filtering recommendation and user-based collaborative filtering recommendation.  ... 
doi:10.3934/dcdss.2019054 fatcat:khkam2usavhffkphfs5ez2g33a

Study of Collaborative Filtering Recommendation Algorithm Scalability Issue

Reena Pagare, Shalmali A. Patil
2013 International Journal of Computer Applications  
The different algorithms studied are cluster based, item based and context based.  ...  This paper focuses on study of different collaborative filtering algorithms taking into consideration the scalability issue.  ...  A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering [11] Some collaborative filtering algorithms are based on user based clustering and some are based on item  ... 
doi:10.5120/11742-7305 fatcat:dll3ctx2qbelnbjslha444nthm

A Review on User Recommendation System Based Upon Semantic Analysis

Lovedeep Kaur, Naveen Kumari
2017 International Journal of Advanced Research in Computer Science and Software Engineering  
Recommender system applied various techniques and prediction algorithm to predict user interest on information, items and services from the tremendous amount of available data on the internet.  ...  filtering to improve the coverage of recommendation.  ...  Item description and a profile of the user"s orientation play an important role in Content-based filtering. Content-based filtering algorithms try to recommend items based on similarity count.  ... 
doi:10.23956/ijarcsse.v7i11.465 fatcat:o3hz2q3x7vdudbxnisqk5xsx44
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