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Effective missing data prediction for collaborative filtering

Hao Ma, Irwin King, Michael R. Lyu
2007 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '07  
Second, we propose an effective missing data prediction algorithm, in which information of both users and items is taken into account.  ...  This paper focuses the memory-based collaborative filtering problems on two crucial factors: (1) similarity computation between users or items and (2) missing data prediction algorithms.  ...  Haixuan Yang for many valuable discussions on this topic.  ... 
doi:10.1145/1277741.1277751 dblp:conf/sigir/MaKL07 fatcat:c5gppjjp7jarzbr2rx7jdubj3y

Ma, H., King, I. and Lyu, R. M.: Effective missing data prediction for collaborative filtering, Proc. 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.39-46, 2007

杉山 一成
Journal of the Japanese Society for Artificial Intelligence  
doi:10.11517/jjsai.23.2_309 fatcat:jvkv4qrurzh6ndkcal3ps7kjfy

Research on User Clustering Collaborative Filtering Algorithm

Lihua Tian, Liguo Han, Junhua Yue
2016 International Journal of Hybrid Information Technology  
So we first fill the missing ratings by SVD prediction, and then implement k-means clustering in the filled matix.  ...  For these issues, a SVD-based K-means clustering CF algorithm is proposed. Traditional clustering-based CF algorithms have low recommendation precision because of data sparsity.  ...  If data are too sparse, cluster-based collaborative filtering predicts very inaccurately.  ... 
doi:10.14257/ijhit.2016.9.4.01 fatcat:4dfnrwjocvet5fvu45dpmuerce

Cultural Distance-Aware Service Recommendation Approach in Mobile Edge Computing

Yan Li, Yan Guo
2018 Scientific Programming  
In addition, considering the extreme sparsity of the rating data, missing rating prediction based on collaboration filtering is introduced in our approach.  ...  Traditional collaborative filtering recommendation approach only focuses on the similarity extracted from the rating data, which may lead to an inaccuracy expression of user preference.  ...  collaborative filtering approach based on the prediction ratings data.  ... 
doi:10.1155/2018/2181974 fatcat:bjaxfz734vgbfahkijfo52zjfu

A Convex Collaborative Filtering Framework for Global Market Return Prediction

Talal Al-Sulaiman, Ali Al-Matouq
2021 IEEE Access  
A new convex collaborative filtering framework for global market return prediction is presented.  ...  The method then uses convex nuclear norm minimization to predict returns for the missing future markets based on this analysis.  ...  ACKNOWLEDGMENT The authors would like to acknowledge the support of Prince Sultan University for paying the Article Processing Charges (APC) of this publication.  ... 
doi:10.1109/access.2021.3058646 fatcat:mue3xc42wvg5zflm3y3rdcn5dm

Boosting collaborative filtering based on missing data imputation using item's genre information

Weiwei Xia, Liang He, Junzhong Gu, Keqin He, Lei Ren
2009 2009 2nd IEEE International Conference on Computer Science and Information Technology  
Keywords -collaborative filtering; recommender system; missing data imputation; sparsity problem I. _____________________________ 978-1-4244-4520-2/09/$25.00 ©2009 IEEE Authorized licensed use limited  ...  However, most collaborative filtering algorithms suffer from data sparsity problem which leads to inaccuracy of recommendation.  ...  It can effectively improve the extreme sparsity of user rating data, and provide better recommendation results than traditional collaborative filtering algorithms.  ... 
doi:10.1109/iccsit.2009.5234936 fatcat:okmwcozabzgwhbxnfsxbvswc7y

Collaborative prediction and ranking with non-random missing data

Benjamin M. Marlin, Richard S. Zemel
2009 Proceedings of the third ACM conference on Recommender systems - RecSys '09  
In this paper we present the first study of the effect of non-random missing data on collaborative ranking, and extend our previous results regarding the impact of non-random missing data on collaborative  ...  Nearly all research on collaborative filtering and recommender systems is founded on the assumption that missing ratings are missing at random.  ...  ACKNOWLEDGMENTS We would like to thank Sam Roweis and Malcolm Slaney for their important contributions to earlier stages of this research.  ... 
doi:10.1145/1639714.1639717 dblp:conf/recsys/MarlinZ09 fatcat:drndmqvloja7fhb6vpt6fdgwmm

WSRec: A Collaborative Filtering Based Web Service Recommender System

Zibin Zheng, Hao Ma, Michael R. Lyu, Irwin King
2009 2009 IEEE International Conference on Web Services  
WSRec includes a user-contribution mechanism for Web service QoS information collection and an effective and novel hybrid collaborative filtering algorithm for Web service QoS value prediction.  ...  As the abundance of Web services on the World Wide Web increase, designing effective approaches for Web service selection and recommendation has become more and more important.  ...  ; 3) The Find Similar Users finds similar users from the training data of WSRec; 4) The Predict Missing Data predicts the missing QoS values for the active user using our hybrid collaborative filtering  ... 
doi:10.1109/icws.2009.30 dblp:conf/icws/ZhengMLK09 fatcat:auiouglpdvfnxnaevan6qhruy4

Imputed Neighborhood Based Collaborative Filtering

Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greiner
2008 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology  
Collaborative filtering (CF) is one of the most effective types of recommender systems.  ...  As data sparsity remains a significant challenge for CF, we consider basing predictions on imputed data, and find this often improves performance on very sparse rating data.  ...  Conclusions Collaborative filtering (CF) is one of the most successful approaches to building effective recommender systems.  ... 
doi:10.1109/wiiat.2008.99 dblp:conf/webi/SuKG08 fatcat:jdj2naog4nad7m5qracqdenuuy

LiRa: A New Likelihood-Based Similarity Score for Collaborative Filtering [article]

Veronika Strnadova-Neeley, Aydin Buluc, John R. Gilbert, Leonid Oliker, Weimin Ouyang
2017 arXiv   pre-print
We show that this score, based on a ratio of likelihoods, is more effective at identifying similar users than traditional similarity scores in user-based collaborative filtering, such as the Pearson correlation  ...  The high missing data rate, in combination with the large scale and high dimensionality that is typical of recommender systems data, requires new tools and methods for efficient data analysis.  ...  Collaborative filtering has proven to be an effective approach for recommendation, relying on the similarity of users or items in a system to predict future user preferences.  ... 
arXiv:1608.08646v2 fatcat:xlpkvgd6s5dcdacp5r74nw6ok4

Local Ensemble across Multiple Sources for Collaborative Filtering

Jing Zheng, Fuzhen Zhuang, Chuan Shi
2017 Proceedings of the 2017 ACM on Conference on Information and Knowledge Management - CIKM '17  
In this paper, we propose a novel LOcal EN semble framework across multiple source domains for collaborative filtering (called LOEN for short), where weights of multiple sources for each missing rating  ...  and effective.  ...  Recently, Transfer Collaborative Filtering (TCF), which transfers knowledge from source domains to the target domain for collaborative filtering, has been applied to alleviate the data sparsity problem  ... 
doi:10.1145/3132847.3133099 dblp:conf/cikm/ZhengZS17 fatcat:w7xmo4xhzzbivphhq3astxt7su

Collaborative Recommender Systems using User-item's Multiclass Co-clustering

Mugdha Adivarekar, Vina Lomte
2017 Indian Journal of Science and Technology  
Methods/Analysis: Collaborative filtering methods have been applied to different data like Sensing and monitoring data, financial data, and Electronic commerce and web applications.  ...  These systems actually work on basis of Collaborative filtering model and apply knowledge discovery techniques for live interaction with person.  ...  Item-item Collaborative Filtering User-user collaborative filtering is effective but suffers from scalability problems as the user base grows.  ... 
doi:10.17485/ijst/2017/v10i29/115863 fatcat:wxrmealsyndyrkuqt2pbicryme

A Machine Learning Model for Recommending Restaurants based on User Ratings

2020 International journal of recent technology and engineering  
Finally, this project aims to develop a good machine learning model, different collaborative filtering methodologies are considered to predict restaurants using user ratings.  ...  This project introduces a data processing pipeline, which uses reviews from registered users to generate a machine-learning model for each registered user.  ...  COLLABORATIVE FILTERING ALGORITHMS Collaborative Filtering is one of the method used for the purpose of recommender system.  ... 
doi:10.35940/ijrte.a1189.059120 fatcat:6jve3fvnwrapbh3jxxuovoxgfu

A Collaborative Filtering Recommendation Algorithm Incorporated with Life Cycle

Chong Lin Zheng, Kuang Rong Hao, Yong Sheng Ding
2013 Advanced Materials Research  
Collaborative filtering recommendation algorithm is the most successful technology for recommendation systems.  ...  However, traditional collaborative filtering recommendation algorithm does not consider the change of time information.  ...  This paper considers the effect of time information change in score predicting, also considers the effect of item life cycle in recommendation process, then proposes a collaborative filtering recommendation  ... 
doi:10.4028/www.scientific.net/amr.765-767.630 fatcat:zal5ypdt3rfmnay5jjpqh3z3ju

Computer Aided Diagnosis Based on K-means Collaborative Filtering Algorithm

Feng Xue-yuan, Li Peng, Qiao Pei-li
2016 International Journal of Hybrid Information Technology  
In this paper, we use k-means clustering algorithm to cluster the same type of patients, and then adopt collaborative filtering method to fill the missing data values for each cluster, in this way to reduce  ...  This paper proposes a clustering collaborative filtering based algorithm to solve the problem of data sparsity.  ...  Acknowledgement This paper is partially supported by Foundation for University Key Teacher of Heilongjiang Province (1252G023). Li Peng is the corresponding author of this paper.  ... 
doi:10.14257/ijhit.2016.9.4.06 fatcat:27zc5qm4v5ardbpryrm4mirtpi
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