research-article

Collaborative Filtering Similarity Measures: Revisiting

Authors:

Hael Al-bashiri,

Mansoor Abdullateef Abdulgabber,

Fadhl HujainahAuthors Info & Claims

ICAIP '17: Proceedings of the International Conference on Advances in Image Processing

Pages 195 - 200

https://doi.org/10.1145/3133264.3133299

Published: 25 August 2017 Publication History

Abstract

This paper discussed the most commonly used similarity measures in Collaborative Filtering (CF) recommender system. In addition, the author introduced some recommendations related to CF system quality improvement which should be considered in the process of formulating similarity measure that may lead to alleviating the issue of data sparsity and some existing measures shortcomings. Generally, CF approach is one of the most widely used and most successful methods for the recommendation system, such as e-commerce. CF system introduced items to the user based on his/her previous ratings and the ratings of his/her neighbors. Therefore, the most important stage in CF system is locating the successful neighbor. Nevertheless, the sparsity of data is the major issue faced by the memory-based CF. The reason behind this is that many of the users rated a few number of items from the huge number of available items. This has encouraged many researchers to provide solutions. One of these solutions was by proposing or updating similarities measures take in considerations the global information preference, all ratings provided by users, the size of common ratings, and so on. In this work, the researcher discussed these measures alongside with their limitations. In addition, the researcher also listed some advices that are important in the process of locating successful neighbors, which may help researchers to improve the quality of CF system.

References

[1]

Adi, S., 2016. A Collaborative Tourism Websites to Support Electronic Promotion and Marketing by Using Enterprise Service Bus (ESB). Advanced Science Letters 22, 5-6, 1179--1183.

[2]

Adomavicius, G. and Tuzhilin, A., 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. Knowledge and Data Engineering, IEEE Transactions on 17, 6, 734--749.

Digital Library

[3]

Ahn, H.J., 2008. A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Information Sciences 178, 1, 37--51.

Digital Library

[4]

Balabanović, M. and Shoham, Y., 1997. Fab: content-based, collaborative recommendation. Communications of the ACM 40, 3, 66--72.

Digital Library

[5]

Bobadilla, J., Hernando, A., Ortega, F., and Bernal, J., 2011. A framework for collaborative filtering recommender systems. Expert Systems with Applications 38, 12, 14609--14623.

Digital Library

[6]

Bobadilla, J., Ortega, F., Hernando, A., and Bernal, J., 2012. A collaborative filtering approach to mitigate the new user cold start problem. Knowledge-Based Systems 26, 225--238.

Digital Library

[7]

Bobadilla, J., Serradilla, F., and Bernal, J., 2010. A new collaborative filtering metric that improves the behavior of recommender systems. Knowledge-Based Systems 23, 6, 520--528.

Digital Library

[8]

Breese, J.S., Heckerman, D., and Kadie, C., 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence Morgan Kaufmann Publishers Inc., 43--52.

[9]

Ekstrand, M.D., Riedl, J.T., and Konstan, J.A., 2011. Collaborative filtering recommender systems. Foundations and Trends in Human-Computer Interaction 4, 2, 81--173.

Digital Library

[10]

Goldberg, D., Nichols, D., Oki, B.M., and Terry, D., 1992. Using collaborative filtering to weave an information tapestry. Communications of the ACM 35, 12, 61--70.

Digital Library

[11]

Hameed, M.A., Al Jadaan, O., and Ramachandram, S., 2012. Collaborative filtering based recommendation system: A survey. International Journal on Computer Science and Engineering 4, 5, 859.

[12]

Herlocker, J.L., Konstan, J.A., Borchers, A., and Riedl, J., 1999. An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval ACM, 230--237.

Digital Library

[13]

Hill, W., Stead, L., Rosenstein, M., and Furnas, G., 1995. Recommending and evaluating choices in a virtual community of use. In Proceedings of the SIGCHI conference on Human factors in computing systems ACM Press/Addison-Wesley Publishing Co., 194--201.

Digital Library

[14]

Hofmann, T., 2004. Latent semantic models for collaborative filtering. ACM Transactions on Information Systems (TOIS) 22, 1 (Jan), 89--115. DOI= http://dx.doi.org/Doi 10.1145/963770.963774.

Digital Library

[15]

Jamali, M. and Ester, M., 2009. Trustwalker: a random walk model for combining trust-based and item-based recommendation. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining ACM, 397--406.

Digital Library

[16]

Karypis, G., 2001. Evaluation of item-based top-n recommendation algorithms. In Proceedings of the tenth international conference on Information and knowledge management ACM, 247--254.

Digital Library

[17]

Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., and Riedl, J., 1997. GroupLens: applying collaborative filtering to Usenet news. Communications of the ACM 40, 3, 77--87.

Digital Library

[18]

Konstan, J.A., Riedl, J., Borchers, A., and Herlocker, J.L., 1998. Recommender systems: A grouplens perspective. In Recommender Systems: Papers from the 1998 Workshop (AAAI Technical Report WS-98-08), 60--64.

[19]

Koren, Y., Bell, R., and Volinsky, C., 2009. Matrix factorization techniques for recommender systems. Computer, 8, 30--37.

Digital Library

[20]

Koutrika, G., Bercovitz, B., and Garcia-Molina, H., 2009. FlexRecs: expressing and combining flexible recommendations. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of data ACM, 745--758.

Digital Library

[21]

Linden, G., Smith, B., and York, J., 2003. Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet computing 7, 1, 76--80.

Digital Library

[22]

Liu, H., Hu, Z., Mian, A., Tian, H., and Zhu, X., 2014. A new user similarity model to improve the accuracy of collaborative filtering. Knowledge-Based Systems 56, 156--166.

Digital Library

[23]

Ma, H., King, I., and Lyu, M.R., 2007. Effective missing data prediction for collaborative filtering. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval ACM, 39--46.

Digital Library

[24]

Murti, Y.R. and Baizal, Z., 2016. Compound Critiquing for Conversational Recommender System Based on Functional Requirement. Advanced Science Letters 22, 8, 1892--1896.

[25]

Patra, B.K., Launonen, R., Ollikainen, V., and Nandi, S., 2015. A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data. Knowledge-Based Systems 82, 163--177.

Digital Library

[26]

Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J., 1994. GroupLens: an open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer supported cooperative work ACM, 175--186.

Digital Library

[27]

Resnick, P. and Varian, H.R., 1997. Recommender systems. Communications of the ACM 40, 3, 56--58.

Digital Library

[28]

Ricci, F., Rokach, L., and Shapira, B., 2011. Introduction to recommender systems handbook. Springer.

[29]

Sarwar, B., Karypis, G., Konstan, J., and Riedl, J., 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web ACM, 285--295.

Digital Library

[30]

Shardanand, U. and Maes, P., 1995. Social information filtering: algorithms for automating "word of mouth". In Proceedings of the SIGCHI conference on Human factors in computing systems ACM Press/Addison-Wesley Publishing Co., 210--217.

Digital Library

[31]

Sharma, L. and Gera, A., 2013. A survey of recommendation system: Research challenges. International Journal of Engineering Trends and Technology (IJETT) 4, 5, 1989--1992.

[32]

Su, X. and Khoshgoftaar, T.M., 2009. A survey of collaborative filtering techniques. Advances in artificial intelligence 2009, 4.

Digital Library

[33]

Ungar, L.H. and Foster, D.P., 1998. Clustering methods for collaborative filtering. In AAAI workshop on recommendation systems, 114--129.

Cited By

Zhang ZDong Y(2022)Weighted Similarity and Core-User-Core-Item Based RecommendationsEntropy10.3390/e2405060924:5(609)Online publication date: 27-Apr-2022
https://doi.org/10.3390/e24050609
Jain GMahara TSharma SAgarwal SKim H(2022)TD-DNN: A Time Decay-Based Deep Neural Network for Recommendation SystemApplied Sciences10.3390/app1213639812:13(6398)Online publication date: 23-Jun-2022
https://doi.org/10.3390/app12136398
Jain GMahara TSharma SSangaiah A(2022)A Cognitive Similarity-Based Measure to Enhance the Performance of Collaborative Filtering-Based Recommendation SystemIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.31874309:6(1785-1793)Online publication date: Dec-2022
https://doi.org/10.1109/TCSS.2022.3187430
Show More Cited By

Index Terms

Collaborative Filtering Similarity Measures: Revisiting
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Collaborative filtering

Recommendations

A new similarity function for selecting neighbors for each target item in collaborative filtering

As one of the collaborative filtering (CF) techniques, memory-based CF technique which recommends items to users based on rating information of like-minded users (called neighbors) has been widely used and has also proven to be useful in many practices ...
Generating Items Recommendations by Fusing Content and User-Item based Collaborative Filtering
Abstract
Nowadays e-commerce has spread all over the world. The e-shops are not similar to the physical shops. The e-shops can have hundreds or thousands of items independent of physical boundaries. The information about all these products is available on ...
An Improved Similarity Measure Method in Collaborative Filtering Recommendation Algorithm
CLOUDCOM-ASIA '13: Proceedings of the 2013 International Conference on Cloud Computing and Big Data

Collaborative filtering recommendation technology is successfully used in personalized recommendation services. Since the magnitudes of users and commodities in E-commerce system has increased dramatically, the user rating data in the entire item space ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICAIP '17: Proceedings of the International Conference on Advances in Image Processing

August 2017

223 pages

ISBN:9781450352956

DOI:10.1145/3133264

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

Sultan Qaboos University: Sultan Qaboos University
USM: Universiti Sains Malaysia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 August 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICAIP 2017

ICAIP 2017: 2017 International Conference on Advances in Image Processing

August 25 - 27, 2017

Bangkok, Thailand

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
171
Total Downloads

Downloads (Last 12 months)17
Downloads (Last 6 weeks)0

Reflects downloads up to 28 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Zhang ZDong Y(2022)Weighted Similarity and Core-User-Core-Item Based RecommendationsEntropy10.3390/e2405060924:5(609)Online publication date: 27-Apr-2022
https://doi.org/10.3390/e24050609
Jain GMahara TSharma SAgarwal SKim H(2022)TD-DNN: A Time Decay-Based Deep Neural Network for Recommendation SystemApplied Sciences10.3390/app1213639812:13(6398)Online publication date: 23-Jun-2022
https://doi.org/10.3390/app12136398
Jain GMahara TSharma SSangaiah A(2022)A Cognitive Similarity-Based Measure to Enhance the Performance of Collaborative Filtering-Based Recommendation SystemIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.31874309:6(1785-1793)Online publication date: Dec-2022
https://doi.org/10.1109/TCSS.2022.3187430
Jain GMahara TC.Sharma S(2022)Effective time context based collaborative filtering recommender system inspired by Gower’s coefficientInternational Journal of System Assurance Engineering and Management10.1007/s13198-022-01813-z14:1(429-447)Online publication date: 25-Dec-2022
https://doi.org/10.1007/s13198-022-01813-z
Jindal ASharma NVerma V(2022)Joyful Jaccard: An Analysis of Jaccard-Based Similarity Measures in Collaborative RecommendationsInternational Conference on Artificial Intelligence and Sustainable Engineering10.1007/978-981-16-8542-2_3(29-41)Online publication date: 30-Apr-2022
https://doi.org/10.1007/978-981-16-8542-2_3
Verma VAggarwal R(2020)A comparative analysis of similarity measures akin to the Jaccard index in collaborative recommendations: empirical and theoretical perspectiveSocial Network Analysis and Mining10.1007/s13278-020-00660-910:1Online publication date: 9-Jun-2020
https://doi.org/10.1007/s13278-020-00660-9
Saoudi EAdoui El Ouadrhiri AJai Andaloussi SOuchetto O(2020)Optimization of a Similarity Performance on Bounded Content of Motion Histogram by Using Distributed ModelAdvances on Smart and Soft Computing10.1007/978-981-15-6048-4_1(3-13)Online publication date: 20-Oct-2020
https://doi.org/10.1007/978-981-15-6048-4_1
Meng YDai XYan XCheng JLiu WGuo JLiao BChen G(2020)PMD: An Optimal Transportation-Based User Distance for Recommender SystemsAdvances in Information Retrieval10.1007/978-3-030-45442-5_34(272-280)Online publication date: 8-Apr-2020
https://doi.org/10.1007/978-3-030-45442-5_34

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents