Journal of Applied Information Science

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Performance Comparison of Similarity Measures Used in Recommendation Systems


Affiliations
1 Ankara University, Computer Engineering Department, Ankara, Turkey
     

   Subscribe/Renew Journal


There are more data on the web, thus it is hard to get relevant data and make good decisions. Recommendation systems provide suggestions to users about the various items. They are classified into four groups which are collaborative filtering, content-based filtering, knowledge-based recommender systems, and hybrid recommendation systems. There are some similarity measures such as Pearson Correlation, Euclidean, Uncentred Cosine, and LogLikelihood to calculate similarity between users or items. In this study, a user-based collaborative filtering recommendation system is developed on Eclipse platform using mahout library. To develop a recommendation system, different similarity measures such as Pearson Correlation, Euclidean, Uncentred Cosine, and LogLikelihood are used. After that, recommendation performances of them are compared. Movielens datasets are used to train and test the system. As a result, it is seen that while the best mean average error and the best ischolar_main mean square error performances belong to Uncentred Cosine similarity measure, the best precision, recall, and f-measure performances belong to Pearson Correlation measurement.

Keywords

Euclidean, Loglikelihood, Pearson, Recommendation, Similarity.
Subscription Login to verify subscription
User
Notifications
Font Size


  • T. K. Paradarami, N. D. Bastian, and J. L. Wightman, “A hybrid recommender system using artificial neural networks,” Expert Systems with Applications, vol. 83, pp. 300-313, 2017.

  • C. Speier, J. S. Valacich, and I. Vessey, “The influence of task interruption on individual decision making: An information overload perspective,” Decision Sciences, vol. 30, no. 2, pp. 337-360, 1999, doi: 10.1111/j.1540-5915.1999.tb01613.x.

  • P. Resnick, and H. R. Varian, “Recommender systems,” Communications of the ACM, vol. 40, no. 3, pp. 56-58, 1997.

  • F. Ricci, L. Rokach, and B. Shapira, “Introduction to recommender systems handbook,” in F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Eds., Recommender Systems Handbook, Boston, MA: Springer US, 2011, pp. 1-35.

  • G. Adomavicius, and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,” IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, June 2005, doi: 10.1109/TKDE.2005.99.

  • R. Burke, “Hybrid recommender systems: Survey and experiments,” User Modeling and User-Adapted Interaction, vol. 12, no. 4, pp. 331-370, November 2002.

  • D. Jannach, M. Zanker, A. Felfernig, and G. Friedrich, Recommender Systems: An Introduction, 2010, doi: 10.1017/CBO9780511763113.

  • R. Yera, and L. Martinez, “Fuzzy tools in recommender systems: A survey,” International Journal of Computational Intelligence Systems, vol. 10, no. 1, pp. 776-803, 2017.

  • S. J. Gong, “The collaborative filtering recommendation based on similar-priority and fuzzy clustering,” 2008 Workshop on Power Electronics and Intelligent Transportation System, Guangzhou, China, Aug. 2-3, 2008, doi: 10.1109/PEITS.2008.99.

  • A. Gunawardana, and G. Shani, “A survey of accuracy evaluation metrics of recommendation tasks,” Journal of Machine Learning Research, vol. 10, pp. 2935-2962, 2009.

  • X. Su, and T. M. Khoshgoftaar, “A survey of collaborative filtering techniques,” Advances in Artificial Intelligence, vol. 2009, article ID 421425, 19 pages, doi: 10.1155/2009/421425.

  • S. Panigrahi, R. K. Lenka, and A. Stitipragyan, “A hybrid distributed collaborative filtering recommender engine using apache spark,” International Workshop on Big Data and Data Mining Challenges on IoT and Pervasive Systems (BigD2M), 2016.

  • G. Adomavicius, and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,” IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734-749, 2005.

  • Y. Zhou, D. Wilkinson, R. Schreiber, and R. Pan, “Large-scale parallel collaborative filtering for the Netflix prize,” in AAIM’08, pp. 337-348, Berlin, Heidelberg, Springer-Verlag, 2008.

  • M. Isard, and Y. Yu, “Distributed data-parallel computing using a high-level programming language,” in SIGMOD, pp. 987-994, 2009.

  • M. A. Ghazanfar, and A. Prugel-Bennett, “Fulfilling the needs of gray-sheep users in recommender systems, a clustering solution,” 2011 Int. Conf. on Information Systems and Computational Intelligence, China, Jan. 18-20, 2011.

  • W. Zhou, J. Wen, Q. Qu, J. Zeng, and T. Cheng, “Shilling attack detection for recommender systems based on credibility of group users and rating time series”. Available: https://doi.org/10.1371/journal.pone.0196533

  • M. M. R. Siddiquee, N. Haider, and R. M. Rahman, “A fuzzy based recommendation system with collaborative filtering,” The 8th Int. Conf. on Software, Knowledge, Information Management and Applications (SKIMA 2014), Dhaka, Bangladesh, Dec. 18-20, 2014, doi: 10.1109/SKIMA.2014.7083524.

  • H. Parvin, P. Moradi, and S. Esmaeili, “A collaborative filtering method based on genetic algorithm and trust statements,” 2018 6th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), Kerman, Iran, Feb. 28-Mar. 2, 2018, doi: 10.1109/CFIS.2018.8336613.

  • B. Alhijawi, and Y. Kilani, “Using genetic algorithms for measuring the similarity values between users in collaborative filtering recommender systems,” 2016 IEEE/ACIS 15th Int. Conf. on Computer and Information Science (ICIS), Okayama, Japan, Jun. 26-29, 2016, doi: 10.1109/ICIS.2016.7550751.

  • S. K. Verma, N. Mittal, and B. Agarwal, “Hybrid recommender system based on fuzzy clustering and collaborative filtering,” 2013 4th Int. Conf. on Computer and Communication Technology (ICCCT), Allahabad, India, Sept. 20-22, 2013, doi: 10.1109/ICCCT.2013.6749613.

  • T. Jeon, J. Cho, S. Lee, G. Baek, and S. Kim, “A movie rating prediction system of user propensity analysis based on collaborative filtering and fuzzy system,” 2009 IEEE Int. Conf. on Fuzzy Systems, Jeju Island, South Korea, Aug. 20-24, 2009, doi: 10.1109/FUZZY.2009.5277415.

  • H. Shivhare, A. Gupta, and S. Sharma, “Recommender system using fuzzy c-means clustering and genetic algorithm based weighted similarity measure,” 2015 IEEE Int. Conf. on Computer, Communication and Control (IC4-2015), Indore, India, Sept. 10-12, 2015, doi: 10.1109/IC4.2015.7375707.

  • L. Shou-Qiang, Q. Ming, and X. Qing-Zhen, “Research and design of hybrid collaborative filtering algorithm scalability reform based on genetic algorithm optimization,” 2016 6th Int. Conf. on Digital Home (ICDH), Guangzhou, China, Dec. 2-4, 2016, doi: 10.1109/ICDH.2016.045.

  • H.-T. Kim, E. Kim, J.-H. Lee, and C. W. Ahn, “A recommender system based on genetic algorithm for music data,” 2010 2nd Int. Conf. on Computer Engineering and Technology, Chengdu, China, April 16-18, 2010, doi: 10.1109/ICCET.2010.5486161.

  • B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Item-based collaborative filtering recommendation algorithms,” Proc. of the 10th Int. Conf. on World Wide Web, pp. 285-295, 2001.

  • D. Roy, and A. Kundu, “Design of movie recommendation system by means of collaborative filtering,” International Journal of Emerging Technology and Advanced Engineering, vol. 3, no. 4, pp. 67-72, 2013.

  • Q. Li, and M. Zhou, “Research and design of an efficient collaborative filtering predication algorithm,” Proc. of the 4th Int. Conf. on Parallel and Distributed Computing, Applications and Technologies, Chengdu, China, Aug. 29-29, 2003, doi: 10.1109/PDCAT.2003.1236281.

  • Y. Ar, and E. Bostanci, “A genetic algorithm solution to the collaborative fi ltering problem,” Expert Systems with Applications, vol. 61, pp. 122-128, 2016.

  • A. J. Bagnall, G. J. Janacek, and M. Powell, “A likelihood ratio distance measure for the similarity between the fourier transform of time series,” PAKDD’05 Proc. of the 9th Pacific-Asia Conf. on Advances in Knowledge Discovery and Data Mining, pp. 737-743, 2005, doi:10.1007/11430919_85.

  • https://mahout.apache.org/docs/0.13.0/api/docs/mahout-mr/org/apache/mahout/cf/taste/impl/similarity/UncenteredCosineSimilarity.html

  • C. Birtolo, D. Ronca, and R. Armenise, “Improving accuracy of recommendation system by means of item-based fuzzy clustering collaborative filtering,” 2011 11th Int. Conf. on Intelligent Systems Design and Applications, Cordoba, Spain, Nov. 22-24, 2011, doi: 10.1109/ISDA.2011.6121638.

  • https://mahout.apache.org/docs/0.13.0/api/docs/mahout-mr/org/apache/mahout/cf/taste/impl/eval/AverageAbsoluteDifferenceRecommenderEvaluator.html 11.12.2019

  • https://mahout.apache.org/docs/0.13.0/api/docs/mahout-mr/org/apache/mahout/cf/taste/impl/eval/AbstractDifferenceRecommenderEvaluator.html#evaluate-org.apache.mahout.cf.taste.eval.RecommenderBuilder-org.apache.mahout.cf.taste.eval.DataModelBuilder-org.apache.mahout.cf.taste.model.DataModel-double-double 11.12.2019

  • https://mahout.apache.org/docs/0.13.0/api/docs/mahout-mr/org/apache/mahout/cf/taste/impl/eval/AverageAbsoluteDifferenceRecommenderEvaluator.html 22.07.2019

  • S. Jamalzehi, and M. B. Menhaj, “Scalable user similarity estimation based on fuzzy proximity for enhancing accuracy of collaborative filtering recommendation,” 2016 4th Int. Conf. on Control, Instrumentation, and Automation (ICCIA), Qazvin, Iran, Jan. 27-28, 2016, doi: 10.1109/ICCIAutom.2016.7483164.

  • M. A. G. Pinto, R. Tanscheit, and M. Vellasco, “Hybrid recommendation system based on collaborative filtering and fuzzy numbers,” 2012 IEEE Int. Conf. on Fuzzy Systems, Brisbane, QLD, Australia, Jun. 10-15, 2012, doi: 10.1109/FUZZ-IEEE.2012.6251308.

  • https://grouplens.org/datasets/movielens/ 25.08.2018


Abstract Views: 204

PDF Views: 0




  • Performance Comparison of Similarity Measures Used in Recommendation Systems

Abstract Views: 204  |  PDF Views: 0

Authors

Berna Seref
Ankara University, Computer Engineering Department, Ankara, Turkey
Erkan Bostanci
Ankara University, Computer Engineering Department, Ankara, Turkey

Abstract


There are more data on the web, thus it is hard to get relevant data and make good decisions. Recommendation systems provide suggestions to users about the various items. They are classified into four groups which are collaborative filtering, content-based filtering, knowledge-based recommender systems, and hybrid recommendation systems. There are some similarity measures such as Pearson Correlation, Euclidean, Uncentred Cosine, and LogLikelihood to calculate similarity between users or items. In this study, a user-based collaborative filtering recommendation system is developed on Eclipse platform using mahout library. To develop a recommendation system, different similarity measures such as Pearson Correlation, Euclidean, Uncentred Cosine, and LogLikelihood are used. After that, recommendation performances of them are compared. Movielens datasets are used to train and test the system. As a result, it is seen that while the best mean average error and the best ischolar_main mean square error performances belong to Uncentred Cosine similarity measure, the best precision, recall, and f-measure performances belong to Pearson Correlation measurement.

Keywords


Euclidean, Loglikelihood, Pearson, Recommendation, Similarity.

References