International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 11, No. 6, 8 Jun. 2019
Cover page and Table of Contents: PDF (size: 785KB)
A New Similarity Measure Based on Simple Matching Coefficient for Improving the Accuracy of Collaborative Recommendations
Full Text (PDF, 785KB), PP.37-49
Views: 0 Downloads: 0
Author(s)
Index Terms
Recommender Systems, Collaborative Filtering, Similarity Measures, Simple Matching Coefficient, Jaccard index, E-commerce
Abstract
Recommender Systems (RSs) are essential tools of an e-commerce portal in making intelligent decisions for an individual to obtain product recommendations. Neighborhood-based approaches are traditional techniques for collaborative recommendations and are very popular due to their simplicity and efficiency. Neighborhood-based recommender systems use numerous kinds of similarity measures for finding similar users or items. However, the existing similarity measures function only on common ratings between a pair of users (i.e. ignore the uncommon ratings) thus do not utilize all ratings made by a pair of users. Furthermore, the existing similarity measures may either provide inadequate results in many situations that frequently occur in sparse data or involve very complex calculations. Therefore, there is a compelling need to define a similarity measure that can deal with such issues. This research proposes a new similarity measure for defining the similarities between users or items by using the rating data available in the user-item matrix. Firstly, we describe a way for applying the simple matching coefficient (SMC) to the common ratings between users or items. Secondly, the structural information between the rating vectors is exploited using the Jaccard index. Finally, these two factors are leveraged to define the proposed similarity measure for better recommendation accuracy. For evaluating the effectiveness of the proposed method, several experiments have been performed using standardized benchmark datasets (MovieLens-1M, 10M, and 20M). Results obtained demonstrate that the proposed method provides better predictive accuracy (in terms of MAE and RMSE) along with improved classification accuracy (in terms of precision-recall).
Cite This Paper
Vijay Verma, Rajesh Kumar Aggarwal, "A New Similarity Measure Based on Simple Matching Coefficient for Improving the Accuracy of Collaborative Recommendations", International Journal of Information Technology and Computer Science(IJITCS), Vol.11, No.6, pp.37-49, 2019. DOI:10.5815/ijitcs.2019.06.05
Reference
[1]P. Resnick and H. R. Varian, Recommender systems, vol. 40, no. 3. 1997.
[2]F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Recommender Systems Handbook, 1st ed. Berlin, Heidelberg: Springer-Verlag, 2010.
[3]C. C. Aggarwal, Recommender Systems: The Textbook, 1st ed. Springer Publishing Company, Incorporated, 2016.
[4]M. Balabanović and Y. Shoham, “Fab: Content-based, Collaborative Recommendation,” Commun. ACM, vol. 40, no. 3, pp. 66–72, Mar. 1997.
[5]K. Lang, “NewsWeeder : Learning to Filter Netnews ( To appear in ML 95 ),” Proc. 12th Int. Mach. Learn. Conf., 1995.
[6]C. Science and J. Wnek, “Learning and Revising User Profiles: The Identification of Interesting Web Sites,” Mach. Learn., vol. 331, pp. 313–331, 1997.
[7]W. Hill, L. Stead, M. Rosenstein, and G. Furnas, “Recommending and evaluating choices in a virtual community of use,” in Proceedings of the SIGCHI conference on Human factors in computing systems - CHI ’95, 1995.
[8]U. Shardanand and P. Maes, “Social information filtering: Algorithms for Automating ‘Word of Mouth,’” Proc. SIGCHI Conf. Hum. factors Comput. Syst. - CHI ’95, pp. 210–217, 1995.
[9]Billsus Daniel and Pazzani Michael J., “User modeling for adaptative news access. ,” User Model. User-adapt. Interact., vol. 10, pp. 147–180, 2002.
[10]R. Burke, “Hybrid recommender systems: Survey and experiments,” User Model. User-Adapted Interact., 2002.
[11]Y. Shi, M. Larson, and A. Hanjalic, “Collaborative Filtering beyond the User-Item Matrix : A Survey of the State of the Art and Future Challenges,” ACM Comput. Surv., vol. 47, no. 1, pp. 1–45, 2014.
[12]X. Su and T. M. Khoshgoftaar, “A Survey of Collaborative Filtering Techniques,” Adv. Artif. Intell., vol. 2009, no. Section 3, pp. 1–19, 2009.
[13]M. D. Ekstrand, “Collaborative Filtering Recommender Systems,” Found. Trends® Human–Computer Interact., vol. 4, no. 2, pp. 81–173, 2011.
[14]J. S. Breese, D. Heckerman, and C. Kadie, “Empirical analysis of predictive algorithms for collaborative filtering,” Proc. 14th Conf. Uncertain. Artif. Intell., vol. 461, no. 8, pp. 43–52, 1998.
[15]D. Joaquin and I. Naohiro, “Memory-Based Weighted-Majority Prediction for Recommender Systems,” Res. Dev. Inf. Retr., 1999.
[16]A. Nakamura and N. Abe, “Collaborative Filtering Using Weighted Majority Prediction Algorithms,” in Proceedings of the Fifteenth International Conference on Machine Learning, 1998, pp. 395–403.
[17]D. Billsus and M. J. Pazzani, “Learning collaborative information filters,” Proc. Fifteenth Int. Conf. Mach. Learn., vol. 54, p. 48, 1998.
[18]T. Hofmann, “Collaborative filtering via gaussian probabilistic latent semantic analysis,” Proc. 26th Annu. Int. ACM SIGIR Conf. Res. Dev. information Retr. - SIGIR ’03, p. 259, 2003.
[19]L. Getoor and M. Sahami, “Using probabilistic relational models for collaborative filtering,” Work. Web Usage Anal. User Profiling, 1999.
[20]B. Marlin, “Modeling User Rating Profiles for Collaborative Filtering,” in Proceedings of the 16th International Conference on Neural Information Processing Systems, 2003, pp. 627–634.
[21]D. Pavlov and D. Pennock, “A maximum entropy approach to collaborative filtering in dynamic, sparse, high-dimensional domains,” Proc. Neural Inf. Process. Syst., pp. 1441–1448, 2002.
[22]K. Laghmari, C. Marsala, and M. Ramdani, “An adapted incremental graded multi-label classification model for recommendation systems,” Prog. Artif. Intell., vol. 7, no. 1, pp. 15–29, 2018.
[23]D. Goldberg, D. Nichols, B. M. Oki, and D. Terry, “Using collaborative filtering to weave an information tapestry,” Commun. ACM, vol. 35, no. 12, pp. 61–70, 1992.
[24]J. O. N. Herlocker and J. Riedl, “An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms,” Inf. Retr. Boston., pp. 287–310, 2002.
[25]P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “GroupLens : An Open Architecture for Collaborative Filtering of Netnews,” in Proceedings of the 1994 ACM conference on Computer supported cooperative work, 1994, pp. 175–186.
[26]J. A. Konstan, B. N. Miller, D. Maltz, J. L. Herlocker, L. R. Gordon, and J. Riedl, “GroupLens: applying collaborative filtering to Usenet news,” Commun. ACM, vol. 40, no. 3, pp. 77–87, 1997.
[27]H. J. Ahn, “A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem,” Inf. Sci. (Ny)., vol. 178, no. 1, pp. 37–51, 2008.
[28]H. Liu, Z. Hu, A. Mian, H. Tian, and X. Zhu, “A new user similarity model to improve the accuracy of collaborative filtering,” Knowledge-Based Syst., vol. 56, pp. 156–166, 2014.
[29]J. Bobadilla, F. Serradilla, and J. Bernal, “A new collaborative filtering metric that improves the behavior of recommender systems,” Knowledge-Based Syst., vol. 23, no. 6, pp. 520–528, 2010.
[30]J. Bobadilla, F. Ortega, A. Hernando, and Á. Arroyo, “A balanced memory-based collaborative filtering similarity measure,” Int. J. Intell. Syst., vol. 27, no. 10, pp. 939–946, Oct. 2012.
[31]J. Bobadilla, A. Hernando, F. Ortega, and A. Gutiérrez, “Collaborative filtering based on significances,” Inf. Sci. (Ny)., vol. 185, no. 1, pp. 1–17, 2012.
[32]J. Bobadilla, F. Ortega, and A. Hernando, “A collaborative filtering similarity measure based on singularities,” Inf. Process. Manag., vol. 48, no. 2, pp. 204–217, 2012.
[33]B. K. Patra, R. Launonen, V. Ollikainen, and S. Nandi, “A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data,” Knowledge-Based Syst., vol. 82, pp. 163–177, 2015.
[34]P. Jaccard, “Distribution comparée de la flore alpine dans quelques régions des Alpes occidentales et orientales,” Bull. la Socit Vaudoise des Sci. Nat., vol. 37, pp. 241–272, 1901.
[35]S. Bag, S. K. Kumar, and M. K. Tiwari, “An efficient recommendation generation using relevant Jaccard similarity,” Inf. Sci. (Ny)., vol. 483, pp. 53–64, 2019.
[36]J. Díez, D. Martínez-Rego, A. Alonso-Betanzos, O. Luaces, and A. Bahamonde, “Optimizing novelty and diversity in recommendations,” Prog. Artif. Intell., 2018.
[37]N. Good et al., “Combining Collaborative Filtering with Personal Agents for Better Recommendations,” in Proceedings of the Sixteenth National Conference on Artificial Intelligence and the Eleventh Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence, 1999, pp. 439–446.
[38]J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2011.
[39]J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl, “An algorithmic framework for performing collaborative filtering,” in Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR ’99, 1999, pp. 230–237.
[40]B. Sarwar, G. Karypis, J. Konstan, and J. Reidl, “Item-based collaborative filtering recommendation algorithms,” Proc. tenth Int. Conf. World Wide Web - WWW ’01, pp. 285–295, 2001.
[41]J. Bobadilla, F. Ortega, A. Hernando, and J. Alcalá, “Improving collaborative filtering recommender system results and performance using genetic algorithms,” Knowledge-Based Syst., vol. 24, no. 8, pp. 1310–1316, 2011.
[42]J. Bobadilla, F. Ortega, A. Hernando, and J. Bernal, “A collaborative filtering approach to mitigate the new user cold start problem,” Knowledge-Based Syst., vol. 26, pp. 225–238, 2012.
[43]B. Jiang, T. T. Song, and C. Yang, “A heuristic similarity measure and clustering model to improve the collaborative filtering algorithm,” ICNC-FSKD 2017 - 13th Int. Conf. Nat. Comput. Fuzzy Syst. Knowl. Discov., pp. 1658–1665, 2018.
[44]“Jester Collaborative Filtering Dataset.” [Online]. Available: https://www.ieor.berkeley.edu/~goldberg/jester-data/. [Accessed: 10-Jan-2019].
[45]“Amazon.com: Online Shopping for Electronics, Apparel, Computers, Books, DVDs & more.” [Online]. Available: https://www.amazon.com/. [Accessed: 08-Feb-2019].
[46]“Netflix – Watch TV Programmes Online, Watch Films Online.” [Online]. Available: https://www.netflix.com/. [Accessed: 08-Feb-2019].
[47]J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl, “Evaluating collaborative filtering recommender systems,” ACM Trans. Inf. Syst., vol. 22, no. 1, pp. 5–53, 2004.
[48]“MovieLens | GroupLens.” [Online]. Available: https://grouplens.org/datasets/movielens/. [Accessed: 22-Dec-2018].
[49]F. M. Harper and J. A. Konstan, “The MovieLens Datasets,” ACM Trans. Interact. Intell. Syst., vol. 5, no. 4, pp. 1–19, 2015.
[50]F. Ortega, B. Zhu, J. Bobadilla, and A. Hernando, “CF4J: Collaborative filtering for Java,” Knowledge-Based Syst., vol. 152, pp. 94–99, 2018.
[51]A. Gunawardana and G. Shani, “A survey of accuracy evaluation metrics of recommendation tasks,” J. Mach. Learn. Res., vol. 10, pp. 2935–2962, 2009.
[52]T. Chai and R. R. Draxler, “Root mean square error (RMSE) or mean absolute error (MAE)? -Arguments against avoiding RMSE in the literature,” Geosci. Model Dev., vol. 7, no. 3, pp. 1247–1250, 2014.
[53]G. Carenini and R. Sharma, “Exploring More Realistic Evaluation Measures for Collaborative Filtering,” in Proceedings of the 19th National Conference on Artifical Intelligence, 2004, pp. 749–754.