International Journal of Intelligent Systems and Applications(IJISA)
ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)
Published By: MECS Press
IJISA Vol.12, No.2, Apr. 2020
A New Similarity Measure Based on Gravitational Attraction for Improving the Accuracy of Collaborative Recommendations
Full Text (PDF, 523KB), PP.44-53
Recommender Systems (RSs) work as a personal agent for individuals who are not able to make decisions from the potentially overwhelming number of alternatives available on the World Wide Web (or simply Web). Neighborhood-based algorithms are traditional approaches for collaborative recommendations and are very popular due to their simplicity and efficiency. Neighborhood-based recommender systems use numerous kinds of similarity measures between users or items in order to achieve diverse goals for designing an RS such as accuracy, novelty, diversity etc. However, the existing similarity measures cannot manage well the data sparsity problems, which results in either very few co-rated items or absolutely no co-rated items. Furthermore, there are also situations where only the associations between users and items, such as buying/browsing behaviors, exist in form of unary ratings, a special case of ratings. In such situations, the existing similarity measures are either undefined or provide extreme values such as either 0 or 1. Thus, there is a compelling need to define a similarity measure that can deal with data sparsity problem and/or unary rating data. This article proposes a new similarity measure for neighborhood-based collaborative recommender systems based on Newton's law of universal gravitation. In order to achieve this, a new way of interpreting the relative mass as well as the relative distance has been taken into consideration by using the rating data from the user-item matrix. Finally, for evaluating the proposed approach against baseline approaches, several experiments have been conducted using standardized benchmark datasets such as MovieLens-100K and MovieLens-1M. Results obtained demonstrate that the proposed method provides better predictive accuracy in terms of RMSE and significantly improves the classification accuracy in terms of precision-recall.
Cite This Paper
Vijay Verma, Rajesh Kumar Aggarwal, "A New Similarity Measure Based on Gravitational Attraction for Improving the Accuracy of Collaborative Recommendations", International Journal of Intelligent Systems and Applications(IJISA), Vol.12, No.2, pp.44-53, 2020. DOI: 10.5815/ijisa.2020.02.05
G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,” IEEE Trans. Knowl. Data Eng., vol. 17, no. 6, pp. 734–749, 2005.
J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez, “Recommender systems survey,” Knowledge-Based Syst., vol. 46, pp. 109–132, 2013.
“YouTube.” [Online]. Available: https://www.youtube.com/. [Accessed: 19-Dec-2018].
“Netflix India – Watch TV Programmes Online, Watch Films Online.” [Online]. Available: https://www.netflix.com/in/. [Accessed: 19-Dec-2018].
J. Ben Schafer, J. Konstan, and J. Riedl, “Recommender Systems in E-Commerce,” pp. 158–166, 1999.
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.
M. Balabanović and Y. Shoham, “Fab: Content-based, Collaborative Recommendation,” Commun. ACM, vol. 40, no. 3, pp. 66–72, Mar. 1997.
K. Lang, “NewsWeeder : Learning to Filter Netnews ( To appear in ML 95 ),” Proc. 12th Int. Mach. Learn. Conf., 1995.
C. Science and J. Wnek, “Learning and Revising User Profiles: The Identification of Interesting Web Sites,” Mach. Learn., vol. 331, pp. 313–331, 1997.
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.
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.
Billsus Daniel and Pazzani Michael J., “User modeling for adaptative news access. ,” User Model. User-adapt. Interact., vol. 10, pp. 147–180, 2002.
R. Burke, “Hybrid recommender systems: Survey and experiments,” User Model. User-Adapted Interact., 2002.
X. Su and T. M. Khoshgoftaar, “A Survey of Collaborative Filtering Techniques,” Adv. Artif. Intell., vol. 2009, no. Section 3, pp. 1–19, 2009.
M. D. Ekstrand, “Collaborative Filtering Recommender Systems,” Found. Trends® Human–Computer Interact., vol. 4, no. 2, pp. 81–173, 2011.
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.
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.
C. C. Aggarwal, J. L. Wolf, K.-L. Wu, and P. S. Yu, “Horting hatches an egg: A New Graph-Theoretich Approach to Collaborative Filtering,” Proc. fifth ACM SIGKDD Int. Conf. Knowl. Discov. data Min. - KDD ’99, pp. 201–212, 1999.
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.
L. Ungar and D. Foster, “Clustering methods for collaborative filtering,” AAAI Work. Recomm. Syst., pp. 114–129, 1998.
Y.-H. Chen and E. I. George, “A Bayesian model for collaborative filtering,” Proc. 7th Int. Work. Artif. Intell. Stat., no. 1, 1999.
L. Getoor and M. Sahami, “Using probabilistic relational models for collaborative filtering,” Work. Web Usage Anal. User Profiling, 1999.
K. Goldberg, T. Roeder, D. Gupta, and C. Perkins, “Eigentaste,” no. August, pp. 1–11, 2000.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
J. Bobadilla, A. Hernando, F. Ortega, and J. Bernal, “A framework for collaborative filtering recommender systems,” Expert Syst. Appl., vol. 38, no. 12, pp. 14609–14623, 2011.
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.
V. Kumar and D. A. Shamma, “The Force Within : Recommendations Via Gravitational Att raction Between Items,” [UMAP2017]Proceedings 25th Conf. User Model. Adapt. Pers., pp. 294–297, 2017.
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.
F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Recommender Systems Handbook, 1st ed. Berlin, Heidelberg: Springer-Verlag, 2010.
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.
S. Kosub, “A note on the triangle inequality for the Jaccard distance,” no. 1, pp. 1–5, 2016.
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.
“MovieLens | GroupLens.” [Online]. Available: https://grouplens.org/datasets/movielens/. [Accessed: 22-Dec-2018].
F. M. Harper and J. A. Konstan, “The MovieLens Datasets,” ACM Trans. Interact. Intell. Syst., vol. 5, no. 4, pp. 1–19, 2015.
“Apache Mahout.” [Online]. Available: https://mahout.apache.org/. [Accessed: 22-Dec-2018].
S. Owen, R. Anil, T. Dunning, and E. Friedman, Mahout in Action. Greenwich, CT, USA: Manning Publications Co., 2011.