A Novel Algorithmic Similarity Measure for Collaborative Filtering: A Recommendation System Based on Rating Distances

Yıl 2022, Cilt: 10 Sayı: 1, 57 - 69, 01.01.2022

Şule Öztürk Birim Ayça Tümtürk

https://doi.org/10.21541/apjess.1060744

Öz

Internet sources contain a vast amount of information about items that people desire to purchase. It is impossible to evaluate these resources and come to an informed decision. People need automated systems that evaluate previous information and propose item alternatives. Recommending items using a smart system, which is based on the previous user preferences, has growing importance since the available product data is exponentially growing. Additionally, it is difficult to find new and correct things that a user would like among this massive amount of data. To make accurate recommendations with a smart system, researchers and practitioners use collaborative filtering methods with similarity calculation based on user preferences. The crucial point in collaborative filtering is to find a valuable measure that resembles correct similarity between users. The current similarity metrics in the literature have some disadvantages in conducting accurate recommendations. To improve the recommendation performance, this study proposes a novel similarity measure that assesses the distance between the user’s ratings and the median score. Considering distance from the median score is essential since some users may prefer to rate close to the median rather than the extremes. Experiments were conducted with a famous collaborative filtering dataset. Results showed that proposed similarity measure demonstrated superior performance regarding the recommendation accuracy. Implications of our results for XYZ are discussed.

Anahtar Kelimeler

Collaborative filtering, recommender systems, similarity measure, prediction accuracy, classification accuracy

Kaynakça

• S. Davis and L. Toney, “How Coronavirus (COVID-19) Is Impacting Ecommerce,” Roi Revolution, 2021. .
• M. Z. Fisher, “Why Product Reviews are Important for Buyers and Sellers | ShipStation,” ShipStation, 2018. [Online]. Available: https://www.shipstation.com/blog/product-reviews-important-buyers-sellers/. [Accessed: 12-Nov-2021].
• G. Linden, B. Smith, and J. York, “Amazon.com recommendations: Item-to-item collaborative filtering,” IEEE Internet Computing, vol. 7, no. 1, pp. 76–80, 2003, doi: 10.1109/MIC.2003.1167344.
• F. M. Harper and J. A. Konstan, “The MovieLens Datasets,” ACM Transactions on Interactive Intelligent Systems, vol. 5, no. 4, pp. 1–19, 2016, doi: 10.1145/2827872.
• E. Rich, “User modeling via stereotypes,” Cognitive Science, vol. 3, no. 4, pp. 329–354, 1979, doi: 10.1016/S0364-0213(79)80012-9.
• P. Lops, M. De Gemmis, and G. Semeraro, “Content-based Recommender Systems: State of the Art and Trends,” in Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Eds. Boston: Springer, 2011.
• W. W. Cohen and W. Fan, “Web-collaborative filtering: recommending music by crawling the Web,” Computer Networks, vol. 33, no. 1, pp. 685–698, 2000, doi: 10.1016/S1389-1286(00)00057-8.
• A. B. Barragáns-Martínez, E. Costa-Montenegro, J. C. Burguillo, M. Rey-López, F. A. Mikic-Fonte, and A. Peleteiro, “A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition,” Information Sciences, vol. 180, no. 22, pp. 4290–4311, 2010, doi: 10.1016/j.ins.2010.07.024.
• J. S. Breese, D. Heckerman, and C. Kadie, “Empirical analysis of predictive algorithms for collaborative filtering,” Proceedings of the 14th Annual Conference on Uncertainty in Artificial Intelligence, pp. 43--52, 1998, doi: 10.1111/j.1553-2712.2011.01172.x.
• H. Ma, I. King, and M. R. Lyu, “Effective missing data prediction for collaborative filtering,” in SIGIR ’07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 2007, pp. 39–46, doi: 10.1145/1277741.1277751.
• D. Goldberg, D. Nichols, B. M. Oki, and D. Terry, “Using collaborative filtering to weave an information tapestry,” Communications of the ACM, vol. 35, no. 12, pp. 61–70, 1992, doi: 10.1145/138859.138867.
• K. Y. Goldberg and T. M. Roeder, “Eigentaste: A Constant Time Collaborative Filtering Algorithm,” CEUR Workshop Proceedings, vol. 1225, no. July, pp. 41–42, 2001, doi: 10.1023/A.
• X. Luo, M. Zhou, Y. Xia, and Q. Zhu, “An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems,” IEEE Transactions on Industrial Informatics, vol. 10, no. 2, pp. 1273–1284, 2014, doi: 10.1109/TII.2014.2308433.
• M. D. Ekstrand, “Collaborative Filtering Recommender Systems,” Foundations and Trends® in Human–Computer Interaction, vol. 4, no. 2, pp. 81–173, 2011, doi: 10.1561/1100000009.
• 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, doi: 10.1109/TKDE.2005.99.
• R. Jin, J. Y. Chai, and L. Si, “An automatic weighting scheme for collaborative filtering,” Proceedings of the 27th annual international conference on Research and development in information retrieval - SIGIR ’04, pp. 337–344, 2004, doi: 10.1145/1008992.1009051.
• H. J. Ahn, “A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem,” Information Sciences, vol. 178, no. 1, pp. 37–51, 2008, doi: 10.1016/j.ins.2007.07.024.
• Y. C. Cai, H. Leung, Q. Li, H. Min, J. Tang, and J. Li, “Typicality-Based Collaborative Filtering Recommendation,” IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 3, pp. 766–779, 2013, doi: 10.1109/TKDE.2013.7.
• H. Luo, C. Niu, R. Shen, and C. Ullrich, “A collaborative filtering framework based on both local user similarity and global user similarity,” Machine Learning, vol. 72, no. 3, pp. 231–245, 2008, doi: 10.1007/s10994-008-5068-4.
• B. Zhang and B. Yuan, “Improved collaborative filtering recommendation algorithm of similarity measure,” AIP Conference Proceedings, vol. 1839, 2017, doi: 10.1063/1.4982532.
• M. Y. H. Al-Shamri and K. K. Bharadwaj, “Fuzzy-genetic approach to recommender systems based on a novel hybrid user model,” Expert Systems with Applications, vol. 35, no. 3, pp. 1386–1399, 2008, doi: 10.1016/j.eswa.2007.08.016.
• M. Jamali and M. Ester, “TrustWalker: a random walk model for combining trust-based and item-based recommendation,” Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 397–406, 2009, doi: citeulike-article-id:5151320.
• J. Bobadilla, F. Ortega, A. Hernando, and J. Bernal, “A collaborative filtering approach to mitigate the new user cold start problem,” Knowledge-Based Systems, vol. 26, pp. 225–238, 2012, doi: 10.1016/j.knosys.2011.07.021.
• J. Bobadilla, F. Serradilla, and J. Bernal, “A new collaborative filtering metric that improves the behavior of recommender systems,” Knowledge-Based Systems, vol. 23, no. 6, pp. 520–528, 2010, doi: 10.1016/j.knosys.2010.03.009.
• J. Bobadilla, F. Ortega, A. Hernando, and J. Alcala, “Improving collaborative filtering recommender system results and performance using genetic algorithms,” Knowledge-Based Systems, vol. 24, no. 8, pp. 1310–1316, 2011, doi: 10.1016/j.knosys.2011.06.005.
• J. Bobadilla, A. Hernando, F. Ortega, and J. Bernal, “A framework for collaborative filtering recommender systems,” Expert Systems with Applications, vol. 38, no. 12, pp. 14609–14623, 2011, doi: 10.1016/j.eswa.2011.05.021.
• J. Bobadilla, A. Hernando, F. Ortega, and A. Gutiérrez, “Collaborative filtering based on significances,” Information Sciences, vol. 185, no. 1, pp. 1–17, 2012, doi: 10.1016/j.ins.2011.09.014.
• J. Bobadilla, F. Ortega, and A. Hernando, “A collaborative filtering similarity measure based on singularities,” Information Processing and Management, vol. 48, no. 2, pp. 204–217, 2012, doi: 10.1016/j.ipm.2011.03.007.
• F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Recommender Systems Handbook, 2nd editio., vol. 53, no. 9. 2011.
• G. Koutrika, B. Bercovitz, and H. Garcia-Molina, “FlexRecs: expressing and combining flexible recommendations,” Proceedings of the 35th SIGMOD international conference on Management of data, pp. 745–758, 2009, doi: 10.1145/1559845.1559923.
• L. Baltrunas and F. Ricci, “Experimental evaluation of context-dependent collaborative filtering using item splitting,” User Modeling and User-Adapted Interaction, vol. 24, no. 1–2, pp. 7–34, 2014, doi: 10.1007/s11257-012-9137-9.
• S. Jiang, X. Qian, J. Shen, Y. Fu, and T. Mei, “Author topic model-based collaborative filtering for personalized POI recommendations,” IEEE Transactions on Multimedia, vol. 17, no. 6, pp. 907–918, 2015, doi: 10.1109/TMM.2015.2417506.
• D. Anand and K. K. Bharadwaj, “Utilizing various sparsity measures for enhancing accuracy of collaborative recommender systems based on local and global similarities,” Expert Systems with Applications, vol. 38, no. 5, pp. 5101–5109, 2011, doi: 10.1016/j.eswa.2010.09.141.
• P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “GroupLens : An Open Architecture for Collaborative Filtering of Netnews,” Proceedings of the 1994 ACM conference on Computer supported cooperative work, pp. 175–186, 1994, doi: 10.1145/192844.192905.
• Z. Chen, Y. Wang, S. Zhang, H. Zhong, and L. Chen, “Differentially private user-based collaborative filtering recommendation based on k-means clustering,” Expert Systems with Applications, vol. 168, no. April 2019, 2021, doi: 10.1016/j.eswa.2020.114366.
• N. Bhalse and R. Thakur, “Algorithm for movie recommendation system using collaborative filtering,” Materials Today: Proceedings, no. xxxx, pp. 1–6, 2021, doi: 10.1016/j.matpr.2021.01.235.
• Y. Afoudi, M. Lazaar, and M. Al Achhab, “Hybrid recommendation system combined content-based filtering and collaborative prediction using artificial neural network,” Simulation Modelling Practice and Theory, vol. 113, no. July, p. 102375, 2021, doi: 10.1016/j.simpat.2021.102375.
• 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 Systems, vol. 56, pp. 156–166, 2014, doi: 10.1016/j.knosys.2013.11.006.
• S. Ahmadian, M. Meghdadi, and M. Afsharchi, “A social recommendation method based on an adaptive neighbor selection mechanism,” Information Processing and Management, vol. 0, pp. 1–19, 2017, doi: 10.1016/j.ipm.2017.03.002.
• W. Wang, J. Lu, and G. Zhang, “A new similarity measure-based collaborative filtering approach for recommender systems,” in Foundations of Intelligent Systems, 2014, pp. 443–452.
• W. Wang, G. Zhang, and J. Lu, “Collaborative Filtering with Entropy‐Driven User Similarity in Recommender Systems.,” International Journal of intelligent Systems, vol. 30, no. 8, pp. 854–870, 2015, doi: 10.1002/int.
• S. Lee, “Improving Jaccard Index Using Genetic Algorithms for Collaborative Filtering,” in Information Science and Applications 2017: ICISA 2017, Springer, 2017, pp. 378–385.
• X. Amatriain, J. M. Pujol, and N. Oliver, “I like it... i like it not: Evaluating user ratings noise in recommender systems,” in International Conference on User Modeling, Adaptation, and Personalization, 2009, vol. 5535 LNCS, pp. 247–258, doi: 10.1007/978-3-642-02247-0_24.
• A. Agarwal and M. Chauhan, “Similarity Measures used in Recommender Systems: A Study,” International Journal of Engineering Technology Science and Research, vol. 4, no. 6, pp. 2394–3386, 2017.
• B. Yapriady and A. Uitdenbogerd, “Combining demographic data with collaborative filtering for automatic music recommendation,” in Knowledge-Based Intelligent Information and Engineering Systems: 9th International Conference, 2005, pp. 201–207, doi: 10.5772/38338.
• B. Sarwar, G. Karypis, J. Konstan, and J. Reidl, “Item-based collaborative filtering recommendation algorithms,” in 10th International Conference on World Wide Web - WWW ’01, 2001, pp. 285–295, doi: 10.1145/371920.372071.
• U. Shardanand and P. Maes, “Social information filtering algorithms for automating ‘word of mouth,” Conference proceedings on Human factors in computing systems, pp. 210–217, 1995.
• Y. Wang, J. Deng, J. Gao, and P. Zhang, “A hybrid user similarity model for collaborative filtering,” Information Sciences, vol. 418–419, pp. 102–118, 2017, doi: 10.1016/j.ins.2017.08.008.
• G. Guo, J. Zhang, and N. Yorke-Smith, “A Novel Evidence-Based Bayesian Similarity Measure for Recommender Systems,” ACM Transactions on the Web, vol. 10, no. 2, pp. 1–30, 2013, doi: 10.1145/2856037.
• J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl, “Evaluating collaborative filtering recommender systems,” ACM Transactions on Information Systems, vol. 22, no. 1, pp. 5–53, 2004, doi: 10.1145/963770.963772.
• K. Miyahara and M. J. Pazzani, “Collaborative Filtering with the Simple Bayesian Classifier,” IPSJ Journal, vol. 43, no. 11, pp. 679–689, 2002, doi: 10.1007/3-540-44533-1_68.
• S. J. Yu, “The dynamic competitive recommendation algorithm in social network services,” Information Sciences, vol. 187, no. 1, pp. 1–14, 2012, doi: 10.1016/j.ins.2011.10.020.
• C. C. Aggarwal, “Neighborhood-Based Collaborative Filtering,” in Recommender Systems, 2016, pp. 29–70.