Research Article
BibTex RIS Cite

Effects of neighborhood-based collaborative filtering parameters on their blockbuster bias performances

Year 2022, , 157 - 168, 31.08.2022

Emre Yalçın

https://doi.org/10.35377/saucis...1065794

Abstract

Collaborative filtering algorithms are efficient tools for providing recommendations with reasonable accuracy performances to individuals. However, the previous research has realized that these algorithms are undesirably biased towards blockbuster items. i.e., both popular and highly-liked items, in their recommendations, resulting in recommendation lists dominated by such blockbuster items. As one most prominent types of collaborative filtering approaches, neighborhood-based algorithms aim to produce recommendations based on neighborhoods constructed based on similarities between users or items. Therefore, the utilized similarity function and the size of the neighborhoods are critical parameters on their recommendation performances. This study considers three well-known similarity functions, i.e., Pearson, Cosine, and Mean Squared Difference, and varying neighborhood sizes and observes how they affect the algorithms’ blockbuster bias and accuracy performances. The extensive experiments conducted on two benchmark data collections conclude that as the size of neighborhoods decreases, these algorithms generally become more vulnerable to blockbuster bias while their accuracy increases. The experimental works also show that using the Cosine metric is superior to other similarity functions in producing recommendations where blockbuster bias is treated more; however, it leads to having unqualified recommendations in terms of predictive accuracy as they are usually conflicting goals.

Keywords

Recommender systems neighborhood-based collaborative filtering blockbuster bias similarity function neighborhood size

Project Number

M-2021-811

References

  • [1] F. Ricci, L. Rokach, and B. Shapira, “Introduction to recommender systems handbook,” in Recommender systems handbook, Springer, 2011, pp. 1–35.
  • [2] Z. Batmaz, A. Yurekli, A. Bilge, and C. Kaleli, “A review on deep learning for recommender systems: challenges and remedies,” Artif. Intell. Rev., vol. 52, no. 1–37, 2019, doi: 10.1007/s10462-018-9654-y.
  • [3] 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, doi: https://doi.org/10.1145/963770.963772.
  • [4] J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez, “Recommender systems survey,” Knowledge-Based Syst., vol. 46, pp. 109–132, 2013, doi: 10.1016/j.knosys.2013.03.012.
  • [5] R. Chen, Q. Hua, Y.-S. Chang, B. Wang, L. Zhang, and X. Kong, “A survey of collaborative filtering-based recommender systems: From traditional methods to hybrid methods based on social networks,” IEEE Access, vol. 6, pp. 64301–64320, 2018, [Online]. Available: https://doi.org/10.1109/ACCESS.2018.2877208.
  • [6] M. Karimi, D. Jannach, and M. Jugovac, “News recommender systems – Survey and roads ahead,” Inf. Process. \& Manag., vol. 54, no. 6, pp. 1203–1227, 2018, [Online]. Available: https://doi.org/10.1016/j.ipm.2018.04.008.
  • [7] M. Nilashi, O. Bin Ibrahim, and N. Ithnin, “Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and Neuro-Fuzzy system,” Knowledge-Based Syst., 2014, doi: 10.1016/j.knosys.2014.01.006.
  • [8] J. L. Sánchez, F. Serradilla, E. Martínez, and J. Bobadilla, “Choice of metrics used in collaborative filtering and their impact on recommender systems,” 2008, doi: 10.1109/DEST.2008.4635147.
  • [9] Y. Koren, “Factor in the neighbors: Scalable and accurate collaborative filtering,” ACM Trans. Knowl. Discov. from Data, vol. 4, no. 1, pp. 1–24, 2010, [Online]. Available: https://doi.org/10.1145/1644873.1644874.
  • [10] 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, [Online]. Available: https://doi.org/10.1016/j.knosys.2010.03.009.
  • [11] J. Herlocker, J. A. Konstan, and J. Riedl, “An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms,” Inf. Retr. Boston., 2002, doi: 10.1023/A:1020443909834.
  • [12] E. Yalcin, “Blockbuster: A New Perspective on Popularity-bias in Recommender Systems,” in 2021 6th International Conference on Computer Science and Engineering (UBMK), Sep. 2021, pp. 107–112, doi: 10.1109/UBMK52708.2021.9558877.
  • [13] J. Chen, H. Dong, X. Wang, F. Feng, M. Wang, and X. He, “Bias and Debias in Recommender System: A Survey and Future Directions,” arXiv Prepr. arXiv2010.03240, 2020.
  • [14] L. Boratto, G. Fenu, and M. Marras, “Connecting user and item perspectives in popularity debiasing for collaborative recommendation,” Inf. Process. \& Manag., vol. 58, no. 1, p. 102387, 2021, [Online]. Available: https://doi.org/10.1016/j.ipm.2020.102387.
  • [15] E. Yalcin and A. Bilge, “Treating adverse effects of blockbuster bias on beyond-accuracy quality of personalized recommendations,” Eng. Sci. Technol. an Int. J., vol. 33, p. 101083, Sep. 2022, doi: 10.1016/J.JESTCH.2021.101083.
  • [16] E. Yalcin, “Blockbuster: A New Perspective on Popularity-bias in Recommender Systems,” pp. 107–112, Oct. 2021, doi: 10.1109/UBMK52708.2021.9558877.
  • [17] T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay, “Accurately Interpreting Clickthrough Data as Implicit Feedback,” ACM SIGIR Forum, vol. 51, no. 1, pp. 4–11, Aug. 2017, doi: 10.1145/3130332.3130334.
  • [18] J. M. Hernández-Lobato, N. Houlsby, and Z. Ghahramani, “Probabilistic matrix factorization with non-random missing data,” in International Conference on Machine Learning, 2014, pp. 1512–1520.
  • [19] S. Krishnan, J. Patel, M. J. Franklin, and K. Goldberg, “A methodology for learning, analyzing, and mitigating social influence bias in recommender systems,” in Proceedings of the 8th ACM Conference on Recommender systems, 2014, pp. 137–144, doi: https://doi.org/10.1145/2645710.2645740.
  • [20] D. Jannach, L. Lerche, I. Kamehkhosh, and M. Jugovac, “What recommenders recommend: an analysis of recommendation biases and possible countermeasures,” User Model. User-adapt. Interact., vol. 25, no. 5, pp. 427–491, Dec. 2015, doi: 10.1007/S11257-015-9165-3.
  • [21] H. Abdollahpouri, R. Burke, and B. Mobasher, “Managing popularity bias in recommender systems with personalized re-ranking,” Proc. 32nd Int. Florida Artif. Intell. Res. Soc. Conf. FLAIRS 2019, pp. 413–418, 2019.
  • [22] E. Yalcin and A. Bilge, “Investigating and counteracting popularity bias in group recommendations,” Inf. Process. Manag., vol. 58, no. 5, Sep. 2021, doi: 10.1016/j.ipm.2021.102608.
  • [23] D. Kowald, M. Schedl, and E. Lex, “The unfairness of popularity bias in music recommendation: A reproducibility study,” in European Conference on Information Retrieval, 2020, pp. 35–42, [Online]. Available: https://doi.org/10.1007/978-3-030-45442-5_5.
  • [24] H. Abdollahpouri, M. Mansoury, R. Burke, and B. Mobasher, “The unfairness of popularity bias in recommendation,” 2019.
  • [25] L. Boratto, G. Fenu, and M. Marras, “The effect of algorithmic bias on recommender systems for massive open online courses,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11437 LNCS, pp. 457–472, 2019, doi: 10.1007/978-3-030-15712-8_30.
  • [26] C. Chen, M. Zhang, Y. Liu, and S. Ma, “Missing data modeling with user activity and item popularity in recommendation,” in Asia Information Retrieval Symposium, 2018, pp. 113–125, [Online]. Available: https://doi.org/10.1007/978-3-030-03520-4_11.
  • [27] T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma, “Correcting Popularity Bias by Enhancing Recommendation Neutrality,” 2014.
  • [28] H. Abdollahpouri, R. Burke, and B. Mobasher, “Popularity-Aware Item Weighting for Long-Tail Recommendation.” 2018.
  • [29] G. Adomavicius and Y. Kwon, “Multi-criteria recommender systems,” in Recommender Systems Handbook, Second Edition, 2015.
  • [30] N. A. Najjar and D. C. Wilson, “Differential neighborhood selection in memory-based group recommender systems,” 2014.
  • [31] Y. Koren, “Factor in the neighbors: Scalable and accurate collaborative filtering,” ACM Trans. Knowl. Discov. Data, vol. 4, no. 1, Jan. 2010, doi: 10.1145/1644873.1644874.
  • [32] K. Choi and Y. Suh, “A new similarity function for selecting neighbors for each target item in collaborative filtering,” Knowledge-Based Syst., 2013, doi: 10.1016/j.knosys.2012.07.019.
  • [33] R. Sanders, “The Pareto principle: its use and abuse,” J. Serv. Mark., 1987.
  • [34] L. Baltrunas and F. Ricci, “Group Recommendations with Rank Aggregation and,” Proc. fourth ACM Conf. Recomm. Syst. ACM, 2010.

Year 2022, , 157 - 168, 31.08.2022

Emre Yalçın

https://doi.org/10.35377/saucis...1065794

Abstract

Supporting Institution

Sivas Cumhuriyet University

Project Number

M-2021-811

References

  • [1] F. Ricci, L. Rokach, and B. Shapira, “Introduction to recommender systems handbook,” in Recommender systems handbook, Springer, 2011, pp. 1–35.
  • [2] Z. Batmaz, A. Yurekli, A. Bilge, and C. Kaleli, “A review on deep learning for recommender systems: challenges and remedies,” Artif. Intell. Rev., vol. 52, no. 1–37, 2019, doi: 10.1007/s10462-018-9654-y.
  • [3] 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, doi: https://doi.org/10.1145/963770.963772.
  • [4] J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez, “Recommender systems survey,” Knowledge-Based Syst., vol. 46, pp. 109–132, 2013, doi: 10.1016/j.knosys.2013.03.012.
  • [5] R. Chen, Q. Hua, Y.-S. Chang, B. Wang, L. Zhang, and X. Kong, “A survey of collaborative filtering-based recommender systems: From traditional methods to hybrid methods based on social networks,” IEEE Access, vol. 6, pp. 64301–64320, 2018, [Online]. Available: https://doi.org/10.1109/ACCESS.2018.2877208.
  • [6] M. Karimi, D. Jannach, and M. Jugovac, “News recommender systems – Survey and roads ahead,” Inf. Process. \& Manag., vol. 54, no. 6, pp. 1203–1227, 2018, [Online]. Available: https://doi.org/10.1016/j.ipm.2018.04.008.
  • [7] M. Nilashi, O. Bin Ibrahim, and N. Ithnin, “Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and Neuro-Fuzzy system,” Knowledge-Based Syst., 2014, doi: 10.1016/j.knosys.2014.01.006.
  • [8] J. L. Sánchez, F. Serradilla, E. Martínez, and J. Bobadilla, “Choice of metrics used in collaborative filtering and their impact on recommender systems,” 2008, doi: 10.1109/DEST.2008.4635147.
  • [9] Y. Koren, “Factor in the neighbors: Scalable and accurate collaborative filtering,” ACM Trans. Knowl. Discov. from Data, vol. 4, no. 1, pp. 1–24, 2010, [Online]. Available: https://doi.org/10.1145/1644873.1644874.
  • [10] 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, [Online]. Available: https://doi.org/10.1016/j.knosys.2010.03.009.
  • [11] J. Herlocker, J. A. Konstan, and J. Riedl, “An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms,” Inf. Retr. Boston., 2002, doi: 10.1023/A:1020443909834.
  • [12] E. Yalcin, “Blockbuster: A New Perspective on Popularity-bias in Recommender Systems,” in 2021 6th International Conference on Computer Science and Engineering (UBMK), Sep. 2021, pp. 107–112, doi: 10.1109/UBMK52708.2021.9558877.
  • [13] J. Chen, H. Dong, X. Wang, F. Feng, M. Wang, and X. He, “Bias and Debias in Recommender System: A Survey and Future Directions,” arXiv Prepr. arXiv2010.03240, 2020.
  • [14] L. Boratto, G. Fenu, and M. Marras, “Connecting user and item perspectives in popularity debiasing for collaborative recommendation,” Inf. Process. \& Manag., vol. 58, no. 1, p. 102387, 2021, [Online]. Available: https://doi.org/10.1016/j.ipm.2020.102387.
  • [15] E. Yalcin and A. Bilge, “Treating adverse effects of blockbuster bias on beyond-accuracy quality of personalized recommendations,” Eng. Sci. Technol. an Int. J., vol. 33, p. 101083, Sep. 2022, doi: 10.1016/J.JESTCH.2021.101083.
  • [16] E. Yalcin, “Blockbuster: A New Perspective on Popularity-bias in Recommender Systems,” pp. 107–112, Oct. 2021, doi: 10.1109/UBMK52708.2021.9558877.
  • [17] T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay, “Accurately Interpreting Clickthrough Data as Implicit Feedback,” ACM SIGIR Forum, vol. 51, no. 1, pp. 4–11, Aug. 2017, doi: 10.1145/3130332.3130334.
  • [18] J. M. Hernández-Lobato, N. Houlsby, and Z. Ghahramani, “Probabilistic matrix factorization with non-random missing data,” in International Conference on Machine Learning, 2014, pp. 1512–1520.
  • [19] S. Krishnan, J. Patel, M. J. Franklin, and K. Goldberg, “A methodology for learning, analyzing, and mitigating social influence bias in recommender systems,” in Proceedings of the 8th ACM Conference on Recommender systems, 2014, pp. 137–144, doi: https://doi.org/10.1145/2645710.2645740.
  • [20] D. Jannach, L. Lerche, I. Kamehkhosh, and M. Jugovac, “What recommenders recommend: an analysis of recommendation biases and possible countermeasures,” User Model. User-adapt. Interact., vol. 25, no. 5, pp. 427–491, Dec. 2015, doi: 10.1007/S11257-015-9165-3.
  • [21] H. Abdollahpouri, R. Burke, and B. Mobasher, “Managing popularity bias in recommender systems with personalized re-ranking,” Proc. 32nd Int. Florida Artif. Intell. Res. Soc. Conf. FLAIRS 2019, pp. 413–418, 2019.
  • [22] E. Yalcin and A. Bilge, “Investigating and counteracting popularity bias in group recommendations,” Inf. Process. Manag., vol. 58, no. 5, Sep. 2021, doi: 10.1016/j.ipm.2021.102608.
  • [23] D. Kowald, M. Schedl, and E. Lex, “The unfairness of popularity bias in music recommendation: A reproducibility study,” in European Conference on Information Retrieval, 2020, pp. 35–42, [Online]. Available: https://doi.org/10.1007/978-3-030-45442-5_5.
  • [24] H. Abdollahpouri, M. Mansoury, R. Burke, and B. Mobasher, “The unfairness of popularity bias in recommendation,” 2019.
  • [25] L. Boratto, G. Fenu, and M. Marras, “The effect of algorithmic bias on recommender systems for massive open online courses,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11437 LNCS, pp. 457–472, 2019, doi: 10.1007/978-3-030-15712-8_30.
  • [26] C. Chen, M. Zhang, Y. Liu, and S. Ma, “Missing data modeling with user activity and item popularity in recommendation,” in Asia Information Retrieval Symposium, 2018, pp. 113–125, [Online]. Available: https://doi.org/10.1007/978-3-030-03520-4_11.
  • [27] T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma, “Correcting Popularity Bias by Enhancing Recommendation Neutrality,” 2014.
  • [28] H. Abdollahpouri, R. Burke, and B. Mobasher, “Popularity-Aware Item Weighting for Long-Tail Recommendation.” 2018.
  • [29] G. Adomavicius and Y. Kwon, “Multi-criteria recommender systems,” in Recommender Systems Handbook, Second Edition, 2015.
  • [30] N. A. Najjar and D. C. Wilson, “Differential neighborhood selection in memory-based group recommender systems,” 2014.
  • [31] Y. Koren, “Factor in the neighbors: Scalable and accurate collaborative filtering,” ACM Trans. Knowl. Discov. Data, vol. 4, no. 1, Jan. 2010, doi: 10.1145/1644873.1644874.
  • [32] K. Choi and Y. Suh, “A new similarity function for selecting neighbors for each target item in collaborative filtering,” Knowledge-Based Syst., 2013, doi: 10.1016/j.knosys.2012.07.019.
  • [33] R. Sanders, “The Pareto principle: its use and abuse,” J. Serv. Mark., 1987.
  • [34] L. Baltrunas and F. Ricci, “Group Recommendations with Rank Aggregation and,” Proc. fourth ACM Conf. Recomm. Syst. ACM, 2010.
There are 34 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence, Computer Software
Journal Section Articles
Authors

Emre Yalçın 0000-0003-3818-6712

Project Number M-2021-811
Publication Date August 31, 2022
Submission Date January 31, 2022
Acceptance Date June 15, 2022
Published in Issue Year 2022

Cite

IEEE E. Yalçın, “Effects of neighborhood-based collaborative filtering parameters on their blockbuster bias performances”, SAUCIS, vol. 5, no. 2, pp. 157–168, 2022, doi: 10.35377/saucis...1065794.

29070    The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License