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Year 2020, Volume: 3 Issue: 2, 74 - 88, 28.08.2020

Burcu Demirelli Okkalıoğlu

https://doi.org/10.35377/saucis.03.02.714969

Abstract

References

  • J. Bobadilla, F. Ortega, A. Hernando and A. Gutiérrez, "Recommender systems survey", Knowledge-Based Systems, vol. 46, pp. 109-132, 2013. Available: 10.1016/j.knosys.2013.03.012.
  • 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. Available: 10.1109/mic.2003.1167344.
  • C. Gomez-Uribe and N. Hunt, "The Netflix Recommender System: Algorithms, Business Value, and Innovation ", ACM Transactions on Management Information Systems, vol. 6, no. 4, pp. 1-19, 2016. Available: 10.1145/2843948.
  • J. Pérez-Marcos and V. López Batista, “Recommender System Based on Collaborative Filtering for Spotify’s Users,” in Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017, Cham, 2018, pp. 214–220, doi: 10.1007/978-3-319-61578-3_22.
  • P. B. Thorat, R. M. Goudar, and S. Barve, “Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System,” IJCA, vol. 110, no. 4, pp. 31–36, Jan. 2015, doi: 10.5120/19308-0760.
  • P. Messina, V. Dominguez, D. Parra, C. Trattner, and A. Soto, “Content-based artwork recommendation: integrating painting metadata with neural and manually-engineered visual features,” User Model User-Adap Inter, vol. 29, no. 2, pp. 251–290, Apr. 2019, doi: 10.1007/s11257-018-9206-9.
  • P. Lops, D. Jannach, C. Musto, T. Bogers, and M. Koolen, “Trends in content-based recommendation: Preface to the special issue on Recommender systems based on rich item descriptions,” User Model User-Adap Inter, vol. 29, no. 2, pp. 239–249, Apr. 2019, doi: 10.1007/s11257-019-09231-w.
  • X. Su and T. M. Khoshgoftaar, “A Survey of Collaborative Filtering Techniques,” Advances in Artificial Intelligence, vol. 2009, pp. 1–19, 2009, doi: 10.1155/2009/421425.
  • 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, Jul. 2014, doi: 10.1145/2556270.
  • D. Kluver, M. D. Ekstrand, and J. A. Konstan, “Rating-Based Collaborative Filtering: Algorithms and Evaluation,” in Social Information Access, vol. 10100, P. Brusilovsky and D. He, Eds. Cham: Springer International Publishing, 2018, pp. 344–390.
  • 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, Nov. 2010, doi: 10.1016/j.ins.2010.07.024.
  • 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, Oct. 2017, doi: 10.1016/j.eswa.2017.04.046.
  • 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, Jun. 2005, doi: 10.1109/TKDE.2005.99.
  • J. Herlocker, J. A. Konstan, and J. Riedl, “An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms,” Information Retrieval, vol. 5, no. 4, pp. 287–310, Oct. 2002, doi: 10.1023/A:1020443909834.
  • C. Kaleli, “An entropy-based neighbor selection approach for collaborative filtering,” Knowledge-Based Systems, vol. 56, pp. 273–280, Jan. 2014, doi: 10.1016/j.knosys.2013.11.020.
  • Y. Koren, R. Bell, and C. Volinsky, “Matrix Factorization Techniques for Recommender Systems,” Computer, vol. 42, no. 8, pp. 30–37, Aug. 2009, doi: 10.1109/MC.2009.263.
  • L. M. de Campos, J. M. Fernández-Luna, J. F. Huete, and M. A. Rueda-Morales, “Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks,” International Journal of Approximate Reasoning, vol. 51, no. 7, pp. 785–799, Sep. 2010, doi: 10.1016/j.ijar.2010.04.001.
  • X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua, “Neural Collaborative Filtering,” in Proceedings of the 26th International Conference on World Wide Web - WWW ’17, Perth, Australia, 2017, pp. 173–182, doi: 10.1145/3038912.3052569.
  • J. Li et al., “Category Preferred Canopy–K-means based Collaborative Filtering algorithm,” Future Generation Computer Systems, vol. 93, pp. 1046–1054, Apr. 2019, doi: 10.1016/j.future.2018.04.025.
  • X. Ning, C. Desrosiers, and G. Karypis, “A Comprehensive Survey of Neighborhood-Based Recommendation Methods,” in Recommender Systems Handbook, F. Ricci, L. Rokach, and B. Shapira, Eds. Boston, MA: Springer US, 2015, pp. 37–76.
  • Y. Park, S. Park, W. Jung, and S. Lee, “Reversed CF: A fast collaborative filtering algorithm using a k-nearest neighbor graph,” Expert Systems with Applications, vol. 42, no. 8, pp. 4022–4028, May 2015, doi: 10.1016/j.eswa.2015.01.001.
  • D.-K. Chae, S.-C. Lee, S.-Y. Lee, and S.-W. Kim, “On identifying k -nearest neighbors in neighborhood models for efficient and effective collaborative filtering,” Neurocomputing, vol. 278, pp. 134–143, Feb. 2018, doi: 10.1016/j.neucom.2017.06.081.
  • H. Zeybek and C. Kaleli̇, “Dynamic k Neighbor Selection for Collaborative Filtering,” Anadolu university journal of science and technology A - Applied Sciences and Engineering, pp. 1–1, Mar. 2018, doi: 10.18038/aubtda.346407.
  • 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 - CSCW ’94, Chapel Hill, North Carolina, United States, 1994, pp. 175–186, doi: 10.1145/192844.192905.
  • 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, Mar. 1997, doi: 10.1145/245108.245126
  • B. Sarwar, G. Karypis, J. Konstan, and J. Reidl, “Item-based collaborative filtering recommendation algorithms,” in Proceedings of the tenth international conference on World Wide Web - WWW ’01, Hong Kong, Hong Kong, 2001, pp. 285–295, doi: 10.1145/371920.372071.
  • T.-H. Kim and S.-B. Yang, “An Effective Threshold-Based Neighbor Selection in Collaborative Filtering,” in Advances in Information Retrieval, vol. 4425, G. Amati, C. Carpineto, and G. Romano, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007, pp. 712–715.
  • H. Ma, I. King, and M. R. Lyu, “Effective missing data prediction for collaborative filtering,” in Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR ’07, Amsterdam, The Netherlands, 2007, p. 39, doi: 10.1145/1277741.1277751.
  • N. Polatidis and C. K. Georgiadis, “A multi-level collaborative filtering method that improves recommendations,” Expert Systems with Applications, vol. 48, pp. 100–110, Apr. 2016, doi: 10.1016/j.eswa.2015.11.023
  • J. S. Breese, D. Heckerman, and C. Kadie, “Empirical analysis of predictive algorithms for collaborative filtering,” in Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, Madison, Wisconsin, Jul. 1998, pp. 43–52.

Improving Prediction Performance of Dynamic Neighbor Selection in User-Based Collaborative Filtering

Year 2020, Volume: 3 Issue: 2, 74 - 88, 28.08.2020

Burcu Demirelli Okkalıoğlu

https://doi.org/10.35377/saucis.03.02.714969

Abstract

Recommender systems have become more and more popular in online environments in recent years. Although different approaches are introduced to build a powerful recommender system, collaborative filtering is one of the most used approaches in the recommender systems. Yet, researchers still introduce new methods to improve prediction performances in collaborative filtering. k nearest neighbor algorithm is one of the most dominant and prevalent one in collaborative filtering. The underlying approach behind it is to select a predefined k neighbors for an active user among all users. In the traditional algorithm, the value of k is constant and is determined before the prediction process. Recently, scholars proposed to use dynamic k neighbor selection for each user. Inspired from this work, we propose to improve prediction performance, accuracy and coverage, of collaborative filtering systems under k nearest neighbor approach. We first propose that users who rate the target item should become nominees for dynamic k neighbor selection instead of all possible users whose similarities can be calculated. The similarity calculation is the most crucial point of the k nearest neighbor algorithm. Furthermore, we also propose to use the significance-weighting approach in addition to the traditional Pearson correlation coefficient when identifying the best dynamic k neighbors for each user. The experimental results on the two well-known datasets show that the prediction accuracy and coverage improve in the dynamic k neighbor selection method by selecting neighbors among users who rated the target item and introducing the significance-weighting factor into the neighbor selection phase to find more eligible neighbors.

Keywords

dynamic k significance-weighting collaborative filtering accuracy coverage

References

  • J. Bobadilla, F. Ortega, A. Hernando and A. Gutiérrez, "Recommender systems survey", Knowledge-Based Systems, vol. 46, pp. 109-132, 2013. Available: 10.1016/j.knosys.2013.03.012.
  • 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. Available: 10.1109/mic.2003.1167344.
  • C. Gomez-Uribe and N. Hunt, "The Netflix Recommender System: Algorithms, Business Value, and Innovation ", ACM Transactions on Management Information Systems, vol. 6, no. 4, pp. 1-19, 2016. Available: 10.1145/2843948.
  • J. Pérez-Marcos and V. López Batista, “Recommender System Based on Collaborative Filtering for Spotify’s Users,” in Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017, Cham, 2018, pp. 214–220, doi: 10.1007/978-3-319-61578-3_22.
  • P. B. Thorat, R. M. Goudar, and S. Barve, “Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System,” IJCA, vol. 110, no. 4, pp. 31–36, Jan. 2015, doi: 10.5120/19308-0760.
  • P. Messina, V. Dominguez, D. Parra, C. Trattner, and A. Soto, “Content-based artwork recommendation: integrating painting metadata with neural and manually-engineered visual features,” User Model User-Adap Inter, vol. 29, no. 2, pp. 251–290, Apr. 2019, doi: 10.1007/s11257-018-9206-9.
  • P. Lops, D. Jannach, C. Musto, T. Bogers, and M. Koolen, “Trends in content-based recommendation: Preface to the special issue on Recommender systems based on rich item descriptions,” User Model User-Adap Inter, vol. 29, no. 2, pp. 239–249, Apr. 2019, doi: 10.1007/s11257-019-09231-w.
  • X. Su and T. M. Khoshgoftaar, “A Survey of Collaborative Filtering Techniques,” Advances in Artificial Intelligence, vol. 2009, pp. 1–19, 2009, doi: 10.1155/2009/421425.
  • 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, Jul. 2014, doi: 10.1145/2556270.
  • D. Kluver, M. D. Ekstrand, and J. A. Konstan, “Rating-Based Collaborative Filtering: Algorithms and Evaluation,” in Social Information Access, vol. 10100, P. Brusilovsky and D. He, Eds. Cham: Springer International Publishing, 2018, pp. 344–390.
  • 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, Nov. 2010, doi: 10.1016/j.ins.2010.07.024.
  • 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, Oct. 2017, doi: 10.1016/j.eswa.2017.04.046.
  • 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, Jun. 2005, doi: 10.1109/TKDE.2005.99.
  • J. Herlocker, J. A. Konstan, and J. Riedl, “An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms,” Information Retrieval, vol. 5, no. 4, pp. 287–310, Oct. 2002, doi: 10.1023/A:1020443909834.
  • C. Kaleli, “An entropy-based neighbor selection approach for collaborative filtering,” Knowledge-Based Systems, vol. 56, pp. 273–280, Jan. 2014, doi: 10.1016/j.knosys.2013.11.020.
  • Y. Koren, R. Bell, and C. Volinsky, “Matrix Factorization Techniques for Recommender Systems,” Computer, vol. 42, no. 8, pp. 30–37, Aug. 2009, doi: 10.1109/MC.2009.263.
  • L. M. de Campos, J. M. Fernández-Luna, J. F. Huete, and M. A. Rueda-Morales, “Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks,” International Journal of Approximate Reasoning, vol. 51, no. 7, pp. 785–799, Sep. 2010, doi: 10.1016/j.ijar.2010.04.001.
  • X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua, “Neural Collaborative Filtering,” in Proceedings of the 26th International Conference on World Wide Web - WWW ’17, Perth, Australia, 2017, pp. 173–182, doi: 10.1145/3038912.3052569.
  • J. Li et al., “Category Preferred Canopy–K-means based Collaborative Filtering algorithm,” Future Generation Computer Systems, vol. 93, pp. 1046–1054, Apr. 2019, doi: 10.1016/j.future.2018.04.025.
  • X. Ning, C. Desrosiers, and G. Karypis, “A Comprehensive Survey of Neighborhood-Based Recommendation Methods,” in Recommender Systems Handbook, F. Ricci, L. Rokach, and B. Shapira, Eds. Boston, MA: Springer US, 2015, pp. 37–76.
  • Y. Park, S. Park, W. Jung, and S. Lee, “Reversed CF: A fast collaborative filtering algorithm using a k-nearest neighbor graph,” Expert Systems with Applications, vol. 42, no. 8, pp. 4022–4028, May 2015, doi: 10.1016/j.eswa.2015.01.001.
  • D.-K. Chae, S.-C. Lee, S.-Y. Lee, and S.-W. Kim, “On identifying k -nearest neighbors in neighborhood models for efficient and effective collaborative filtering,” Neurocomputing, vol. 278, pp. 134–143, Feb. 2018, doi: 10.1016/j.neucom.2017.06.081.
  • H. Zeybek and C. Kaleli̇, “Dynamic k Neighbor Selection for Collaborative Filtering,” Anadolu university journal of science and technology A - Applied Sciences and Engineering, pp. 1–1, Mar. 2018, doi: 10.18038/aubtda.346407.
  • 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 - CSCW ’94, Chapel Hill, North Carolina, United States, 1994, pp. 175–186, doi: 10.1145/192844.192905.
  • 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, Mar. 1997, doi: 10.1145/245108.245126
  • B. Sarwar, G. Karypis, J. Konstan, and J. Reidl, “Item-based collaborative filtering recommendation algorithms,” in Proceedings of the tenth international conference on World Wide Web - WWW ’01, Hong Kong, Hong Kong, 2001, pp. 285–295, doi: 10.1145/371920.372071.
  • T.-H. Kim and S.-B. Yang, “An Effective Threshold-Based Neighbor Selection in Collaborative Filtering,” in Advances in Information Retrieval, vol. 4425, G. Amati, C. Carpineto, and G. Romano, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007, pp. 712–715.
  • H. Ma, I. King, and M. R. Lyu, “Effective missing data prediction for collaborative filtering,” in Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR ’07, Amsterdam, The Netherlands, 2007, p. 39, doi: 10.1145/1277741.1277751.
  • N. Polatidis and C. K. Georgiadis, “A multi-level collaborative filtering method that improves recommendations,” Expert Systems with Applications, vol. 48, pp. 100–110, Apr. 2016, doi: 10.1016/j.eswa.2015.11.023
  • J. S. Breese, D. Heckerman, and C. Kadie, “Empirical analysis of predictive algorithms for collaborative filtering,” in Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, Madison, Wisconsin, Jul. 1998, pp. 43–52.
There are 30 citations in total.

Details

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

Burcu Demirelli Okkalıoğlu 0000-0003-2867-4667

Publication Date August 28, 2020
Submission Date April 5, 2020
Acceptance Date July 17, 2020
Published in Issue Year 2020Volume: 3 Issue: 2

Cite

IEEE B. Demirelli Okkalıoğlu, “Improving Prediction Performance of Dynamic Neighbor Selection in User-Based Collaborative Filtering”, SAUCIS, vol. 3, no. 2, pp. 74–88, 2020, doi: 10.35377/saucis.03.02.714969.
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