Araştırma Makalesi
BibTex RIS Kaynak Göster

Yıl 2021, Cilt: 4 Sayı: 3, 287 - 301, 31.12.2021

Burcu Demirelli Okkalıoğlu

https://doi.org/10.35377/saucis...953348

Öz

Kaynakça

  • 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, Dec. 1992, doi: 10.1145/138859.138867.
  • M. Jalili, S. Ahmadian, M. Izadi, P. Moradi, and M. Salehi, “Evaluating Collaborative Filtering Recommender Algorithms: A Survey,” IEEE Access, vol. 6, pp. 74003–74024, 2018, doi: 10.1109/ACCESS.2018.2883742.
  • S. Chen and Y. Peng, “Matrix factorization for recommendation with explicit and implicit feedback,” Knowledge-Based Systems, vol. 158, pp. 109–117, Oct. 2018, doi: 10.1016/j.knosys.2018.05.040.
  • 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, Feb. 2012, doi: 10.1016/j.knosys.2011.07.021.
  • C. C. Aggarwal, Recommender Systems: The Textbook, 1st ed. 2016. Cham: Springer International Publishing: Imprint: Springer, 2016.
  • G. Adomavicius and Y. Kwon, “New Recommendation Techniques for Multicriteria Rating Systems,” IEEE Intell. Syst., vol. 22, no. 3, pp. 48–55, May 2007, doi: 10.1109/MIS.2007.58.
  • 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.
  • 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.
  • N. Polatidis and C. K. Georgiadis, “A dynamic multi-level collaborative filtering method for improved recommendations,” Computer Standards & Interfaces, vol. 51, pp. 14–21, Mar. 2017, doi: 10.1016/j.csi.2016.10.014.
  • L. Candillier, F. Meyer, and F. Fessant, “Designing Specific Weighted Similarity Measures to Improve Collaborative Filtering Systems,” in Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects, 2008, pp. 242–255.
  • E. Sadikoğlu and B. D. Okkalioğlu, “Increasing Prediction Performance Using Weighting Methods in Multi-Criteria Item-Based Collaborative Filtering,” European Journal of Science and Technology, pp. 110–121, Aug. 2020, doi: 10.31590/ejosat.779171.
  • E. Yalçin and A. Bi̇lge, “Binary multicriteria collaborative filtering,” Turk J Elec Eng & Comp Sci, vol. 28, no. 6, pp. 3419–3437, Nov. 2020.
  • M. Plantié, J. Montmain, and G. Dray, “Movies Recommenders Systems: Automation of the Information and Evaluation Phases in a Multi-criteria Decision-Making Process,” in Database and Expert Systems Applications, 2005, pp. 633–644.
  • A. Bilge and C. Kaleli, “A multi-criteria item-based collaborative filtering framework,” in 2014 11th International Joint Conference on Computer Science and Software Engineering (JCSSE), Chon Buri, May 2014, pp. 18–22. doi: 10.1109/JCSSE.2014.6841835.
  • Q. Shambour and J. Lu, "A Hybrid Multi-criteria Semantic-Enhanced Collaborative Filtering Approach for Personalized Recommendations," 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 2011, pp. 71-78, doi: 10.1109/WI-IAT.2011.109.
  • Q. Shambour, M. Hourani, and S. Fraihat, “An Item-based Multi-Criteria Collaborative Filtering Algorithm for Personalized Recommender Systems,” International Journal of Advanced Computer Science and Applications, vol. 7, no. 8, 2016, doi: 10.14569/IJACSA.2016.070837.
  • Q. Shambour, “A user-based multi-criteria recommendation approach for personalized recommendations,” International Journal of Computer Science and Information Security, vol. 14, no. 12, p. 657, 2016.
  • 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.
  • 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.
  • J. Wei, J. He, K. Chen, Y. Zhou, and Z. Tang, “Collaborative filtering and deep learning based recommendation system for cold start items,” Expert Systems with Applications, vol. 69, pp. 29–39, Mar. 2017, doi: 10.1016/j.eswa.2016.09.040.
  • H.-J. Kwon, T.-H. Lee, J.-H. Kim, and K.-S. Hong, “Improving Prediction Accuracy Using Entropy Weighting in Collaborative Filtering,” in 2009 Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, Jul. 2009, pp. 40–45. doi: 10.1109/UIC-ATC.2009.50.
  • M. Ghazanfar and A. Prugel-Bennett, “Novel Significance Weighting Schemes for Collaborative Filtering: Generating Improved Recommendations in Sparse Environments,” presented at the DMIN’10, the 2010 International Conference on Data Mining!, Jul. 12, 2010. Accessed: Jun. 02, 2021. [Online]. Available: https://eprints.soton.ac.uk/270846/
  • J. Han and M. Kamber, Data mining. Burlington, MA: Elsevier, 2012.
  • S. Choi and S. Cha, “A survey of Binary similarity and distance measures,” Journal of Systemics, Cybernetics and Informatics, pp. 43–48, 2010.
  • J. Podani, “Distance, similarity, correlation…”, in Introduction to the exploration of multivariate biological data. Leiden: Backhuys Publishers, 2000.
  • S.-H. Cha, C. Tappert, and S. Yoon, “Enhancing binary feature vector similarity measures,” J. Pattern Recognit. Res., vol. 1, no. 1, pp. 63–77, 2006. doi: 10.13176/11.20.
  • D. Jannach, Z. Karakaya, and F. Gedikli, “Accuracy improvements for multi-criteria recommender systems,” in Proceedings of the 13th ACM Conference on Electronic Commerce - EC ’12, Valencia, Spain, 2012, p. 674. doi: 10.1145/2229012.2229065.

Effects of Binary Vectors Similarities on the Accuracy of Multi-Criteria Collaborative Filtering

Yıl 2021, Cilt: 4 Sayı: 3, 287 - 301, 31.12.2021

Burcu Demirelli Okkalıoğlu

https://doi.org/10.35377/saucis...953348

Öz

Recommender systems offer tailored recommendations by employing various algorithms, and collaborative filtering is one of the well-known and commonly used of those. A traditional collaborative filtering system allows users to rate on a single criterion. However, a single criterion may be insufficient to indicate preferences in domains such as restaurants, movies, or tourism. Multi-criteria collaborative filtering provides a multi-dimensional rating option. In similarity-based multi-criteria collaborative filtering schemes, existing similarity methods utilize co-users or co-items regardless of how many there are. However, a high correlation with a few co-ratings does not always provide a reliable neighborhood. Therefore, it is very common, in both single- and multi-criteria collaborative filtering, to weight similarities with functions utilizing the number of co-ratings. Since multi-criteria collaborative filtering is yet growing, it lacks a comprehensive view of the effects of similarity weighting. This work studies multi-criteria collaborative filtering and the literature of binary vector similarities, which are frequently used for weighting, by giving a related taxonomy and conducts extensive experiments to analyze the effects of weighting similarities on item- and user-based multi-criteria collaborative filtering. Experimental findings suggest that prediction accuracy of item-based multi-criteria collaborative filtering can be boosted by especially binary vector similarity measures which do not consider mutual absences.

Anahtar Kelimeler

multi-criteria collaborative filtering similarity-weighting binary vector similarity

Kaynakça

  • 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, Dec. 1992, doi: 10.1145/138859.138867.
  • M. Jalili, S. Ahmadian, M. Izadi, P. Moradi, and M. Salehi, “Evaluating Collaborative Filtering Recommender Algorithms: A Survey,” IEEE Access, vol. 6, pp. 74003–74024, 2018, doi: 10.1109/ACCESS.2018.2883742.
  • S. Chen and Y. Peng, “Matrix factorization for recommendation with explicit and implicit feedback,” Knowledge-Based Systems, vol. 158, pp. 109–117, Oct. 2018, doi: 10.1016/j.knosys.2018.05.040.
  • 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, Feb. 2012, doi: 10.1016/j.knosys.2011.07.021.
  • C. C. Aggarwal, Recommender Systems: The Textbook, 1st ed. 2016. Cham: Springer International Publishing: Imprint: Springer, 2016.
  • G. Adomavicius and Y. Kwon, “New Recommendation Techniques for Multicriteria Rating Systems,” IEEE Intell. Syst., vol. 22, no. 3, pp. 48–55, May 2007, doi: 10.1109/MIS.2007.58.
  • 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.
  • 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.
  • N. Polatidis and C. K. Georgiadis, “A dynamic multi-level collaborative filtering method for improved recommendations,” Computer Standards & Interfaces, vol. 51, pp. 14–21, Mar. 2017, doi: 10.1016/j.csi.2016.10.014.
  • L. Candillier, F. Meyer, and F. Fessant, “Designing Specific Weighted Similarity Measures to Improve Collaborative Filtering Systems,” in Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects, 2008, pp. 242–255.
  • E. Sadikoğlu and B. D. Okkalioğlu, “Increasing Prediction Performance Using Weighting Methods in Multi-Criteria Item-Based Collaborative Filtering,” European Journal of Science and Technology, pp. 110–121, Aug. 2020, doi: 10.31590/ejosat.779171.
  • E. Yalçin and A. Bi̇lge, “Binary multicriteria collaborative filtering,” Turk J Elec Eng & Comp Sci, vol. 28, no. 6, pp. 3419–3437, Nov. 2020.
  • M. Plantié, J. Montmain, and G. Dray, “Movies Recommenders Systems: Automation of the Information and Evaluation Phases in a Multi-criteria Decision-Making Process,” in Database and Expert Systems Applications, 2005, pp. 633–644.
  • A. Bilge and C. Kaleli, “A multi-criteria item-based collaborative filtering framework,” in 2014 11th International Joint Conference on Computer Science and Software Engineering (JCSSE), Chon Buri, May 2014, pp. 18–22. doi: 10.1109/JCSSE.2014.6841835.
  • Q. Shambour and J. Lu, "A Hybrid Multi-criteria Semantic-Enhanced Collaborative Filtering Approach for Personalized Recommendations," 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 2011, pp. 71-78, doi: 10.1109/WI-IAT.2011.109.
  • Q. Shambour, M. Hourani, and S. Fraihat, “An Item-based Multi-Criteria Collaborative Filtering Algorithm for Personalized Recommender Systems,” International Journal of Advanced Computer Science and Applications, vol. 7, no. 8, 2016, doi: 10.14569/IJACSA.2016.070837.
  • Q. Shambour, “A user-based multi-criteria recommendation approach for personalized recommendations,” International Journal of Computer Science and Information Security, vol. 14, no. 12, p. 657, 2016.
  • 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.
  • 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.
  • J. Wei, J. He, K. Chen, Y. Zhou, and Z. Tang, “Collaborative filtering and deep learning based recommendation system for cold start items,” Expert Systems with Applications, vol. 69, pp. 29–39, Mar. 2017, doi: 10.1016/j.eswa.2016.09.040.
  • H.-J. Kwon, T.-H. Lee, J.-H. Kim, and K.-S. Hong, “Improving Prediction Accuracy Using Entropy Weighting in Collaborative Filtering,” in 2009 Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, Jul. 2009, pp. 40–45. doi: 10.1109/UIC-ATC.2009.50.
  • M. Ghazanfar and A. Prugel-Bennett, “Novel Significance Weighting Schemes for Collaborative Filtering: Generating Improved Recommendations in Sparse Environments,” presented at the DMIN’10, the 2010 International Conference on Data Mining!, Jul. 12, 2010. Accessed: Jun. 02, 2021. [Online]. Available: https://eprints.soton.ac.uk/270846/
  • J. Han and M. Kamber, Data mining. Burlington, MA: Elsevier, 2012.
  • S. Choi and S. Cha, “A survey of Binary similarity and distance measures,” Journal of Systemics, Cybernetics and Informatics, pp. 43–48, 2010.
  • J. Podani, “Distance, similarity, correlation…”, in Introduction to the exploration of multivariate biological data. Leiden: Backhuys Publishers, 2000.
  • S.-H. Cha, C. Tappert, and S. Yoon, “Enhancing binary feature vector similarity measures,” J. Pattern Recognit. Res., vol. 1, no. 1, pp. 63–77, 2006. doi: 10.13176/11.20.
  • D. Jannach, Z. Karakaya, and F. Gedikli, “Accuracy improvements for multi-criteria recommender systems,” in Proceedings of the 13th ACM Conference on Electronic Commerce - EC ’12, Valencia, Spain, 2012, p. 674. doi: 10.1145/2229012.2229065.
Toplam 28 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Yazılımı
Bölüm Makaleler
Yazarlar

Burcu Demirelli Okkalıoğlu 0000-0003-2867-4667

Yayımlanma Tarihi 31 Aralık 2021
Gönderilme Tarihi 16 Haziran 2021
Kabul Tarihi 19 Eylül 2021
Yayımlandığı Sayı Yıl 2021Cilt: 4 Sayı: 3

Kaynak Göster

IEEE B. Demirelli Okkalıoğlu, “Effects of Binary Vectors Similarities on the Accuracy of Multi-Criteria Collaborative Filtering”, SAUCIS, c. 4, sy. 3, ss. 287–301, 2021, doi: 10.35377/saucis...953348.
 Kapak Resmi İndir

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