SavedAdd your journal

Journals / Advances in Artificial Intelligence Research / 2024 / Cilt: 4 Sayı: 2

Comparison of The Performances of Clustering and Dimensionality Reduction Approaches in Collaborative Filtering

Özge Taş(Kapadokya Üniversitesi)0000-0001-7220-5054
Pages
96–110
Publication Date
2024-12-30
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This study aims to compare the performances of eleven clustering techniques of eleven clustering techniques, four of which are non-hierarchical seven of which are hierarchical clustering algorithms, and two variable dimensionality reduction techniques, consisting of SVD and PCA METHODS, in CF.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Recommendation systems (RS) can be defined as systems that aim to offer personalized product and service recommendations to users based on users' past product preferences and similarities with other users in the system, especially in systems that provide e-commerce services.
📊
Key Findings & Data:Content-based recommendation systems focus on analyzing the attributes of items such as articles, movies or music to generate tailored recommendations.
💡
Conclusion & Impact:This study aims to compare the performances of eleven clustering techniques of eleven clustering techniques, four of which are non-hierarchical seven of which are hierarchical clustering algorithms, and two variable dimensionality reduction techniques, consisting of SVD and PCA METHODS, in CF.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Recommender Systems · Collaborative Filtering · Cluster Analysis

Abstract

Recommendation systems (RS) can be defined as systems that aim to offer personalized product and service recommendations to users based on users' past product preferences and similarities with other users in the system, especially in systems that provide e-commerce services. The main purpose of RS is to reveal meaningful information from large-scale data to users and to recommend systems that aim to simplify the analysis of user behaviors and product attributes. It is possible to divide the techniques used in RS into two main categories content-based and collaborative filtering (CF) according to the information they receive as input. Content-based recommendation systems focus on analyzing the attributes of items such as articles, movies or music to generate tailored recommendations. CF methods analyze user-generated scores for products and services to identify patterns and preferences. The success of CF techniques hinges on accurately identifying user similarities within large datasets. However, in CF techniques, large-scale data sets consisting of a large number of users and the scores given by users to these products are used. Consequently, identifying user similarities in such extensive datasets poses significant challenges. Two different methods are used to overcome this problem. The first method applies clustering analysis to divide the dataset into smaller subsets (user or product), followed by the application of CF techniques. In the other method, dimensionality reduction is performed on a product (object) basis using Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) methods. Up to now, many studies have been carried out using clustering analysis and variable dimensionality reduction methods Despite extensive research, a thorough comparison of clustering and dimensionality reduction methods on real-world datasets remains unexplored. This study aims to compare the performances of eleven clustering techniques of eleven clustering techniques, four of which are non-hierarchical seven of which are hierarchical clustering algorithms, and two variable dimensionality reduction techniques, consisting of SVD and PCA METHODS, in CF.

Özet

Recommendation systems (RS) can be defined as systems that aim to offer personalized product and service recommendations to users based on users' past product preferences and similarities with other users in the system, especially in systems that provide e-commerce services. The main purpose of RS is to reveal meaningful information from large-scale data to users and to recommend systems that aim to simplify the analysis of user behaviors and product attributes. It is possible to divide the techniques used in RS into two main categories content-based and collaborative filtering (CF) according to the information they receive as input. Content-based recommendation systems focus on analyzing the attributes of items such as articles, movies or music to generate tailored recommendations. CF methods analyze user-generated scores for products and services to identify patterns and preferences. The success of CF techniques hinges on accurately identifying user similarities within large datasets. However, in CF techniques, large-scale data sets consisting of a large number of users and the scores given by users to these products are used. Consequently, identifying user similarities in such extensive datasets poses significant challenges. Two different methods are used to overcome this problem. The first method applies clustering analysis to divide the dataset into smaller subsets (user or product), followed by the application of CF techniques. In the other method, dimensionality reduction is performed on a product (object) basis using Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) methods. Up to now, many studies have been carried out using clustering analysis and variable dimensionality reduction methods Despite extensive research, a thorough comparison of clustering and dimensionality reduction methods on real-world datasets remains unexplored. This study aims to compare the performances of eleven clustering techniques of eleven clustering techniques, four of which are non-hierarchical seven of which are hierarchical clustering algorithms, and two variable dimensionality reduction techniques, consisting of SVD and PCA METHODS, in CF.

🔖

Cite This Article

Taş, Ö. (2024). Comparison of The Performances of Clustering and Dimensionality Reduction Approaches in Collaborative Filtering. Advances in Artificial Intelligence Research, Cilt: 4 Sayı: 2, 96–110. https://doi.org/10.54569/aair.1597930

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (33)

  1. Cai, D., Wang, X., & He, X. (2009, June). Probabilistic dyadic data analysis with local and global consistency. In Proceedings of the 26th annual international conference on machine learning (pp. 105 112).
  2. George T, Merugu S., (2005), A scalable collaborative filtering framework based on co-clustering. In Proc. the 5th IEEE Int. Conf. Data Mining, Nov. pp.625-628.
  3. Hastie,T,R.Tibshirani and J. Friedman (2009). The Elements Of Statistical Learning: datamining, inference and prediction (2 ed.). Springer, pp 745.
  4. Heckerman D., Chickering D., Meek C., Rounthwaite R. and Kadie C., (2001) Dependency networks for inference, collaborative filtering, and data visualization. The Journal of Machine Learning Research, 1:49–75.
  5. MacQueen, J. B., (1967), Some Methods for Classification and Analysis of Multivariate Observations, Proc. Symp. Math. Statist. and Probability (5th), 281– 297.
  6. Şenol, A., Kaya, M. ve Canbay, Y. (2024). Akan veri kümeleme probleminde ağaç veri yapılarının performans karşılaştırması. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 39 (1), 217-232.
  7. Groth, D., Hartmann, S., Klie, S. ve Selbig, J. (2013). Başlıca Bileşenler analizi. Hesaplamalı Toksikoloji: Cilt II, 527-547.
  8. Bakır, Ç., & Albayrak, S. (2014, April). User based and item based collaborative filtering with temporal dynamics. In 2014 22nd Signal Processing and Communications Applications Conference (Siu) (pp. 252-255). IEEE.
  9. Sarwar B., Karypis G., Konstan J. and Riedl J., (2001) Item-based Collaborative Filtering Recommendation Algorithms. In Proceedings of the 10th International Conference on World Wide Web (WWW ’01). ACM, 285– 295. DOI:http://dx.doi.org/10.1145/371920.372071.
  10. Xu R,Wunsch D., (2005). Survey Of Clustering Algorithms, IEEE Transactionson Neural Networks, 16(3):645–678.
  11. Altinisik, A., Yildirim, U., & Topcu, Y. I. (2022). Evaluation of failure risks for manual tightening operations in automotive assembly lines. Assembly Automation, 42(5), 653-676.
  12. Koohi, H., Kiani, K. (2016), User based collaborative filtering using fuzzy c-means, Measurement, 91:134-139.
  13. Chen, J., Wang, H., & Yan, Z. (2018). Evolutionary heterogeneous clustering for rating prediction based on user collaborative filtering. Swarm and Evolutionary Computation, 38, 35-41.
  14. Liao, C.L., Lee, S.J. (2016) A clustering based approach to improving the efficiency of collaborative filtering recommendation, Electronic Commerce Research and Applications,18:1-9.
  15. Ba, J. ve Frey, B. (2013). Derin sinir ağlarını eğitmek için uyarlanabilir bırakma. Sinirsel bilgi işleme sistemlerindeki gelişmeler, 26.Chicago
  16. Roelofsen, P. (2018), Time Series Clustering, Master Thesis, Vrıje Unıversıteıt, Amsterdam, 83s.
  17. Kaufman, L. ve Rousseeuw, PJ (2009). Verilerde grupları bulma: kümeleme analizine giriş. John Wiley & Sons.
  18. Kohonen T. (1995) Learning Vector Quantization. In: Self-Organizing Maps. Springer Series in Information Sciences, vol 30. Springer, Berlin, Heidelberg pp 175-189.
  19. X. Zhang, D. Rajan, and B. Story, “Concrete crack detection using context-aware deep semantic segmentation network,” Computer-Aided Civil and Infrastructure Engineering, 34(11) (2019) 951–971; https://doi.org/10.1111/mice.12477.
  20. Konstan, J.A., Riedl, J. (2012) Recommender systems: from algorithms to user experience, Adapt Interact 22: 101–23.
  21. Pan, C., Li. W. (2010) Research paper recommendation with topic analysis. In Computer Design and Applications IEEE 4, pp V4-264.
  22. Konstan J.A., Miller B.N., Maltz D., Herlocker J.L., Gordon L.R., Riedl J., (1997), Applying collaborative filtering to Usenet news.Commun ACM; 40(3):77-87.
  23. Link 1, (https://www.kaggle.com/datasets), (Jester Collaborative Filtering Dataset), (Restoran_tavsiye_sistemi), (Recommendation System (CF) | Anime ),01.08.2023
  24. Link 2, https://github.com/Ramakrishna05/Recommendation-Algorithm, 01.08.2023.
  25. Link 3, Web: https://bookdown.org/egarpor/PM-UC3M/lm-ii-dimred.html
  26. Hastie,T,R.Tibshirani and J. Friedman (2009). The Elements Of Statistical Learning: datamining, inference and prediction (2 ed.). Springer, pp 745.
  27. Şenol, A., Kaya, M. ve Canbay, Y. (2024). Akan veri kümeleme probleminde ağaç veri yapılarının performans karşılaştırması. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 39 (1), 217-232.
  28. Xu R,Wunsch D., (2005). Survey Of Clustering Algorithms, IEEE Transactionson Neural Networks, 16(3):645–678.
  29. Ba, J. ve Frey, B. (2013). Derin sinir ağlarını eğitmek için uyarlanabilir bırakma. Sinirsel bilgi işleme sistemlerindeki gelişmeler, 26.Chicago
  30. Kaufman, L. ve Rousseeuw, PJ (2009). Verilerde grupları bulma: kümeleme analizine giriş. John Wiley & Sons.
  31. Konstan, J.A., Riedl, J. (2012) Recommender systems: from algorithms to user experience, Adapt Interact 22: 101–23.
  32. Pan, C., Li. W. (2010) Research paper recommendation with topic analysis. In Computer Design and Applications IEEE 4, pp V4-264.
  33. Link 1, (https://www.kaggle.com/datasets), (Jester Collaborative Filtering Dataset), (Restoran_tavsiye_sistemi), (Recommendation System (CF) | Anime ),01.08.2023