Araştırma Makalesi
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Bibliometric Analysis of Recommender Systems Literature

Yıl 2024, Cilt: 12 Sayı: 3, 232 - 251, 31.12.2024
https://doi.org/10.22139/jobs.1476377

Öz

This study includes a bibliometric analysis of the literature on recommendation systems conducted over the past five years. Utilizing data obtained from the Web of Science (WoS) database, this research meticulously examines this field's development and turning points. Recommendation systems, which offer personalized content and product suggestions using user data, have gained significance with the widespread adoption of the internet and digital transactions. Rich data gathered through direct user feedback or methods such as eye-tracking technology are used to analyze user preferences and provide suitable recommendations. The research addresses significant milestones such as the GroupLens study, shedding light on the development of fundamental approaches like collaborative filtering and content-based filtering. Platforms like Google and Facebook employ these systems to analyze user interactions and predict future preferences. The bibliometric analysis, supported by visualizations created with VOSviewer, presents a detailed map of the frequently encountered terms in the recommendation systems literature and the relationships between these terms. Designed to guide those researching in this area, the study demonstrates the increasing scientific impact of recommendation systems. Bibliometric analysis provides a quantitative assessment of scientific publications, objectively measuring their scientific impact and quality. The analysis results show an increase over time in academic studies and citations within the field of recommendation systems, indicating a growing interest and influence in the area. Such an analysis can serve as a guide for future researchers on this topic and lay the groundwork for further development of recommendation systems. In conclusion, this work offers a comprehensive analysis of the recommendation systems literature, allowing for a deeper examination of scientific advancements in the research field.

Kaynakça

  • Aggarwal, C. C. (2016). Recommender Systems. https://doi.org/10.1007/978-3-319-29659-3
  • Albort-Morant, G., & Ribeiro-Soriano, D. (2016). A bibliometric analysis of international impact of business incubators. Journal of Business Research, 69(5), 1775–1779. https://doi.org/10.1016/J.JBUSRES.2015.10.054
  • Belkin, N. J., & Croft, W. B. (1992). Information filtering and Information retrieval: Two Sides of the Same Coin? Communications of the ACM, 35(12), 29–38. https://doi.org/10.1145/138859.138861
  • Berg, R. van den, Kipf, T. N., & Welling, M. (2017). Graph Convolutional Matrix Completion. https://arxiv.org/abs/1706.02263v2
  • Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109–132. https://doi.org/10.1016/j.knosys.2013.03.012
  • Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modelling and User-Adapted Interaction, 12(4), 331–370. https://doi.org/10.1023/A:1021240730564
  • Clarivate Inc., Essential Science Indicators Help. (2024, March 10). Essential Science Indicators Help. https://esi.help.clarivate.com/Content/home.htm
  • Cohen, W. W., Schapire, R. E., & Singer, Y. (1999). Learning to order things. Journal of Artificial Intelligence Research, 10.
  • De Leon-Martinez, S., Moro, R., & Bielikova, M. (2023). Eye Tracking as a Source of Implicit Feedback in Recommender Systems: A Preliminary Analysis. https://doi.org/10.1145/3588015.3589511
  • Ding, X., & Yang, Z. (2022). Knowledge mapping of platform research: A visual analysis using VOSviewer and CiteSpace. Electronic Commerce Research, 22(3), 787–809. https://doi.org/10.1007/S10660-020-09410-7/TABLES/10
  • Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/J.JBUSRES.2021.04.070
  • Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. International Journal of Information Management, 48, 63–71. https://doi.org/10.1016/J.IJINFOMGT.2019.01.021
  • Duraisamy, P., Yuvaraj, S., Natarajan, Y., & Niranjani, V. (2023). An overview of different types of recommendations systems-a survey. 2023 International Conference on Innovative Trends in Information Technology, ICITIIT 2023. https://doi.org/10.1109/ICITIIT57246.2023.10068631
  • EBSE Technical Report Guidelines for performing Systematic Literature Reviews in Software Engineering. (2007).
  • Fayyaz, Z., Ebrahimian, M., Nawara, D., Ibrahim, A., & Kashef, R. (2020). Recommendation systems: Algorithms, challenges, metrics, and business opportunities. Appl. Scş, 10(7748). https://doi.org/10.3390/app10217748
  • Felfernig, A., Jeran, M., Ninaus, G., Reinfrank, F., Reiterer, S., & Stettinger, M. (2014). Basic approaches in recommendation systems. Recommendation Systems in Software Engineering, 15–37. https://doi.org/10.1007/978-3-642-45135-5_2/TABLES/14
  • Fraumann, G., & Mutz, R. (2020). The h-index. Handbook Bibliometrics, 169–177. https://doi.org/10.1515/9783110646610-018
  • Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to Weave an Information tapestry. Communications of the ACM, 35(12), 61–70. https://doi.org/10.1145/138859.138867
  • Hu, Y., Zhang, D., Ye, J., Li, X., & He, X. (2013). Fast and accurate matrix completion via truncated nuclear norm regularization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(9), 2117–2130. https://doi.org/10.1109/TPAMI.2012.271
  • Jabbar, M., Javaid, Q., Arif, M., Munir, A., & Javed, A. (2018). An efficient and intelligent recommender system for mobile platform. Mehran University Research Journal of Engineering and Technology, 37(4), 463–480. https://doi.org/10.22581/MUET1982.1804.02
  • Katzman, J., Shaham, U., Bates, J., Cloninger, A., Jiang, T., & Kluger, Y. (2016). DeepSurv: Personalized treatment recommender system using a cox proportional hazards deep neural network. BMC Medical Research Methodology, 18(1). https://doi.org/10.1186/s12874-018-0482-1
  • Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30–37. https://doi.org/10.1109 / MC.2009.263
  • Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2016). A survey of deep neural network architectures and their applications. Neurocomputing, 234, 11-26. https://doi.org/10.1016/j.neucom.2016.12.038
  • Mu, R. (2018). A survey of recommender systems based on deep learning. IEEE Access, 6, 69009–69022. https://doi.org/10.1109/ACCESS.2018.2880197
  • Papneja, S., Sharma, K., & Khilwani, N. (2021). Content recommendation based on topic modeling. Advances in Intelligent Systems and Computing, 1227, 1–10. https://doi.org/10.1007/978-981-15-6876-3_1/FIGURES/2
  • Pazzani, M., & Billsus, D. (1997). Learning and revising user profiles: The identification of interesting web sites. Machine Learning, 27(3), 313–331. https://doi.org/10.1023/A:1007369909943/METRICS
  • Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994). GroupLens: An open architecture for collaborative filtering of netnews. Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, CSCW 1994, 175–186. https://doi.org/10.1145/192844.192905
  • Resnick, P., & Varian, H. R. (1997). Recommender Systems. Communications of the ACM, 40(3). https://doi.org/10.1145/245108.245121
  • Rich, E. (1979). User Modeling via Stereotypes. Cognitive Science, 3, 329–354.
  • Roy, D., & Dutta, M. (2022). A systematic review and research perspective on recommender systems. Journal of Big Data, 9(1), 1–36. https://doi.org/10.1186/S40537-022-00592-5/FIGURES/6
  • Sanderson, M., & Croft, W. B. (2012). The history of information retrieval research. Proceedings of the IEEE, 100, 1444–1451.
  • Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. Proceedings of the 10th International Conference on World Wide Web, WWW 2001, 285–295. https://doi.org/10.1145/371920.372071/ASSET/CFB8B952-6F16-43A6-8125-16F950D0D3E3/ASSETS/371920.372071.FP.PNG
  • Shi, C., Hu, B., Xin, W., Member, Z., & Yu, P. S. (2017). Heterogeneous Information Network Embedding for Recommendation. IEEE Transactions on Knowledge and Data Engineering, 31(2), 357 - 370. https://doi.org/10.1109/TKDE.2018.2833443
  • Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/J.JBUSRES.2019.07.039
  • Wang, X., He, X., Wang, M., Feng, F., & Chua, T.-S. (2019). Neural Graph Collaborative Filtering. SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 165–174. https://doi.org/10.1145/3331184.3331267
  • Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., & Tan, T. (2019). Session-Based Recommendation with Graph Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 346–353. https://doi.org/10.1609/AAAI.V33I01.3301346
  • Zhang, S., Yao, L., Sun, A., & Tay, Y. (2017). Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys, 52(1). https://doi.org/10.1145/3285029
  • Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., & Zhang, J. (2019). Edge intelligence: paving the last mile of artificial intelligence with edge computing. Proceedings of the IEEE, 107(8), 1738–1762. https://doi.org/10.1109/JPROC.2019.2918951

Tavsiye Sistemleri Literatürünün Bibliyometrik Analizi

Yıl 2024, Cilt: 12 Sayı: 3, 232 - 251, 31.12.2024
https://doi.org/10.22139/jobs.1476377

Öz

Bu çalışma, tavsiye sistemleri literatürünün son beş yılda gerçekleştirilen bibliyometrik analizini içermektedir. Web of Science (WoS) veri tabanından elde edilen veriler kullanılarak, bu alanın gelişimi ve dönemeç noktaları detaylı bir şekilde incelenmiştir. Tavsiye sistemleri, kullanıcı verilerini kullanarak kişiselleştirilmiş içerik ve ürün önerileri sunan teknolojilerdir ve bu sistemler, internetin ve dijital işlemlerin yaygınlaşmasıyla birlikte önem kazanmıştır. Kullanıcılar tarafından verilen doğrudan geri bildirimler veya göz izleme teknolojisi gibi yöntemlerle elde edilen zengin veriler, kullanıcı tercihlerinin analiz edilmesi ve ihtiyaçlara uygun önerilerin sunulması için kullanılmaktadır. Araştırma, GroupLens çalışması gibi önemli adımları ele alarak, işbirlikçi filtreleme ve içerik tabanlı filtreleme gibi temel yaklaşımların gelişimine ışık tutmaktadır. Bu sistemler, Google ve Facebook gibi platformlar tarafından kullanıcı etkileşimlerini analiz edip, gelecekteki tercihleri tahmin etmek amacıyla kullanılmaktadır. Bibliyometrik analiz, VOSviewer aracılığıyla yapılan görselleştirmelerle desteklenmiş olup, tavsiye sistemleri literatüründe sıkça karşılaşılan terimlerin ve bu terimler arasındaki ilişkilerin detaylı bir haritasını sunmaktadır. Çalışma, bu alanda araştırma yapacak olanlara rehberlik edecek şekilde tasarlanmıştır ve tavsiye sistemlerinin bilimsel etkisinin arttığını göstermektedir. Bibliyometrik analiz, bilimsel yayınların niceliksel bir değerlendirmesini sağlayarak, bu yayınların bilimsel etki ve kalitesini objektif bir şekilde ölçmüştür. Analiz sonuçları, tavsiye sistemleri alanındaki akademik çalışmaların ve atıfların zaman içindeki artışını göstermektedir ve bu artış, alandaki ilginin ve etkinin giderek arttığını işaret etmektedir. Bu tür bir analiz, gelecekte bu konu üzerine çalışacak araştırmacılara yol gösterici olabilir ve tavsiye sistemlerinin daha da geliştirilmesi için temel oluşturabilir. Sonuç olarak, bu çalışma, tavsiye sistemleri literatürünün kapsamlı bir analizini sunmakta ve araştırma alanındaki bilimsel ilerlemeleri daha derinlemesine inceleme imkanı sağlamaktadır.

Kaynakça

  • Aggarwal, C. C. (2016). Recommender Systems. https://doi.org/10.1007/978-3-319-29659-3
  • Albort-Morant, G., & Ribeiro-Soriano, D. (2016). A bibliometric analysis of international impact of business incubators. Journal of Business Research, 69(5), 1775–1779. https://doi.org/10.1016/J.JBUSRES.2015.10.054
  • Belkin, N. J., & Croft, W. B. (1992). Information filtering and Information retrieval: Two Sides of the Same Coin? Communications of the ACM, 35(12), 29–38. https://doi.org/10.1145/138859.138861
  • Berg, R. van den, Kipf, T. N., & Welling, M. (2017). Graph Convolutional Matrix Completion. https://arxiv.org/abs/1706.02263v2
  • Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109–132. https://doi.org/10.1016/j.knosys.2013.03.012
  • Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modelling and User-Adapted Interaction, 12(4), 331–370. https://doi.org/10.1023/A:1021240730564
  • Clarivate Inc., Essential Science Indicators Help. (2024, March 10). Essential Science Indicators Help. https://esi.help.clarivate.com/Content/home.htm
  • Cohen, W. W., Schapire, R. E., & Singer, Y. (1999). Learning to order things. Journal of Artificial Intelligence Research, 10.
  • De Leon-Martinez, S., Moro, R., & Bielikova, M. (2023). Eye Tracking as a Source of Implicit Feedback in Recommender Systems: A Preliminary Analysis. https://doi.org/10.1145/3588015.3589511
  • Ding, X., & Yang, Z. (2022). Knowledge mapping of platform research: A visual analysis using VOSviewer and CiteSpace. Electronic Commerce Research, 22(3), 787–809. https://doi.org/10.1007/S10660-020-09410-7/TABLES/10
  • Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/J.JBUSRES.2021.04.070
  • Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. International Journal of Information Management, 48, 63–71. https://doi.org/10.1016/J.IJINFOMGT.2019.01.021
  • Duraisamy, P., Yuvaraj, S., Natarajan, Y., & Niranjani, V. (2023). An overview of different types of recommendations systems-a survey. 2023 International Conference on Innovative Trends in Information Technology, ICITIIT 2023. https://doi.org/10.1109/ICITIIT57246.2023.10068631
  • EBSE Technical Report Guidelines for performing Systematic Literature Reviews in Software Engineering. (2007).
  • Fayyaz, Z., Ebrahimian, M., Nawara, D., Ibrahim, A., & Kashef, R. (2020). Recommendation systems: Algorithms, challenges, metrics, and business opportunities. Appl. Scş, 10(7748). https://doi.org/10.3390/app10217748
  • Felfernig, A., Jeran, M., Ninaus, G., Reinfrank, F., Reiterer, S., & Stettinger, M. (2014). Basic approaches in recommendation systems. Recommendation Systems in Software Engineering, 15–37. https://doi.org/10.1007/978-3-642-45135-5_2/TABLES/14
  • Fraumann, G., & Mutz, R. (2020). The h-index. Handbook Bibliometrics, 169–177. https://doi.org/10.1515/9783110646610-018
  • Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to Weave an Information tapestry. Communications of the ACM, 35(12), 61–70. https://doi.org/10.1145/138859.138867
  • Hu, Y., Zhang, D., Ye, J., Li, X., & He, X. (2013). Fast and accurate matrix completion via truncated nuclear norm regularization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(9), 2117–2130. https://doi.org/10.1109/TPAMI.2012.271
  • Jabbar, M., Javaid, Q., Arif, M., Munir, A., & Javed, A. (2018). An efficient and intelligent recommender system for mobile platform. Mehran University Research Journal of Engineering and Technology, 37(4), 463–480. https://doi.org/10.22581/MUET1982.1804.02
  • Katzman, J., Shaham, U., Bates, J., Cloninger, A., Jiang, T., & Kluger, Y. (2016). DeepSurv: Personalized treatment recommender system using a cox proportional hazards deep neural network. BMC Medical Research Methodology, 18(1). https://doi.org/10.1186/s12874-018-0482-1
  • Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30–37. https://doi.org/10.1109 / MC.2009.263
  • Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2016). A survey of deep neural network architectures and their applications. Neurocomputing, 234, 11-26. https://doi.org/10.1016/j.neucom.2016.12.038
  • Mu, R. (2018). A survey of recommender systems based on deep learning. IEEE Access, 6, 69009–69022. https://doi.org/10.1109/ACCESS.2018.2880197
  • Papneja, S., Sharma, K., & Khilwani, N. (2021). Content recommendation based on topic modeling. Advances in Intelligent Systems and Computing, 1227, 1–10. https://doi.org/10.1007/978-981-15-6876-3_1/FIGURES/2
  • Pazzani, M., & Billsus, D. (1997). Learning and revising user profiles: The identification of interesting web sites. Machine Learning, 27(3), 313–331. https://doi.org/10.1023/A:1007369909943/METRICS
  • Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994). GroupLens: An open architecture for collaborative filtering of netnews. Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, CSCW 1994, 175–186. https://doi.org/10.1145/192844.192905
  • Resnick, P., & Varian, H. R. (1997). Recommender Systems. Communications of the ACM, 40(3). https://doi.org/10.1145/245108.245121
  • Rich, E. (1979). User Modeling via Stereotypes. Cognitive Science, 3, 329–354.
  • Roy, D., & Dutta, M. (2022). A systematic review and research perspective on recommender systems. Journal of Big Data, 9(1), 1–36. https://doi.org/10.1186/S40537-022-00592-5/FIGURES/6
  • Sanderson, M., & Croft, W. B. (2012). The history of information retrieval research. Proceedings of the IEEE, 100, 1444–1451.
  • Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. Proceedings of the 10th International Conference on World Wide Web, WWW 2001, 285–295. https://doi.org/10.1145/371920.372071/ASSET/CFB8B952-6F16-43A6-8125-16F950D0D3E3/ASSETS/371920.372071.FP.PNG
  • Shi, C., Hu, B., Xin, W., Member, Z., & Yu, P. S. (2017). Heterogeneous Information Network Embedding for Recommendation. IEEE Transactions on Knowledge and Data Engineering, 31(2), 357 - 370. https://doi.org/10.1109/TKDE.2018.2833443
  • Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/J.JBUSRES.2019.07.039
  • Wang, X., He, X., Wang, M., Feng, F., & Chua, T.-S. (2019). Neural Graph Collaborative Filtering. SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 165–174. https://doi.org/10.1145/3331184.3331267
  • Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., & Tan, T. (2019). Session-Based Recommendation with Graph Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 346–353. https://doi.org/10.1609/AAAI.V33I01.3301346
  • Zhang, S., Yao, L., Sun, A., & Tay, Y. (2017). Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys, 52(1). https://doi.org/10.1145/3285029
  • Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., & Zhang, J. (2019). Edge intelligence: paving the last mile of artificial intelligence with edge computing. Proceedings of the IEEE, 107(8), 1738–1762. https://doi.org/10.1109/JPROC.2019.2918951
Toplam 38 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İş Sistemleri (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Can İlkhan 0009-0009-4052-9703

Emrah Önder 0000-0002-0554-1290

Erken Görünüm Tarihi 23 Aralık 2024
Yayımlanma Tarihi 31 Aralık 2024
Gönderilme Tarihi 1 Mayıs 2024
Kabul Tarihi 11 Kasım 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 12 Sayı: 3

Kaynak Göster

APA İlkhan, C., & Önder, E. (2024). Tavsiye Sistemleri Literatürünün Bibliyometrik Analizi. İşletme Bilimi Dergisi, 12(3), 232-251. https://doi.org/10.22139/jobs.1476377