Tavsiye Sistemlerinde Büyük Verinin Kullanımı Üzerine Kapsamlı Bir İnceleme
Yıl 2018,
Cilt: 30 Sayı: 4, 339 - 357, 31.12.2018
Anıl Utku
,
Muhammet Ali Akcayol
Öz
Web tabanlı e-ticaret platformlarındaki gelişmeler, tavsiye
sistemlerinin giderek önem kazanmasına neden olmaktadır. Tavsiye sistemleri,
kullanıcılar için faydalı ve kişiselleştirilmiş öneriler sunmak için
geliştirilen sistemlerdir. Büyük veri çağında, artan sayıda kullanıcı ve ürün
karşısında mevcut tavsiye sistemleri ölçeklenebilirlik ve verimlilik sorunları
yaşamaktadır. Bu çalışma kapsamında, büyük veri ve tavsiye sistemleri üzerine
kapsamlı ve karşılaştırmalı bir inceleme yapılmıştır. Literatürde büyük verinin
tavsiye sistemlerinde kullanıldığı çalışmalar incelenmiş, büyük verinin tavsiye
sistemlerine yüksek performans ve başarı ile uygulanabilmesi için gerekli
önişlemler ve yöntemler detaylı bir şekilde incelenmiştir.
Kaynakça
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Yıl 2018,
Cilt: 30 Sayı: 4, 339 - 357, 31.12.2018
Anıl Utku
,
Muhammet Ali Akcayol
Kaynakça
- [1] Muthukrishnan, S. (2005). Data streams: Algorithms and applications. Foundations and Trends in Theoretical Computer Science, 1(2), 117-236.
- [2] Isinkaye, F., Folajimi, Y. ve Ojokoh B. (2015). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, 261-273.
- [3] Amatriain, X. (2013). Mining large streams of user data for personalized recommendations. ACM SIGKDD Explorations Newsletter, 14(2), 37-48.
- [4] Subbian, K., Aggarwal, C. ve Hegde, K. (2016). Recommendations for streaming data. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 2185-2190.
- [5] Anceaume, E., Busnel, Y. ve Rivetti, N. (2015). Estimating the Frequency of Data Items in Massive Distributed Streams. In Network Cloud Computing and Applications (NCCA), 2015 IEEE Fourth Symposium, 59-66.
- [6] Werner, S. ve Lommatzsch, A. (2014). Optimizing and Evaluating Stream-based News Recommendation Algorithms. In CLEF (Working Notes), 813-824.
- [7] Ludmann, C.A. (2015). Online Recommender Systems based on Data Stream Management Systems. In Proceedings of the 9th ACM Conference on Recommender Systems, 391-394.
- [8] Lommatzsch, A. ve Albayrak, S. (2015). Real-time recommendations for user-item streams. In Proceedings of the 30th Annual ACM Symposium on Applied Computing, 1039-1046.
- [9] Chen, C., Yin, H., Yao, J. ve Cui, B. (2013). Terec: A temporal recommender system over tweet stream. Proceedings of the VLDB Endowment, 6(12), 1254-1257.
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- [20] Gandomi, A. ve Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.
- [21] Tsai, C.W, Lai, C.F., Chao, H.C. ve Vasilakos, A.V. (2015). Big data analytics: A survey. Journal of Big Data, 2(1), 21.
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- [26] Verma, J.P., Agrawal, S., Patel, B. ve Patel, A. (2016). Big data analytics. Challenges and applicatıons for text, audio, video, and social media data.
- [27] Evelson, B.(2015). Vendor Landscape: Big Data Text Analytics. For Application Development & Delivery Professionals.
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- [29] Russom, P. (2013). Managing big data. TDWI Best Practices Report, TDWI Research, 1-40.
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- [33] Tufekci, Z. (2014). Big questions for social media big data: Representativeness, validity and other methodological pitfalls. arXiv preprint.
- [34] Manovich, L. (2011). Trending: The promises and the challenges of big social data. Debates in the digital humanities, 2, 460-475.
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