Tavsiye Sistemlerinde Büyük Verinin Kullanımı Üzerine Kapsamlı Bir İnceleme

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.

Anahtar Kelimeler

• Büyük Veri,Akan Veri,Tavsiye Sistemleri

Kaynakça

1. [1] Muthukrishnan, S. (2005). Data streams: Algorithms and applications. Foundations and Trends in Theoretical Computer Science, 1(2), 117-236.
2. [2] Isinkaye, F., Folajimi, Y. ve Ojokoh B. (2015). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, 261-273.
3. [3] Amatriain, X. (2013). Mining large streams of user data for personalized recommendations. ACM SIGKDD Explorations Newsletter, 14(2), 37-48.
4. [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. [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. [6] Werner, S. ve Lommatzsch, A. (2014). Optimizing and Evaluating Stream-based News Recommendation Algorithms. In CLEF (Working Notes), 813-824.
7. [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. [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. [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.
10. [10] Sri, P.A. ve Anusha, M. (2016). Big Data-Survey. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 4(1), 74-80.
11. [11] Beyer, M. (2011). Gartner Says Solving'Big Data'Challenge Involves More Than Just Managing Volumes of Data. Gartner. Archived from the original on.
12. [12] TechAmerica Foundation’s Federal Big Data Commission. (2012). Demystifying bigdata. A practical guide to transforming the business of Government”.
13. [13] Chen, C.P. ve Zhang, C.Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314-347.
14. [14] Cukier, K. (2010). Data, data everywhere: A special report on managing information. Economist Newspaper.
15. [15] Khan, N., Yaqoob, I., Hashem, I.A.T, Inayat, Z., Mahmoud, A.W.K., Alam, M. ve Gani, A. (2014). Big data: survey, technologies, opportunities, and challenges. The Scientific World Journal.
16. [16] Ward, J.S. ve Barker, A. (2013). Undefined by data: a survey of big data definitions. arXiv preprint.
17. [17] Chen, M., Mao, S. ve Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171-209.
18. [18] LaValle, S., Lesser, E., Shockley, R., Hopkins, M.S. ve Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT sloan management review, 52(2), 21.
19. [19] Sagiroglu, S. ve Sinanc, D. (2013). Big data: A review. In Collaboration Technologies and Systems (CTS), 42-47.
20. [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. [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.
22. [22] Zikopoulos, P. ve Eaton, C. (2011). Understanding big data: Analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media.
23. [23] Snijders, C., Matzat, U. ve Reips, U.D. (2012). Big Data: Big gaps of knowledge in the field of internet science. International Journal of Internet Science, 7(1), 1-5.
24. [24] Liu, X., Iftikhar, N. ve Xie, X. (2014). Survey of real-time processing systems for big data. In Proceedings of the 18th International Database Engineering & Applications Symposium, 356-361.
25. [25] Fahad, A., Alshatri, N., Tari, Z., Alamri, A., Khalil, I., Zomaya, A.Y. ve Bouras, A. (2014). A survey of clustering algorithms for big data: Taxonomy and empirical analysis. IEEE transactions on emerging topics in computing, 2(3), 267-279.
26. [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. [27] Evelson, B.(2015). Vendor Landscape: Big Data Text Analytics. For Application Development & Delivery Professionals.
28. [28] Lohr, S. (2012). The age of big data. New York Times, 11(2012).
29. [29] Russom, P. (2013). Managing big data. TDWI Best Practices Report, TDWI Research, 1-40.
30. [30] Snoek, C.G., Worring, M. ve Smeulders, A.W. (2005). Early versus late fusion in semantic video analysis. In Proceedings of the 13th annual ACM international conference on Multimedia, 399-402.
31. [31] Davenport, T.H., Barth, P. ve Bean, R. (2012). How big data is different. MIT Sloan Management Review, 54(1), 43.
32. [32] Cambria, E., Rajagopal, D., Olsher, D. ve Das, D. (2013). Big social data analysis. Big data computing, 2013, 401-414.
33. [33] Tufekci, Z. (2014). Big questions for social media big data: Representativeness, validity and other methodological pitfalls. arXiv preprint.
34. [34] Manovich, L. (2011). Trending: The promises and the challenges of big social data. Debates in the digital humanities, 2, 460-475.
35. [35] Bravo-Marquez, F., Mendoza, M. ve Poblete, B. (2014). Meta-level sentiment models for big social data analysis. Knowledge-Based Systems, 69, 86-99.
36. [36] Cambria, E., Wang, H. ve White, B. (2014). Guest editorial: Big social data analysis. Knowledge-Based Systems, (69), 1-2.
37. [37] Lazer, D., Kennedy, R., King, G. ve Vespignani, A. (2014). The parable of Google Flu: Traps in big data analysis. Science, 343(6176), 1203-1205.
38. [38] Chen, H., Chiang, R.H. ve Storey, V.C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 36(4), 1165-1188.
39. [39] Laurila, J.K., Gatica-Perez, D., Aad, I., Bornet, O., Do, T.M.T., Dousse, O. ve Miettinen, M. (2012). The mobile data challenge: Big data for mobile computing research. In Pervasive Computing.
40. [40] Hashem, I.A.T, Yaqoob, I., Anuar, N.B., Mokhtar, S., Gani, A. ve Khan, S.U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, 98-115.
41. [41] Wang, G., Ng, T.S. ve Shaikh, A. (2012). Programming your network at run-time for big data applications. In Proceedings of the first workshop on Hot topics in software defined networks, 103-108.
42. [42] Fan, W. ve Bifet, A. (2013). Mining big data: current status, and forecast to the future. ACM sIGKDD Explorations Newsletter, 14(2), 1-5.
43. [43] Dittrich, J., Quiané-Ruiz, J.A. (2012). Efficient big data processing in Hadoop MapReduce. Proceedings of the VLDB Endowment, 5(12), 2014-2015.
44. [44] Katal, A., Wazid, M. ve Goudar, R.H. (2013). Big data: issues, challenges, tools and good practices. In Contemporary Computing (IC3), 2013 Sixth International Conference on, 404-409.
45. [45] Wang, Y. (2016). Stream Processing Systems Benchmark. StreamBench.
46. [46] Inoublia, W., Aridhib, S., Meznic, H. ve Jungd, A. (2016). An Experimental Survey on Big Data Frameworks. arXiv preprint.
47. [47] Grolinger, K., Hayes, M., Higashino, W.A., L'Heureux, A., Allison, D.S. ve Capretz, M.A. (2014). Challenges for mapreduce in big data. In Services (SERVICES), 2014 IEEE World Congress on, 182-189.
48. [48] Karau, H., Konwinski, A., Wendell, P. ve Zaharia, M. (2015). Learning spark: lightning-fast big data analysis. O'Reilly Media, Inc.
49. [49] Reyes-Ortiz, J.L., Oneto, L. ve Anguita, D. (2015). Big data analytics in the cloud: Spark on hadoop vs mpi/openmp on beowulf. Procedia Computer Science, 53, 121-130.
50. [50] Madden, S. (2016). From databases to big data. IEEE Internet Computing, 16(3), 4-6.
51. [51] Landset, S., Khoshgoftaar, T.M., Richter, A.N. ve Hasanin, T. (2015). A survey of open source tools for machine learning with big data in the Hadoop ecosystem. Journal of Big Data, 2(1), 24.
52. [52] Markl, V. (2014). Breaking the chains: On declarative data analysis and data independence in the big data era. Proceedings of the VLDB Endowment, 7(13), 1730-1733.
53. [53] Carbone, P., Katsifodimos, A., Ewen, S., Markl, V., Haridi, S. ve Tzoumas, K. (2015). Apache flink: Stream and batch processing in a single engine. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 36(4).
54. [54] Zhang, Y. (2016). GroRec: a group-centric intelligent recommender system integrating social, mobile and big data technologies. IEEE Transactions on Services Computing, 9(5), 786-795.
55. [55] Anastasiu, D.C., Christakopoulou, E., Smith, S., Sharma, M. ve Karypis, G. (2016). Big Data and Recommender Systems.
56. [56] Bobadilla, J., Ortega, F., Hernando, A. ve Gutiérrez, A. (2013). Recommender systems survey. Knowledge-based systems, 46, 109-132.
57. [57] Ricci, F., Rokach, L. ve Shapira, B. (2011). Introduction to recommender systems handbook. Springer US.
58. [58] Chen, K., Chen, T., Zheng, G., Jin, O., Yao, E., & Yu, Y. (2012). Collaborative personalized tweet recommendation. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, 661-670.
59. [59] Meng, S., Dou, W., Zhang, X. ve Chen J. (2014). KASR: A Keyword-Aware Service Recommendation method on MapReduce for big data applications”. IEEE Transactions on Parallel and Distributed Systems, 25(12), 3221-3231.
60. [60] Weider, D.Y., Pratiksha, C., Swati, S., Akhil, S. ve Sarath, M. (2015). A Modeling Approach to Big Data Based Recommendation Engine in Modern Health Care Environment. In Computer Software and Applications Conference (COMPSAC), 75-86.
61. [61] Gu, R., Tang, Y., Wang, Z., Wang, S., Yin, X., Yuan, C. ve Huang, Y. (2015). Efficient large scale distributed matrix computation with spark. In Big Data (Big Data), 2015 IEEE International Conference on, 2327-2336.
62. [62] Verma, J.P., Patel, B. ve Patel, A. (2015). Big data analysis: recommendation system with Hadoop framework. In Computational Intelligence & Communication Technology (CICT), 2015 IEEE International Conference on, 92-97.
63. [63] Dai, J., Yang, B., Guo, C. ve Ding, Z. (2015). Personalized route recommendation using big trajectory data. In Data Engineering (ICDE), 2015 IEEE 31st International Conference, 543-554.
64. [64] Huang, Y., Cui, B., Zhang, W., Jiang, J. ve Xu, Y. (2015). Tencentrec: Real-time stream recommendation in practice. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, 227-238.
65. [65] Riyaz, P.A. ve Varghese, S.M. (2016). A Scalable Product Recommendations Using Collaborative Filtering in Hadoop for Bigdata. Procedia Technology, 24, 1393-1399.
66. [66] Shang, S., Shi, M., Shang, W. ve Hong, Z. (2016). A micro-video recommendation system based on big data. In Computer and Information Science (ICIS), 2016 IEEE/ACIS 15th International Conference on, 1-5.
67. [67] Chang, S., Zhang, Y., Tang, J., Yin, D., Chang, Y., Hasegawa-Johnson, M.A. ve Huang, T.S. (2016). Streaming Recommender Systems. arXiv preprint.
68. [68] Prando, A.V., Contratres, F., Souza, S., ve De Souza, L. (2017). Content-based recommender system using social networks for cold-start users. In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 181-189.
69. [69] Ajantha, D., Vijay, J. ve Sridhar, R. (2017). A user-location vector based approach for personalised tourism and travel recommendation. In Big Data Analytics and Computational Intelligence (ICBDAC), 2017 International Conference on, 440-446.
70. [70] Zhou, T., Chen, L. ve Shen, J. (2017). Movie Recommendation System Employing the User-Based CF in Cloud Computing. In Computational Science and Engineering (CSE) and Embedded and Ubiquitous Computing (EUC), 2017 IEEE International Conference on, 46-50.
71. [71] Seo, Y. D., Kim, Y. G., Lee, E. ve Baik, D. K. (2017). Personalized recommender system based on friendship strength in social network services. Expert Systems with Applications, 69, 135-148.
72. [72] Wei, J., He, J., Chen, K., Zhou, Y. ve Tang, Z. (2017). Collaborative filtering and deep learning based recommendation system for cold start items. Expert Systems with Applications, 69, 29-39.
73. [73] Lee, T.Q., Park, Y. ve Park, Y.T. (2008). A time-based approach to effective recommender systems using implicit feedback. Expert systems with applications, 34(4), 3055-3062.
74. [74] Hu, Y., Koren, Y. ve Volinsky, C. (2008). Collaborative filtering for implicit feedback datasets. In Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on, 263-272.
75. [75] Núñez-Valdéz, E.R., Lovelle, J.M.C., Martínez, O.S., García-Díaz, V., de Pablos, P.O. ve Marín, C.E.M. (2012). Implicit feedback techniques on recommender systems applied to electronic books. Computers in Human Behavior, 28(4), 1186-1193.
76. [76] Jawaheer, G., Weller, P. ve Kostkova, P. (2014). Modeling user preferences in recommender systems: A classification framework for explicit and implicit user feedback. ACM Transactions on Interactive Intelligent Systems (TiiS), 4(2).
77. [77] Karatzoglou, A., Baltrunas, L. ve Shi, Y. (2013). Learning to rank for recommender systems. In Proceedings of the 7th ACM conference on Recommender systems, 493-494.
78. [78] Lops, P., De Gemmis, M. ve Semeraro, G. (2011). Content-based recommender systems: State of the art and trends. In Recommender systems handbook, 73-105.
79. [79] Tatiya, R.V. ve Vaidya, A.S. (2011). A Survey of Recommendation Algorithms. IOSR Journals (IOSR Journal of Computer Engineering, 1(16), 16-19.
80. [80] Ekstrand, M.D., Riedl, J.T. ve Konstan, J.A. (2011). Collaborative filtering recommender systems. Foundations and Trends in Human–Computer Interaction, 4(2), 81-173.
81. [81] Tso-Sutter, K.H., Marinho, L.B. ve Schmidt-Thieme, L. (2008). Tag-aware recommender systems by fusion of collaborative filtering algorithms. In Proceedings of the 2008 ACM symposium on Applied computing, 1995-1999, 2008.
82. [82] Hameed, M.A, Al Jadaan, O. ve Ramachandram, S. (2012). Collaborative filtering based recommendation system: A survey. International Journal on Computer Science and Engineering, 4(5), 859.
83. [83] Bobadilla, J., Ortega, F., Hernando, A. ve Alcalá, J. (2011). Improving collaborative filtering recommender system results and performance using genetic algorithms. Knowledge-based systems, 24(8), 1310-1316.
84. [84] Pham, M.C., Cao, Y., Klamma, R. ve Jarke, M. (2011). A clustering approach for collaborative filtering recommendation using social network analysis. J. UCS, 17(4), 583-604.
85. [85] Gao, M., Wu, Z. ve Jiang, F. (2011). Userrank for item-based collaborative filtering recommendation. Information Processing Letters, 111(9), 440-446.
86. [86] Schafer, J. B., Frankowski, D., Herlocker, J. ve Sen, S. (2007). Collaborative filtering recommender systems. In The adaptive web, Springer, Berlin, Heidelberg 291-324.
87. [87] Koren, Y. ve Bell, R. (2015). Advances in collaborative filtering. In Recommender systems handbook, 77-118, 2015.
88. [88] Wang, C. ve Blei, D.M. (2011). Collaborative topic modeling for recommending scientific articles. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 448-456.
89. [89] Klašnja-Milićević, A., Vesin, B., Ivanović, M. ve Budimac, Z. (2011). E-Learning personalization based on hybrid recommendation strategy and learning style identification. Computers & Education, 56(3), 885-899.
90. [90] Porcel, C., Tejeda-Lorente, A., Martínez, M.A. ve Herrera-Viedma, E. (2012). A hybrid recommender system for the selective dissemination of research resources in a technology transfer Office. Information Sciences, 184(1), 1-19.
91. [91] Elahi, M., Ricci, F. ve Rubens, N. (2016). A survey of active learning in collaborative filtering recommender systems. Computer Science Review.
92. [92] Adomavicius, G. ve Tuzhilin, A. (2015). Context-aware recommender systems. In Recommender systems handbook, 191-226.
93. [93] Desrosiers, C. ve Karypis, G. (2011). A comprehensive survey of neighborhood-based recommendation methods. Recommender systems handbook, 107-144.
94. [94] Zhou, X., Xu, Y., Li, Y., Josang, A. ve Cox, C. (2012). The state-of-the-art in personalized recommender systems for social networking. Artificial Intelligence Review, 37(2), 119-132.
95. [95] Cacheda, F., Carneiro, V., Fernández, D. ve Formoso, V. (2011). Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Transactions on the Web (TWEB), 5(1).
96. [96] Yang, X., Guo, Y., Liu, Y. ve Steck H. (2014). A survey of collaborative filtering based social recommender systems. Computer Communications, 41, 1-10.
97. [97] Noulas, A., Scellato, S., Lathia, N. ve Mascolo, C. (2012). A random walk around the city: New venue recommendation in location-based social networks. In Privacy, security, risk and trust (PASSAT), 2012 international conference on and 2012 international confernece on social computing, 144-153.
98. [98] Schelter, S., Boden, C. ve Markl, V. (2012). Scalable similarity-based neighborhood methods with MapReduce. In Proceedings of the sixth ACM conference on Recommender systems, 163-170.
99. [99] Gantner, Z., Rendle, S., Freudenthaler, C. ve Schmidt-Thieme, L. (2011). MyMediaLite: A free recommender system library. In Proceedings of the fifth ACM conference on Recommender systems, 305-308.
100. [100] Takács, G. ve Tikk, D. (2012). Alternating least squares for personalized ranking. In Proceedings of the sixth ACM conference on Recommender systems, 83-90.
101. [101] Yu, H.F., Hsieh, C.J., Si, S. ve Dhillon, I. (2012). Scalable coordinate descent approaches to parallel matrix factorization for recommender systems. In Data Mining (ICDM), 2012 IEEE 12th International Conference on, 765-774.
102. [102] Gemulla, R., Nijkamp, E., Haas, P.J. ve Sismanis, Y. (2011). Large-scale matrix factorization with distributed stochastic gradient descent. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 69-77.
103. [103] Lü, L., Medo, M., Yeung, C.H., Zhang, Y.C, Zhang, Z.K. ve Zhou, T. (2012). Recommender systems. Physics Reports, 519(1), 1-49.
104. [104] Shani, G. ve Gunawardana, A. (2011). Evaluating recommendation systems. Recommender systems handbook, 257-297.
105. [105] Bellogin, A., Castells, P. ve Cantador, I. (2011). Precision-oriented evaluation of recommender systems: An algorithmic comparison. In Proceedings of the fifth ACM conference on Recommender systems, 333-336.
106. [106] Zaier, Z., Godin, R. ve Faucher, L. (2008). Evaluating recommender systems. In Automated solutions for Cross Media Content and Multi-channel Distribution, 2008. AXMEDIS'08. International Conference on, 211-217.