مدل‌سازی رفتار اطلاعاتی کاربران پایگاه‌های اطلاعاتی با روش شبکه عصبی با تأکید بر تعاملات پیشین آنها با نتایج جست‌وجو

نویسندگان

1 پژوهشگاه علوم و فناوری اطلاعات ایران (ایرانداک)؛ تهران، ایران؛

2 دانشگاه شاهد؛ تهران، ایران؛

3 دانشگاه علم و صنعت؛ تهران، ایران؛

چکیده

در طراحی موتورهای جست‌وجو بررسی پایگاه داده مورد مطالعه و ایجاد ارتباط با آن دارای اهمیت است. در پژوهش حاضر با بررسی تعاملات کاربران پایگاه‌های اطلاعاتی با سیستم در حین جست‌وجو، به مدل‌سازی رفتار اطلاع‌یابی آن‌ها پرداخته می‌شود. این پژوهش یک مطالعه کاربردی است که با استفاده از شبکه عصبی اجرا شده است. داده‌ها از طریق مشاهده رفتار کاربران در استفاده از پایگاه داده «ایرانداک» و مطالعه لاگ کاربران گردآوری شده است. الگوی اغلب جست‌وجوهای انجام‌شده در بیشتر موارد به‌صورت «کلی به جزیی» و «اختصاصی» است. شرکت‌کنندگان جست‌وجوی خود را با اطلاعات کلی در مورد موضوع، مانند معرفی و بررسی حقایق، شروع نموده و سپس، هر یک بر روی جنبه‌های خاصی از موضوع تمرکز کردند. در بعضی موارد، کاربران در حین جست‌وجو ایده‌های جدیدی به‌دست آوردند. با توجه به نتایج به‌دست‌آمده می‌توان چنین استنتاج کرد که بر اساس مدل استخراجی، عواملی چون پیشینه کلی فرد در مورد موضوع، دانش موضوعی، محدوده زمان و ابزارهای در اختیار، پاسخ مورد نظر در جست‌وجو را تحت تأثیر قرار می‌دهد. موتورهای جست‌وجوی کنونی تنها بخشی از مدارک مرتبط با موضوع را در یک مجموعه داده بازیابی می‌کند. برای دسترسی به مطالب بیشتر و مناسب‌تر در حجم عظیم داده‌ها لازم است مدل‌های بهتری مورد بررسی قرار گیرد. روش شبکه عصبی پیشنهادی در این مطالعه امکان بهبود بازیابی اطلاعات را در مدت زمان کم فراهم می‌آورد. همچنین، با این روش می‌توان این امکان را به‌وجود آورد که مدل جست‌وجو هر مرتبه با جست‌وجوی کاربران به‌روزرسانی شده و نتایج کامل‌تر و دقیق‌تری به‌دست آید.

کلیدواژه‌ها

عنوان مقاله [English]

Neural Network Modelling of the Information Behavior of Database Users Based on their Previous Interactions with the Search Results

نویسندگان [English]

  • fataneh Wahabi 1
  • saeid asadi 2
  • Soheil Ganjehtar 3
چکیده [English]

In designing search engines, it is important to check the database you are studying and make a connection with it.
This study is an applied one conducted by using observation method. What is used in this study is a case study.
The pattern of most searches is generally partial and specific in most cases. Participants begin their search with general information such as introducing and reviewing facts, and then focusing on specific aspects. In some cases, users come up with new ideas while searching. According to the results, general background, subject knowledge, time range and tools available influence desired response.
According to the analysis of findings and results, it is suggested that information behavior using neural network is more accurate in identifying information skills, barriers, goals and motivation and determining and predicting resources and services. Information and ways of accessing information should be compared with the results of present study. Based on the results, it seems that specialized information retrieval training for all classes of users is needed to increase information skills of current and future users.
Current search engines retrieve only part of relevant documents in a collection. Better models are needed to overcome the huge volume of documents. The proposed model in this way enables improved data retrieval in a short period of time. It is also possible that search model is also updated each time with user searches and results in more accurate results.

کلیدواژه‌ها [English]

  • information retrieval
  • user
  • Behavior Feedback
  • Neural network
  • Search Engine

مراجع

  1. بدر، عاطفه، صدیقه محمداسماعیل، و حنیف حیدری. 1396. استفاده از تکنیک داده‌کاوی جهت دسته‌بندی کاربران هدف کتابخانه مرکزی دانشگاه صنعتی اصفهان (مطالعه انگیزه‌ها و رفتارهای اطلاع‌یابی آنان). پژوهشنامه پردازش و مدیریت اطلاعات 33 (1): 271-294.
  2. بدر، عاطفه، صدیقه محمداسماعیل، و حنیف حیدری. 1396. استفاده از تکنیک داده‌کاوی جهت دسته‌بندی کاربران هدف کتابخانه مرکزی دانشگاه صنعتی اصفهان (مطالعه انگیزه‌ها و رفتارهای اطلاع‌یابی آنان). پژوهشنامه پردازش و مدیریت اطلاعات 33 (1): 271-294.
  3. راد، ایرج. 1388. رفتار اطلاع‌یابی دانشجویان تحصیلات تکمیلی دانشگاه آزاد اسلامی در استفاده از شبکه جهانی وب.‎ کتابداری و اطلاع‌رسانی 12(3 (مسلسل 47)): 141-168.
  4. راد، ایرج. 1388. رفتار اطلاع‌یابی دانشجویان تحصیلات تکمیلی دانشگاه آزاد اسلامی در استفاده از شبکه جهانی وب.‎ کتابداری و اطلاع‌رسانی 12(3 (مسلسل 47)): 141-168.
  5. محمداسماعیل، صدیقه، و جمیله نعیمی. 1395. بررسی تطبیقی نیازهای اطلاعاتی و رفتار اطلاع‌یابی دانش‌پژوهان حوزه و دانشگاه استان خراسان رضوی با رویکرد شبکه عصبی.‎ تعامل انسان و اطلاعات 3 (4): 60-73.
  6. محمداسماعیل، صدیقه، و جمیله نعیمی. 1395. بررسی تطبیقی نیازهای اطلاعاتی و رفتار اطلاع‌یابی دانش‌پژوهان حوزه و دانشگاه استان خراسان رضوی با رویکرد شبکه عصبی.‎ تعامل انسان و اطلاعات 3 (4): 60-73.
  7. نعیمی، جمیله، صدیقه محمداسماعیل، و حنیف حیدری. 1397. تعیین نیازهای اطلاعاتی و رفتار اطلاع‌یابی دانش‌پژوهان حوزه علمیه خراسان رضوی با رویکرد شبکه عصبی. علوم و فنون مدیریت اطلاعات 4 (1): 91-118.
  8. نعیمی، جمیله، صدیقه محمداسماعیل، و حنیف حیدری. 1397. تعیین نیازهای اطلاعاتی و رفتار اطلاع‌یابی دانش‌پژوهان حوزه علمیه خراسان رضوی با رویکرد شبکه عصبی. علوم و فنون مدیریت اطلاعات 4 (1): 91-118.
  9. یاری، شیوا، و حمید احمدی. 1393. مروری بر متون رفتار اطلاع‌یابی در ایران. پژوهشنامه پردازش و مدیریت اطلاعات 30 (1): 173-187.
  10. یاری، شیوا، و حمید احمدی. 1393. مروری بر متون رفتار اطلاع‌یابی در ایران. پژوهشنامه پردازش و مدیریت اطلاعات 30 (1): 173-187.
  11. Abu Abbas, Osama. .2008. Comparisons between data clustering algorithms. The International Arab Journal of Information Technology 5 (3): 320-325.
  12. Abu Abbas, Osama. .2008. Comparisons between data clustering algorithms. The International Arab Journal of Information Technology 5 (3): 320-325.
  13. Al-mumen, N, & A. Morris. 2012. Modelling information-seeking Behaviour of graduate students at Kuwait University. Retrieved from www.Emeraldinsight.com (accessed Jan 20, 2016).
  14. Al-mumen, N, & A. Morris. 2012. Modelling information-seeking Behaviour of graduate students at Kuwait University. Retrieved from www.Emeraldinsight.com (accessed Jan 20, 2016).
  15. Badr, A., S. Mohamad Esmaeeli, & H. Heidari. 2018. Applying Data Mining Technique in order to categorize the Target Users of the Central Library of Isfahan University of Technology (Studying the Motives and Information Seeking Behaviors of Them). Information Sciences & Technology 33 (1): 275-294. Magiran.com/p177922
  16. Badr, A., S. Mohamad Esmaeeli, & H. Heidari. 2018. Applying Data Mining Technique in order to categorize the Target Users of the Central Library of Isfahan University of Technology (Studying the Motives and Information Seeking Behaviors of Them). Information Sciences & Technology 33 (1): 275-294. Magiran.com/p177922
  17. Billsus, D., and M. J. Pazzani. 1998. Learning collaborative information filters. In Proceedings of the Fifteenth International Conference on Machine Learning (ICML) Vol. 98, pp. 46-54. USA.
  18. Billsus, D., and M. J. Pazzani. 1998. Learning collaborative information filters. In Proceedings of the Fifteenth International Conference on Machine Learning (ICML) Vol. 98, pp. 46-54. USA.
  19. Biranvand, Ali 1392. Investigating the information-seeking behaviors of faculty members of Payame Noor University of Jahrom. Information Management and Knowledge 1 (1): 101-110.
  20. Biranvand, Ali 1392. Investigating the information-seeking behaviors of faculty members of Payame Noor University of Jahrom. Information Management and Knowledge 1 (1): 101-110.
  21. Bookstein, A. 1983. Information retrieval: A sequential learning process. Journal of American Society (ASIS) 34 (5): 331-342. [DOI:10.1002/asi.4630340504]
  22. Bookstein, A. 1983. Information retrieval: A sequential learning process. Journal of American Society (ASIS) 34 (5): 331-342. [DOI:10.1002/asi.4630340504]
  23. Borlund, P. 2010. The Concept of Relevance in IR. Journal of the American Society for Information Science and Technology 54 (10): 913-925. [DOI:10.1002/asi.10286]
  24. Borlund, P. 2010. The Concept of Relevance in IR. Journal of the American Society for Information Science and Technology 54 (10): 913-925. [DOI:10.1002/asi.10286]
  25. Buresova, I, & M. Simikova. 2012. Information Behaviour of Gifted Children - The Qualitative Study. Procedia-social and behavioral Sciences 8 (69): 242-246. [DOI:10.1016/j.sbspro.2012.11.405]
  26. Buresova, I, & M. Simikova. 2012. Information Behaviour of Gifted Children - The Qualitative Study. Procedia-social and behavioral Sciences 8 (69): 242-246. [DOI:10.1016/j.sbspro.2012.11.405]
  27. Carbonell, J., and J. Goldstein. 1998. The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval (pp. 335-336). Melbourne Australia August 24 - 28, 1998. [DOI:10.1145/290941.291025]
  28. Carbonell, J., and J. Goldstein. 1998. The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval (pp. 335-336). Melbourne Australia August 24 - 28, 1998. [DOI:10.1145/290941.291025]
  29. Chen, H., and D. R. Karger. 2006. Less is more: Probabilistic models for retrieving fewer relevant documents. ACM SIGIR 429-436. The 29th Annual International SIGIR Conference. Seattle Washington USA August 6 - 11, 2006429-436. [DOI:10.1145/1148170.1148245]
  30. Chen, H., and D. R. Karger. 2006. Less is more: Probabilistic models for retrieving fewer relevant documents. ACM SIGIR 429-436. The 29th Annual International SIGIR Conference. Seattle Washington USA August 6 - 11, 2006429-436. [DOI:10.1145/1148170.1148245]
  31. Cohn, D. A. 1996. Active learning with statistical models. Journal of artificial intelligence research 4: 129-145. [DOI:10.1613/jair.295]
  32. Cohn, D. A. 1996. Active learning with statistical models. Journal of artificial intelligence research 4: 129-145. [DOI:10.1613/jair.295]
  33. De Diego, A. & V. Eske. 2019. Öffentliches Wasserrecht im Rahmen von Zivilrechtsstreitigkeiten. Zeitschrift für Deutsches und Europäisches Wasser-, Abwasser-und Bodenschutzrecht 8 (4): 234-240.
  34. De Diego, A. & V. Eske. 2019. Öffentliches Wasserrecht im Rahmen von Zivilrechtsstreitigkeiten. Zeitschrift für Deutsches und Europäisches Wasser-, Abwasser-und Bodenschutzrecht 8 (4): 234-240.
  35. Fuhr, N. 2008. A probability ranking principle for interactive information retrieval. Information Retrieval 11 (3): 251-265. [DOI:10.1007/s10791-008-9045-0]
  36. Fuhr, N. 2008. A probability ranking principle for interactive information retrieval. Information Retrieval 11 (3): 251-265. [DOI:10.1007/s10791-008-9045-0]
  37. Hauff, C. 2010. Predicting the effectiveness of queries and retrieval systems. SIGIR (Special Intrest Group on Information Retireval) Forum (Vol. 44, No. 1, p. 88). Germany. [DOI:10.1145/1842890.1842906]
  38. Hauff, C. 2010. Predicting the effectiveness of queries and retrieval systems. SIGIR (Special Intrest Group on Information Retireval) Forum (Vol. 44, No. 1, p. 88). Germany. [DOI:10.1145/1842890.1842906]
  39. Hayati, Zahir. 1393. Segmenting public library clients based on their needs using the network: (3) Artificial neural, hierarchical analysis and Kano model. Information Research and Public Libraries 20: 534-513
  40. Hayati, Zahir. 1393. Segmenting public library clients based on their needs using the network: (3) Artificial neural, hierarchical analysis and Kano model. Information Research and Public Libraries 20: 534-513
  41. Heidelberg Shani, G., and A. Gunawardana. 2011. Evaluating recommendation systems. In Recommender systems handbook (pp. 257-297). Boston, MA: Springer. [DOI:10.1007/978-0-387-85820-3_8]
  42. Heidelberg Shani, G., and A. Gunawardana. 2011. Evaluating recommendation systems. In Recommender systems handbook (pp. 257-297). Boston, MA: Springer. [DOI:10.1007/978-0-387-85820-3_8]
  43. Jahrer, M., A. Töscher, and R. Legenstein. 2010. Combining predictions for accurate recommender systems. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD "10, 693-702, New York, NY, USA. ACM. [DOI:10.1145/1835804.1835893]
  44. Jahrer, M., A. Töscher, and R. Legenstein. 2010. Combining predictions for accurate recommender systems. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD "10, 693-702, New York, NY, USA. ACM. [DOI:10.1145/1835804.1835893]
  45. Jarvelin, K. 1986. Information Processing and Management: an International Journal 22 (6): 523-548. [DOI:10.1016/0306-4573(86)90103-2]
  46. Jarvelin, K. 1986. Information Processing and Management: an International Journal 22 (6): 523-548. [DOI:10.1016/0306-4573(86)90103-2]
  47. Järvelin, K., and P. Ingwersen. 2004. Information Seeking Research Needs Extension towards Tasks and Technology. Information Research 10 (1): 212.
  48. Järvelin, K., and P. Ingwersen. 2004. Information Seeking Research Needs Extension towards Tasks and Technology. Information Research 10 (1): 212.
  49. Jones, R., and F. Diaz. 2007. Temporal profiles of queries. ACM Trans. Information Systems 25 (3): 14. [DOI:10.1145/1247715.1247720]
  50. Jones, R., and F. Diaz. 2007. Temporal profiles of queries. ACM Trans. Information Systems 25 (3): 14. [DOI:10.1145/1247715.1247720]
  51. Joshi, P. A, S. M. Nikose. 2013. Information seeking Behaviours of Users: A Case Study of Private Higher Technical Libraries in Chandrapur District. Retrived from http:// Eprints.rclis.org/3794. (accessed Jan. 20, 2016).
  52. Joshi, P. A, S. M. Nikose. 2013. Information seeking Behaviours of Users: A Case Study of Private Higher Technical Libraries in Chandrapur District. Retrived from http:// Eprints.rclis.org/3794. (accessed Jan. 20, 2016).
  53. Khoshbaf, Mehdi. 1392. Investigating the information retrieval behavior of virtual graduate students of Imam Reza University based on Ellis model. Master Thesis. Imam Reza University of Mashhad.
  54. Khoshbaf, Mehdi. 1392. Investigating the information retrieval behavior of virtual graduate students of Imam Reza University based on Ellis model. Master Thesis. Imam Reza University of Mashhad.
  55. Kohrs, A., & B.Merialdo. 1999. Improving collaborative filtering with multimedia indexing techniques to create user-adapting web sites. In Proceedings of the seventh ACM international conference on Multimedia (Part 1) (pp. 27-36). New York, US. [DOI:10.1145/319463.319467]
  56. Kohrs, A., & B.Merialdo. 1999. Improving collaborative filtering with multimedia indexing techniques to create user-adapting web sites. In Proceedings of the seventh ACM international conference on Multimedia (Part 1) (pp. 27-36). New York, US. [DOI:10.1145/319463.319467]
  57. Lavrenko, V., and W. B. Croft. 2001. Relevance-based language models. ACM SIGIR (Special Interest Group on Information Retrieval) (2): 120-127. [DOI:10.1145/383952.383972]
  58. Lavrenko, V., and W. B. Croft. 2001. Relevance-based language models. ACM SIGIR (Special Interest Group on Information Retrieval) (2): 120-127. [DOI:10.1145/383952.383972]
  59. Liu, X., & W. B. Croft. 2004. Cluster-based retrieval using language models. In SIGIR '04: Proceedings of the 27th annual international ACM SIGIR conference on research and development in information retrieval. 186-193. New York, NY: ACM. [DOI:10.1145/1008992.1009026]
  60. Liu, X., & W. B. Croft. 2004. Cluster-based retrieval using language models. In SIGIR '04: Proceedings of the 27th annual international ACM SIGIR conference on research and development in information retrieval. 186-193. New York, NY: ACM. [DOI:10.1145/1008992.1009026]
  61. Lu, H., M. Zhang & S. Ma. 2018. Between Clicks and Satisfaction: Study on Multi-Phase User Preferences and Satisfaction for Online News Reading. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (pp. 435-444). ACM. New York, US. [DOI:10.1145/3209978.3210007]
  62. Lu, H., M. Zhang & S. Ma. 2018. Between Clicks and Satisfaction: Study on Multi-Phase User Preferences and Satisfaction for Online News Reading. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (pp. 435-444). ACM. New York, US. [DOI:10.1145/3209978.3210007]
  63. Mohamad Esmaeeli, S., & J.Naeimi. 2017. Investigation through and Clustering the Information Needs and Information Seeking Behavior of Seminary and University Students of Khorasan-e- Razavi with Neural Network Analysis, Human Information Interaction 3 (4): 1. Magiran.com/p1517874
  64. Mohamad Esmaeeli, S., & J.Naeimi. 2017. Investigation through and Clustering the Information Needs and Information Seeking Behavior of Seminary and University Students of Khorasan-e- Razavi with Neural Network Analysis, Human Information Interaction 3 (4): 1. Magiran.com/p1517874
  65. Naeimi, J., & S. Mohamad Esmaeeli. 2016. The Assessment of Information- Seeking Behavior of Medical Sciences University Researchers of Khorasan Razavi using Neural Network Approach, Library and Information Research Journal 6 (2): 80-96. Magiran.com/p1625373
  66. Naeimi, J., & S. Mohamad Esmaeeli. 2016. The Assessment of Information- Seeking Behavior of Medical Sciences University Researchers of Khorasan Razavi using Neural Network Approach, Library and Information Research Journal 6 (2): 80-96. Magiran.com/p1625373
  67. _____ & H. Heidari. 2018. The Assessment of Information- Seeking Behavior of Khorasan Razavi Seminary Students with Neural Network Approach, Journal of Sciences and Techniques of Information Management 4 (1): 91-118. Magiran.com/p1834056
  68. _____ & H. Heidari. 2018. The Assessment of Information- Seeking Behavior of Khorasan Razavi Seminary Students with Neural Network Approach, Journal of Sciences and Techniques of Information Management 4 (1): 91-118. Magiran.com/p1834056
  69. Paramita, M. L., M. Sanderson, & P. Clough. 2009. Diversity in photo retrieval: overview of the ImageCLEFPhoto task 2009. In Workshop of the Cross-Language Evaluation Forum for European Languages (pp. 45-59). Springer, Berlin, Heidelberg. [DOI:10.1007/978-3-642-15751-6_6]
  70. Paramita, M. L., M. Sanderson, & P. Clough. 2009. Diversity in photo retrieval: overview of the ImageCLEFPhoto task 2009. In Workshop of the Cross-Language Evaluation Forum for European Languages (pp. 45-59). Springer, Berlin, Heidelberg. [DOI:10.1007/978-3-642-15751-6_6]
  71. Qasemokhani, Sakineh. 1393. Investigating the information seeking behavior of the elderly in public libraries in Khoy. Master Thesis, Tehran Teacher Training University.
  72. Qasemokhani, Sakineh. 1393. Investigating the information seeking behavior of the elderly in public libraries in Khoy. Master Thesis, Tehran Teacher Training University.
  73. Rad, I., 2009. Information seeking behavior of Islamic Azad University graduate students in using the World Wide Web, Journal of Library and Information Science 12 (3): 3.
  74. Rad, I., 2009. Information seeking behavior of Islamic Azad University graduate students in using the World Wide Web, Journal of Library and Information Science 12 (3): 3.
  75. Radlinski, F., and S. Dumais. 2006. Improving personalized web search using result diversification. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 691-692). Washington, USA. [DOI:10.1145/1148170.1148320]
  76. Radlinski, F., and S. Dumais. 2006. Improving personalized web search using result diversification. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 691-692). Washington, USA. [DOI:10.1145/1148170.1148320]
  77. Robertson, S. E., and K. S. Jones. 1976. Relevance weighting of search terms. Journal of the American Society of Information Science 27 (3): 129-146. [DOI:10.1002/asi.4630270302]
  78. Robertson, S. E., and K. S. Jones. 1976. Relevance weighting of search terms. Journal of the American Society of Information Science 27 (3): 129-146. [DOI:10.1002/asi.4630270302]
  79. Roy, N. 2001. Toward optimal active learning through Montecarlo estimation of error reduction. International Conference on Machine Learning (ICML). Williamstow, USA. pp. 441-448.
  80. Roy, N. 2001. Toward optimal active learning through Montecarlo estimation of error reduction. International Conference on Machine Learning (ICML). Williamstow, USA. pp. 441-448.
  81. Salton, G., and C. Buckley. 1990. Improving retrieval performance by relevance feedback. Journal of Information Science 41 (4): 288-297. https://doi.org/10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H [DOI:10.1002/(SICI)1097-4571(199006)41:43.0.CO;2-H]
  82. Salton, G., and C. Buckley. 1990. Improving retrieval performance by relevance feedback. Journal of Information Science 41 (4): 288-297. https://doi.org/10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H [DOI:10.1002/(SICI)1097-4571(199006)41:43.0.CO;2-H]
  83. Sanderson, M., J. Tang, T. Arni, & P. Clough. 2009. What else is there? search diversity examined. In European Conference on Information Retrieval (pp. 562-569). Springer, Berlin, Heidelberg. [DOI:10.1007/978-3-642-00958-7_51]
  84. Sanderson, M., J. Tang, T. Arni, & P. Clough. 2009. What else is there? search diversity examined. In European Conference on Information Retrieval (pp. 562-569). Springer, Berlin, Heidelberg. [DOI:10.1007/978-3-642-00958-7_51]
  85. Shani, G., and A. Gunawardana. 2011. Evaluating recommendation systems. In Recommender systems handbook (pp. 257-297). Boston, MA: Springer. [DOI:10.1007/978-0-387-85820-3_8]
  86. Shani, G., and A. Gunawardana. 2011. Evaluating recommendation systems. In Recommender systems handbook (pp. 257-297). Boston, MA: Springer. [DOI:10.1007/978-0-387-85820-3_8]
  87. Tang, J., and M. Sanderson. 2010. Evaluation and user preference study on spatial diversity. In European Conference on Information Retrieval (pp. 179-190). Springer, Berlin, Heidelberg. [DOI:10.1007/978-3-642-12275-0_18]
  88. Tang, J., and M. Sanderson. 2010. Evaluation and user preference study on spatial diversity. In European Conference on Information Retrieval (pp. 179-190). Springer, Berlin, Heidelberg. [DOI:10.1007/978-3-642-12275-0_18]
  89. Tao, T. and C. Zhai. 2006. Regularized estimation of mixture models for robust pseudo-relevance feedback. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 162-169). USA. [DOI:10.1145/1148170.1148201]
  90. Tao, T. and C. Zhai. 2006. Regularized estimation of mixture models for robust pseudo-relevance feedback. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 162-169). USA. [DOI:10.1145/1148170.1148201]
  91. Ticineto Clough, P. 2009. The new empiricism: Affect and sociological method. European Journal of Social Theory 12 (1): 43-61. [DOI:10.1177/1368431008099643]
  92. Ticineto Clough, P. 2009. The new empiricism: Affect and sociological method. European Journal of Social Theory 12 (1): 43-61. [DOI:10.1177/1368431008099643]
  93. Yadav, S., S. Ramesh, S. Saha, and A. Ekbal. 2021. Relation extraction from biomedical and clinical text: Unified multitask learning framework. IEEE/ACM Transactions on Computational Biology and Bioinformatics. [DOI:10.1109/TCBB.2020.3020016]
  94. Yadav, S., S. Ramesh, S. Saha, and A. Ekbal. 2021. Relation extraction from biomedical and clinical text: Unified multitask learning framework. IEEE/ACM Transactions on Computational Biology and Bioinformatics. [DOI:10.1109/TCBB.2020.3020016]
  95. Yan, X., J. Guo, S. Liu, X. Cheng, and Y. Wang. 2013. Learning topics in short texts by non-negative matrix factorization on term correlation matrix. In Proceedings of the 2013 SIAM International Conference on Data Mining (pp. 749-757). Society for Industrial and Applied Mathematics. Germany. [DOI:10.1137/1.9781611972832.83]
  96. Yan, X., J. Guo, S. Liu, X. Cheng, and Y. Wang. 2013. Learning topics in short texts by non-negative matrix factorization on term correlation matrix. In Proceedings of the 2013 SIAM International Conference on Data Mining (pp. 749-757). Society for Industrial and Applied Mathematics. Germany. [DOI:10.1137/1.9781611972832.83]
  97. Yao, C., J. Bu, C. Wu, and G. Chen. 2013. Semi-supervised spectral hashing for fast similarity search. Neuro computing 101 (1): 52-58. [DOI:10.1016/j.neucom.2012.06.035]
  98. Yao, C., J. Bu, C. Wu, and G. Chen. 2013. Semi-supervised spectral hashing for fast similarity search. Neuro computing 101 (1): 52-58. [DOI:10.1016/j.neucom.2012.06.035]
  99. Yari, S., & H. Ahmadi. 2014. A Review on Information Seeking Behaviour Literature in Iran, Information Sciences & Technology 30 (1): 173-197. Magiran.com/p1338101
  100. Yari, S., & H. Ahmadi. 2014. A Review on Information Seeking Behaviour Literature in Iran, Information Sciences & Technology 30 (1): 173-197. Magiran.com/p1338101
  101. Zhang, Y., V. Zhong, V. Chen, A. Angeli, and C. Manning. 2017. Position-aware Attention and Supervised Data Improve Slot Filling. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 35-45. USA. [DOI:10.18653/v1/D17-1004]
  102. Zhang, Y., V. Zhong, V. Chen, A. Angeli, and C. Manning. 2017. Position-aware Attention and Supervised Data Improve Slot Filling. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 35-45. USA. [DOI:10.18653/v1/D17-1004]
  103. Zhang, Y., H. Lin, Z. Yang, J. Wang, S. Zhang, & L. Yang. 2018a. A hybrid model based on neural networks for biomedical relation extractionJournal of Biomedical Informatics, 81: 83-92. [DOI:10.1016/j.jbi.2018.03.011]
  104. Zhang, Y., H. Lin, Z. Yang, J. Wang, S. Zhang, & L. Yang. 2018a. A hybrid model based on neural networks for biomedical relation extractionJournal of Biomedical Informatics, 81: 83-92. [DOI:10.1016/j.jbi.2018.03.011]
  105. Zhang, Y., S. Liu, J. Tan, G. Jiang, & Q. Zhu. 2018b. Effects of risks on the performance of business process outsourcing projects: The moderating roles of knowledge management capabilities. International Journal of Project Management 36 (4): 627-639. [DOI:10.1016/j.ijproman.2018.02.002]
  106. Zhang, Y., S. Liu, J. Tan, G. Jiang, & Q. Zhu. 2018b. Effects of risks on the performance of business process outsourcing projects: The moderating roles of knowledge management capabilities. International Journal of Project Management 36 (4): 627-639. [DOI:10.1016/j.ijproman.2018.02.002]
  107. Zhao, J, & Y. Yun. 2009. A proximity language model for information retrieval. In SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on research and development in information retrieval. 291-298. New York, NY: ACM. [DOI:10.1145/1571941.1571993]
  108. Zhao, J, & Y. Yun. 2009. A proximity language model for information retrieval. In SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on research and development in information retrieval. 291-298. New York, NY: ACM. [DOI:10.1145/1571941.1571993]
  109. Zheng, W., H. Lin, L. Luo, Z. Zhao, Z. Li, Y. Zhang, & J. Wang. 2017. An attention-based effective neural model for drug-drug interactions extraction. BMC Bioinformatics 18 (1): 445-451. [DOI:10.1186/s12859-017-1855-x]
  110. Zheng, W., H. Lin, L. Luo, Z. Zhao, Z. Li, Y. Zhang, & J. Wang. 2017. An attention-based effective neural model for drug-drug interactions extraction. BMC Bioinformatics 18 (1): 445-451. [DOI:10.1186/s12859-017-1855-x]
  111. Zhou, N., J. Du, X. Yao, W. Cui, Z. Xue, & M. Liang. 2019. A content search method for security topics in microblog based on deep reinforcement learning. World Wide Web 23 (1): 75-101 [DOI:10.1007/s11280-019-00697-7]
  112. Zhou, N., J. Du, X. Yao, W. Cui, Z. Xue, & M. Liang. 2019. A content search method for security topics in microblog based on deep reinforcement learning. World Wide Web 23 (1): 75-101 [DOI:10.1007/s11280-019-00697-7]

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  • تاریخ دریافت: 19 آذر 1401
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