پایان نامه استفاده از کاربرانی با دقت پیشگویی بالا در سیستم های فیلترینگ اشتراکی

فهرست:

فصل 1 : مقدمه­.............................................................................................................................................1

1-1- پیشگفتار..............................................................................................................................................2

1-2- موتورهای جستجوگر........................................................................................................................2

1-2-1- موتورهای جستجوگر پیمایشی..................................................................................................3

1-2- 2- فهرست­های تکمیل دستی........................................................................................................3

1-2-3- موتورهای جستجوگر ترکیبی.....................................................................................................4

1-2-4- ابرجستجوگرها...............................................................................................................................4

1-3- سیستم­های پیشنهادگر....................................................................................................................5

1-3-1- سیستم پیشنهادگر بر اساس فیلترینگ اشتراکی..................................................................7

1-3-2- سیستم پیشنهادگر بر اساس محتوا..........................................................................................8

1-3-3- سیستم پیشنهادگر بر اساس آمار گیری..................................................................................8

1-3-4- سیستم پیشنهادگر بر اساس سود.............................................................................................9

1-3-5- سیستم پیشنهادگر بر اساس دانش..........................................................................................9

1-3-6- سیستم پیشنهادگر ترکیبی........................................................................................................9

1-4- بررسی سایت MovieLens........................................................................................................10

1-5- اهداف پایان نامه.............................................................................................................................13

1-6- ساختار پایان نامه............................................................................................................................14

فصل 2 : روش فیلترینگ اشتراکی.........................................................................................................15

2-1- پیشگفتار...........................................................................................................................................16

2-2- مروری بر کارهای انجام شده در این راستا................................................................................16

2-3- مبانی فیلترینگ اشتراکی..............................................................................................................21

2-4-  وظایف فیلترینگ اشتراکی..........................................................................................................22

2-4-1- پیشنهاد........................................................................................................................................23

2-4-2- پیش­بینی.....................................................................................................................................23

2-5- دسته بندی متدهای فیلترینگ اشتراکی..................................................................................23

2-5-1- فیلترینگ اشتراکی مبتنی بر حافظه....................................................................................24

2-5-1-1- فیلترینگ اشتراکی مبتنی بر حافظه با پیش­بینی بر اساس کاربران.........................25

2-5-1-2- فیلترینگ اشتراکی مبتنی بر حافظه با پیش­بینی بر اساس اقلام..............................25

2-5-1- 3- تفاوت فیلترینگ اشتراکی بر اساس کاربران و بر اساس اقلام...................................26

2-5-2- فیلترینگ اشتراکی مبتنی بر مدل.........................................................................................26

2-6- نحوه­ تشخیص علائق کاربران.......................................................................................................27

2-6-1- تشخیص علائق به صورت صریح.............................................................................................27

2-6-2- تشخیص علائق به صورت ضمنی...........................................................................................27

2-7- محاسبه­ شباهت...............................................................................................................................28

2-7-1- معیار همبستگی پیرسون.........................................................................................................28

2-7-2- معیار اندازه­گیری کسینوس.....................................................................................................29

2-8- انتخاب همسایه...............................................................................................................................30

2-8-1- استفاده از حد آستانه................................................................................................................30

2-8-2- انتخاب تعداد ثابتی از همسایگان...........................................................................................30

2-9- پیش­بینی و تخمین رتبه...............................................................................................................31

2-9-1- استفاده از امتیازهای خام.........................................................................................................31

2-9-2- استفاده از امتیازهای نرمال شده............................................................................................31

2-10- مشکلات فیلترینگ اشتراکی.....................................................................................................32

2-10-1- پراکنده بودن داده...................................................................................................................32

2-10-2- مقیاس پذیری.........................................................................................................................32

2-10-3- اقلام مشابه...............................................................................................................................33

2-10-4- گری­شیپ.................................................................................................................................33

2-11- بررسی چگونگی کارکرد سایت آمازون....................................................................................33

فصل 3 : روش محتوا محور.....................................................................................................................36

3-1- پیشگفتار...........................................................................................................................................37

3-2- روند کار روش محتوا محور...........................................................................................................37

3-2-1- تحلیل­گر محتوا..........................................................................................................................38

3-2-2- یادگیرنده نمایه .................................................................................................................39

3-2-3- جزء فیلترینگ............................................................................................................................42

3-3- مزایای روش محتوا محور..............................................................................................................42

3-3-1- استقلال کاربر.............................................................................................................................42

3-3-2- شفافیت........................................................................................................................................42

3-3-3- قلم جدید.....................................................................................................................................43

3-4- معایب روش محتوا محور...............................................................................................................43

3-4-1- کمبود محتوا...............................................................................................................................43

3-4-2- خصوصی سازی افزون...............................................................................................................43

3-4-3- کاربر جدید..................................................................................................................................44

فصل 4 : روش پیشنهادی.........................................................................................................................45

4-1- پیشگفتار...........................................................................................................................................46

4-2- مروری بر کارهای انجام شده در این راستا................................................................................46

4-3- مقدمه­ای بر روش پیشنهادی........................................................................................................48

4-4- روش پیشنهادی..............................................................................................................................48

4-4-1- پیش­ پردازش..............................................................................................................................49

4-4-1-1- پیش پردازش بر روی پایگاه داده MovieLens........................................................49

4-4-1-2- پیش پردازش بر روی پایگاه داده EachMovie........................................................50

4-4-2- وزن­دهی به اقلام........................................................................................................................51

4-4-3- انتخاب­همسایگی........................................................................................................................53

4-4-4- پیش­بینی....................................................................................................................................54

فصل 5 : آزمایش­ها و نتایج......................................................................................................................56

5-1- پایگاه داده­های مورد استفاده........................................................................................................57

5-2- نحوه­ اجرای روش پیشنهادی روی پایگاه داده­ MovieLens..............................................57

5-3- نحوه­ اجرای روش پیشنهادی روی پایگاه داده ٍEachMovie..............................................58

5-4- معیارهای­ارزیابی..............................................................................................................................58

5-4-1- میانگین خطای مطلق...............................................................................................................58

5-4-2- دقت و فراخوانی.........................................................................................................................59

5-4-3- معیار ارزیابیF1........................................................................................................................60

5-5- ارزیابی روش پیشنهادی توسط معیارهای معرفی شده...........................................................61

فصل 6 : بحث و نتیجه­گیری...................................................................................................................66

6-1- بحث...................................................................................................................................................67

6-2- نتیجه­گیری......................................................................................................................................67

6-4- پیشنهادات........................................................................................................................................68

مراجع...........................................................................................................................................................69

منبع:

M. Coyle and B. Smyth, “Information recovery and discovery in collaborative web search”, In Proceedings of the European Conference on Information retrieval, pp. 356–367, 2007.

S. Feldman and C. Sherman, “The High Cost of Not Finding Information”, In (IDCWhite Paper), IDC Group, 2000.

R. Burke, “Hybrid recommender systems: Survey and experiments”, Journal of User Modeling and User-Adapted Interaction, vol. 12, no. 4, pp. 331-370, 2002.

R. Bell and Y. Koren, “ Lessons from the Netflix prize challenge”, Journal of  SIGKDD Explorations, vol. 9, no. 2, pp. 75–79, 2007.

http://www.Movielens.org/

D. Goldberg, D. Nichols, BM. Oki and D. Terry, “Using collaborative filtering to weave an information tapestry”, Journal of Communication of the ACM, vol. 35, no. 12, pp. 61-70, 1992.

P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom and J. Riedl, “GroupLens : An open architecture for collaborative filtering of netnews”, Proceedings of the 1994 Conference on Computer Supported Cooperative Work, USA, pp. 175–186, 1994.

J. Constan, B. Miller, D. Maltz, J. Herlocker, L. Gordon and J. Riedl, “applying collaborative filtering to Usenet news”, Journal of communications of the ACM, vol. 40, no. 3, pp.77-87, 1997.

U. Shardanand and P. Maes, “Social information filtering: Algorithms for automating word of mouth ”, Proceedings of ACM CHI’95 Conference on Human Factors in Computing Systems ACM Press, USA, pp. 210–217, 1995.

W. Hil, L. Stead, M. Rosenstein and GW. Furnas, “Recommending and evaluating choices in a virtual community of use”, Proceedings of ACM CHI’95 Conference on Human Factors in Computing Systems ACM Press, USA, pp. 194–201, 1995.

J. Bannett and S. Lanning, “The Netflix Prize”, Proceedings of KDD Cup and Workshop, USA, pp.8-25, Aug 2007.

JS. Breeze, D. Heckermand and C. Kadie, “Empirical analysis of predictive algorithms for collaborative filtering”, Proceedings of the Fourteenth conference on Uncertainty in artificial  intelligence, USA,  pp. 43-52, May 1998.

DM. Pennock, E. Horvitz, S. Lawrence and CL. Giles, “Collaborative filtering by personality diagnosis: A hybrid memory- and model-based approach”, Proceedings of the Sixteenth Annual Conference on Uncertainty in Artificial Intelligence, USA, pp. 473–480, 2000.

I. Im and BH. Kim, “Personalizing the Settings for CF-Based Recommender Systems”, Proceedings of the fourth ACM conference on Recommender systems, New York, pp. 248-254, 2010.

Y. Ge, H. Xiong, A. Tuzhilin and Q. Liu, “Collaborative Filtering  with Collective Training”,  Proceedings of the fifth ACM conference on Recommender systems, New York, pp. 281-284, 2011.

.

R. Hu and P. Pu , “Enhancing Collaborative Filtering Systems with  Personality Information”,  In Proceedings of the fifth ACM conference on Recommender systems, New York, pp. 197-204, 2011.

J.Bobadilla, F. Ortega,A.Hernando and J. Bernal, “Generalization of recommender systems: Collaborative filtering extended to groups of users and restricted to groups of items”, Journal of Expert Systems with Applications , vol. 39, no.1, pp. 172-186, 2012.

J. Comput and S. Technol, “JacUOD: A New Similarity Measurement for Collaborative Filtering”, vol. 27, no. 6, pp. 1252-1260, 2012

.

JF. Huete, J.M. Fernández-Luna, LM. de Campos and MA. Rueda-Morales, “ Using past-prediction accuracy in recommender systems”, Jouranal of Information Science, vol. 199, no. 7, pp. 78-92,  2012.

M.A. Ghazanfar, A. Prugel-Bennett and S. Szedmak, “Kernel-Mapping Recommender system algorithms”, Journal of  Information Science, vol. 208, pp. 81-104, 2012.

G. Tac'acs, I. Pil'a szy, B. N'emeth and D. Tikk, "Major components of the garvity recommendation system", ACM SIGKDD Explorations Newsletter, vol. 2, pp. 80 -83, 2007.

Y. Koren, "Factorization meets the neighborhood: a multifaceted collaborative filtering model", In Proceedings of the International Conference on Knowledge Discovery and Data Mining, Las Vegas, pp. 426-434, 2008.

Y. Koren, "Recent Progress in Collaborative Filtering - Tutorial Notes", In Proceedings of the 2nd ACM Conference on Recommender Systems, 2008.

F. Ricci, L. Rokach and B. Shapira, “Introduction to Recommender Systems Handbook”, Springer, 2011

P. Symeonidis, A. Nanopoulos, A.N. Papadopoulos and Y. Manolopoulos, “Collaborative recommender systems: Combining effectiveness and efficiency”, Journal of Expert Systems with Applications, vol. 34, no. 4, pp. 2995-3013, 2008.

L. Brozovsky and V.Petricek, “Recommender system for online dating service”, In Proceedings of the 6th Conference Znalosti , Czech Republic, pp. 29-40, 2007.

JL. Herlocker, JA. Konstan, A. Borchers and J. Riedl, “An algorithmic framework for performing collaborative filtering”, Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, USA, pp. 230–237, 1999.

B. Sarwar, G. Karypis, J. Konstan and J. Reidl, “Item-based collaborative filtering”, Proceedings of the 10th international conference on World Wide Web, China, pp. 295, 2001.

JL. Herlocker, JA. Konstan, A. Borchers and J. Riedl, “An algorithmic framework for performing collaborative filtering”, Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, USA, pp. 230–237, 1999.

C. Desrosiers and G. Karypis, “A comprehensive survey of neighborhood-based recommendation methods”, Recommender Systems Handbook, Springer, 2011

C.  Basu, H. Hirsh and W. Cohen, “Recommendation as Classification: Using Social and Content-based information in recommendation”, In Proceedings of the Fifteenth National Conference on Artificial Intelligence, USA, pp.714-720, 1998.

L. H. Ungar and D. P. Foster, “Clustering Methods for Collaborative Filtering”, In Proceedings of the Workshop on Recommender Systems at the 15th National Conference on Artificial Intelligence, USA, AAAI Press, pp. 112-125, 1998.

D. Kelly and T. Teevan, “Implicit feedback for inferring user preferences: a bibliography”, ACM SIGIR Forum, vol. 37, no. 2, pp.18-28, 2003.

D.M. Nichols, “Implicit Rating and Fitering”, In Proceedings of the 5th DELOS Workshop on Filtering and Collaborative Filtering, Hungary, pp. 31-36, 1997.

D.W. Oard and J. Kim, “Implicit Feedback for Recommender Systems”, In Proceedings of the Workshop on Recommender Systems at the 15th National Conference on Artificial Intelligence, USA, pp. 81-83,1998.

R. Rafter and  B. Smyth, “Passive Profiling from Server  Logs in an Online Recruitment Environment”, In Proceedings of  the Workshop on Intelligent Techniques for Web Personalization at the 17th International Joint Conference on Artificial Intelligence, USA, pp. 35-41, 2001.

G. Salton and C. Buckley, “Term-weighting approaches in automatic text retrieval”, Journal of Information Processing and Management, vol. 24, no. 5, pp. 513-523, 1998.

G. Salton and M. Mcgill, “Introduction to Modern Information Retrieval”, McGraw-Hill, New York, 1983.

P. Melville, R.J. Mooney and R. Nagarajan, “Contenet -boosted collaborative filtering for improved recommendations”, In Proceedings of the 18th International conference on Artificial Intelligence, pp. 187-192, 2002.

M. Balabanovic and Y. Shoham, “Fab: Content-Based, Collaborative Recommendation”, Journal of Communications of the ACM, vol. 40, no. 3, pp. 16-72, 1997.

N. Good, J. B. Schafer, J.A. konstan, A. Borchers, B. M. Sarwar, J. L. Herlocker and J. Riedl, “Combining  Collaborative Filtering with Personal Agents  for Better Recommendations”, In Proceedings of  the 16th National  Conference on Artificial Intelligence, USA, pp. 430-446, 1999.

M.A. Pazzani, “Framework for Collaborative, Content-Based and Demographic Filtering”, Journal of Artificial Intelligence Review, vol. 13,  no. 5-6, pp. 393-408, 1999.

B.M. Sarwbar, J.A. Konstan, A. Borchers, J.L. Herlocker, B.N. Miller and J. Riedl, “Using Fitering Agents to Improve Prediction Quality in the GroupLens Research Collaborative Filtering System”, In Proceedings of  the ACM Conference on Computer Supported Cooperative Work, USA, pp. 345-354, 1998.

http://kunuk.wordpress.com/2012/03/04/how-does-the-amazon- recommendation-system-work-analyze-the-algorithm-and-make-a-prototype-that-visualizes-the-algorithm

G. Linden, B. Smith and J. York, “Amazon.com Recommendations: Item-to-Item Collaborative Filtering”, Journal of IEEE Internet Computing, vol. 7, no.1, pp. 76-80, 2003.

M.J. Pazzani and D. Billsus,” Content-based recommendation systems”, The adaptive web, Springer-Verlag Berlin, Heidelberg, pp. 325-341, 2007.

J.S. Breese, D. Heckerman and C.M. Kadie, “ Empirical analysis of predictive algorithms for collaborative filtering”, In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, UAI, pp. 43–52, 1998.

J.L. Herlocker, J.A. Konstan, A. Borchers and J. Riedl, “An algorithmic frame work for performing collaborative filtering”, In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, New York, pp. 230–237, 1999.

K. Yu, X. Xu, M. Ester and H-P. Kriegel, “Feature weighting and instance selection for collaborative filtering: An information-theoretic approach”, Journal of Knowledge and Information Systems, vol. 5, no. 2, pp. 201–224, 2003.

R. Jin, J.Y. Chai and L. Si, “An automatic weighting scheme for collaborative filtering”,  In Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 337–344, New York, 2004.

L. Baltrunas and F. Ricci, “Dynamic item weighting and selection for collaborative filtering”, in Web mining 2.0 Workshop, ECML-PKDD, Springer, 2007.

H. Luo, Ch. Niu, R. Shen and C. Ullrich, “A collaborative filtering framework based on both local user similarity and global user similarity”, Journal of  Machine Learning, vol. 72, no. 3, pp. 231-245, 2008.

L. Baltrunas and F. Ricci, “Item Weighting Techniques for Collaborative Filtering”, Knowledge Discovery Enhanced with Semantic and Social Information, vol. 220, pp. 109-126, 2009

A. Said, J.J. Brijnesh and S. Albayrak, ”Analyzing weighting

schemes in collaborative filtering: cold start, post cold start and power users”, In Proceedings of the 27th Annual ACM Symposium on Applied Computing, New York, pp. 2035-2040, 2012.