Введение 3
1. Рекомендательные системы 6
2. Построение рекомендательных систем 12
3. Задача коллаборативной фильтрации 14
4. Модель предпочтения SVD 18
5. Обратная связь в рекомендательных системах 20
6. Учет контекста в рекомендательных системах 23
Список использованных источников 24
В научной литературе проблематика выбора наилучшего критерия отбора рекламного объявления стала активно изучаться в последние 10-15 лет. Встречается достаточно большое количество работ с различными критериями: классический CPM (Cost — Per — Million) = CTR x Bid, bid x quality + v, релевантность, информация о продавце и бренде, позиционность, информация о пользователе, bidk x CTR...
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Рекомендательные системы. А также похожие готовые работы: страница 9 #1506053
Артикул: 1506053
- Предмет: Прикладная информатика
- Уникальность: 86% (Антиплагиат.ВУЗ)
- Разместил(-а): 103 Егор в 2016 году
- Количество страниц: 27
- Формат файла: docx
970p.
1. Andrew I Schein, Alexandrin Popescul, Lyle H Ungar, and David M Pennock. Methods and metrics for cold-start recommendations. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pages 253-260. ACM, 2002.
2. Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web, pages 285-295. ACM, 2001.
3. Balazs Hidasi and Domonkos Tikk. Context-aware item-to-item recommendation within the factorization framework. In Proceedings of the 3rd Workshop on Context-awareness in Retrieval and Recommendation, pages 19-25. ACM, 2013.
4. Balazs Hidasi and Domonkos Tikk. Fast als-based tensor factorization for context-aware recommendation from implicit feedback. In Machine Learning and Knowledge Discovery in Databases, pages 67-82. Springer, 2012.
5. Chengjie Sun, Lei Lin, Yuan Chen, and Bingquan Liu. Expanding user features with social relationships in social recommender systems. In Natural Language Processing and Chinese Computing, pages 247-254. Springer, 2013.
6. Christian Desrosiers and George Karypis. A comprehensive survey of neighborhood- based recommendation methods. In Recommender systems handbook, pages 107-144. Springer, 2011.
7. Daniel Lemire and Anna Maclachlan. Slope one predictors for online rating-based collaborative filtering. In SDM, volume 5, pages 1-5. SIAM, 2005.
8. Emmanuel J Candes and Benjamin Recht. Exact matrix completion via convex optimization. Foundations of Computational mathematics, 9(6):717-772, 2009.
9. Gediminas Adomavicius and Alexander Tuzhilin. Context-aware recommender systems. In Recommender systems handbook, pages 217-253. Springer, 2011.
10. Gediminas Adomavicius and Alexander Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. Knowledge and Data Engineering, IEEE Transactions on, 17(6):734-749, 2005.
11. Gregory Piatetsky. Interview with simon funk. ACM SIGKDD Explorations Newsletter, 9(1):38-40, 2007.
12. Harald Steck. Training and testing of recommender systems on data missing not at random. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 713-722. ACM, 2010.
13. Istvan Pilaszy and Domonkos Tikk. Recommending new movies: even a few ratings are more valuable than metadata. In Proceedings of the third ACM conference on Recommender systems, pages 93-100. ACM, 2009.
14. Istvan Pilaszy, David Zibriczky, and Domonkos Tikk. Fast als-based matrix factorization for explicit and implicit feedback datasets. In Proceedings of the fourth ACM conference on Recommender systems, pages 71-78. ACM, 2010.
15. Ivan Cantador, Peter Brusilovsky, and Tsvi Kuflik. 2nd workshop on information heterogeneity and fusion in recommender systems (hetrec 2011). In Proceedings of the 5th ACM conference on Recommender systems, RecSys 2011, New York, NY, USA, 2011. ACM.
16. James Bennett and Stan Lanning. The netflix prize. In Proceedings of KDD cup and workshop, volume 2007, page 35, 2007.
17. Jun Wang, Arjen P De Vries, and Marcel JT Reinders. Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pages 501-508. ACM, 2006.
18. Keith Cheverst, Nigel Davies, Keith Mitchell, Adrian Friday, and Christos Efstratiou. Developing a context-aware electronic tourist guide: some issues and experiences. In Proceedings of the SIGCHI conference on Human factors in computing systems, pages 17-24. ACM, 2000.
19. Liangjie Hong, Aziz S Doumith, and Brian D Davison. Co-factorization machines: modeling user interests and predicting individual decisions in twitter. In Proceedings of the sixth ACM international conference on Web search and data mining, pages 557-566. ACM, 2013.
20. Nathan Srebro, Tommi Jaakkola, et al. Weighted low-rank approximations. In ICML, volume 3, pages 720-727, 2003.
21. Steffen Rendle and Lars Schmidt-Thieme. Pairwise interaction tensor factorization for personalized tag recommendation. In Proceedings of the third ACM international conference on Web search and data mining, pages 81-90. ACM, 2010.
22. Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty- Fifth Conference on Uncertainty in Artificial Intelligence, pages 452-461. AUAI Press, 2009.
23. Steffen Rendle, Zeno Gantner, Christoph Freudenthaler, and Lars Schmidt-Thieme. Fast context-aware recommendations with factorization machines. In Proceedings of the 34th ACM SIGIR Conference on Reasearch and Development in Information Retrieval. ACM, 2011.
24. Steffen Rendle. Factorization machines with libfm. ACM Transactions on Intelligent Systems and Technology (TIST), 3(3):57, 2012.
25. Steffen Rendle. Factorization machines. In Proceedings of the 10th IEEE International Conference on Data Mining. IEEE Computer Society, 2010.
26. Tamara G Kolda and Brett W Bader. Tensor decompositions and applications. SIAM review, 51(3):455-500, 2009.
27. Tianqi Chen, Weinan Zhang, Qiuxia Lu, Kailong Chen, Zhao Zheng, and Yong Yu. Svdfeature: a toolkit for feature-based collaborative filtering. The Journal of Machine Learning Research, 13(1):3619-3622, 2012.
28. Tianqi Chen, Zhao Zheng, Qiuxia Lu, Weinan Zhang, and Yong Yu. Feature-based matrix factorization. arXiv preprint arXiv:1109.2271, 2011.
29. Yehuda Koren, Robert Bell, and Chris Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8):30-37, 2009.
30. Yehuda Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the tfth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 426-434. ACM, 2008.
31. Yifan Hu, Yehuda Koren, and Chris Volinsky. Collaborative filtering for implicit feedback datasets. In Data Mining, 2008. ICDM’08. Eighth IEEE International Conference on, pages 263-272. IEEE, 2008.
32. Yunhong Zhou, Dennis Wilkinson, Robert Schreiber, and Rong Pan. Large-scale parallel collaborative filtering for the netflix prize. In Algorithmic Aspects in Information and Management, pages 337-348. Springer, 2008.
33. Zeno Gantner, Steffen Rendle, and Lars Schmidt-Thieme. Factorization models for context-/time-aware movie recommendations. In Proceedings of the Workshop on Context-Aware Movie Recommendation, pages 14-19. ACM, 2010.
34. К. В. Воронцов. Математические методы обучения по прецедентам (теория обучения машин). Москва, 2011.
2. Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web, pages 285-295. ACM, 2001.
3. Balazs Hidasi and Domonkos Tikk. Context-aware item-to-item recommendation within the factorization framework. In Proceedings of the 3rd Workshop on Context-awareness in Retrieval and Recommendation, pages 19-25. ACM, 2013.
4. Balazs Hidasi and Domonkos Tikk. Fast als-based tensor factorization for context-aware recommendation from implicit feedback. In Machine Learning and Knowledge Discovery in Databases, pages 67-82. Springer, 2012.
5. Chengjie Sun, Lei Lin, Yuan Chen, and Bingquan Liu. Expanding user features with social relationships in social recommender systems. In Natural Language Processing and Chinese Computing, pages 247-254. Springer, 2013.
6. Christian Desrosiers and George Karypis. A comprehensive survey of neighborhood- based recommendation methods. In Recommender systems handbook, pages 107-144. Springer, 2011.
7. Daniel Lemire and Anna Maclachlan. Slope one predictors for online rating-based collaborative filtering. In SDM, volume 5, pages 1-5. SIAM, 2005.
8. Emmanuel J Candes and Benjamin Recht. Exact matrix completion via convex optimization. Foundations of Computational mathematics, 9(6):717-772, 2009.
9. Gediminas Adomavicius and Alexander Tuzhilin. Context-aware recommender systems. In Recommender systems handbook, pages 217-253. Springer, 2011.
10. Gediminas Adomavicius and Alexander Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. Knowledge and Data Engineering, IEEE Transactions on, 17(6):734-749, 2005.
11. Gregory Piatetsky. Interview with simon funk. ACM SIGKDD Explorations Newsletter, 9(1):38-40, 2007.
12. Harald Steck. Training and testing of recommender systems on data missing not at random. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 713-722. ACM, 2010.
13. Istvan Pilaszy and Domonkos Tikk. Recommending new movies: even a few ratings are more valuable than metadata. In Proceedings of the third ACM conference on Recommender systems, pages 93-100. ACM, 2009.
14. Istvan Pilaszy, David Zibriczky, and Domonkos Tikk. Fast als-based matrix factorization for explicit and implicit feedback datasets. In Proceedings of the fourth ACM conference on Recommender systems, pages 71-78. ACM, 2010.
15. Ivan Cantador, Peter Brusilovsky, and Tsvi Kuflik. 2nd workshop on information heterogeneity and fusion in recommender systems (hetrec 2011). In Proceedings of the 5th ACM conference on Recommender systems, RecSys 2011, New York, NY, USA, 2011. ACM.
16. James Bennett and Stan Lanning. The netflix prize. In Proceedings of KDD cup and workshop, volume 2007, page 35, 2007.
17. Jun Wang, Arjen P De Vries, and Marcel JT Reinders. Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pages 501-508. ACM, 2006.
18. Keith Cheverst, Nigel Davies, Keith Mitchell, Adrian Friday, and Christos Efstratiou. Developing a context-aware electronic tourist guide: some issues and experiences. In Proceedings of the SIGCHI conference on Human factors in computing systems, pages 17-24. ACM, 2000.
19. Liangjie Hong, Aziz S Doumith, and Brian D Davison. Co-factorization machines: modeling user interests and predicting individual decisions in twitter. In Proceedings of the sixth ACM international conference on Web search and data mining, pages 557-566. ACM, 2013.
20. Nathan Srebro, Tommi Jaakkola, et al. Weighted low-rank approximations. In ICML, volume 3, pages 720-727, 2003.
21. Steffen Rendle and Lars Schmidt-Thieme. Pairwise interaction tensor factorization for personalized tag recommendation. In Proceedings of the third ACM international conference on Web search and data mining, pages 81-90. ACM, 2010.
22. Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty- Fifth Conference on Uncertainty in Artificial Intelligence, pages 452-461. AUAI Press, 2009.
23. Steffen Rendle, Zeno Gantner, Christoph Freudenthaler, and Lars Schmidt-Thieme. Fast context-aware recommendations with factorization machines. In Proceedings of the 34th ACM SIGIR Conference on Reasearch and Development in Information Retrieval. ACM, 2011.
24. Steffen Rendle. Factorization machines with libfm. ACM Transactions on Intelligent Systems and Technology (TIST), 3(3):57, 2012.
25. Steffen Rendle. Factorization machines. In Proceedings of the 10th IEEE International Conference on Data Mining. IEEE Computer Society, 2010.
26. Tamara G Kolda and Brett W Bader. Tensor decompositions and applications. SIAM review, 51(3):455-500, 2009.
27. Tianqi Chen, Weinan Zhang, Qiuxia Lu, Kailong Chen, Zhao Zheng, and Yong Yu. Svdfeature: a toolkit for feature-based collaborative filtering. The Journal of Machine Learning Research, 13(1):3619-3622, 2012.
28. Tianqi Chen, Zhao Zheng, Qiuxia Lu, Weinan Zhang, and Yong Yu. Feature-based matrix factorization. arXiv preprint arXiv:1109.2271, 2011.
29. Yehuda Koren, Robert Bell, and Chris Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8):30-37, 2009.
30. Yehuda Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the tfth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 426-434. ACM, 2008.
31. Yifan Hu, Yehuda Koren, and Chris Volinsky. Collaborative filtering for implicit feedback datasets. In Data Mining, 2008. ICDM’08. Eighth IEEE International Conference on, pages 263-272. IEEE, 2008.
32. Yunhong Zhou, Dennis Wilkinson, Robert Schreiber, and Rong Pan. Large-scale parallel collaborative filtering for the netflix prize. In Algorithmic Aspects in Information and Management, pages 337-348. Springer, 2008.
33. Zeno Gantner, Steffen Rendle, and Lars Schmidt-Thieme. Factorization models for context-/time-aware movie recommendations. In Proceedings of the Workshop on Context-Aware Movie Recommendation, pages 14-19. ACM, 2010.
34. К. В. Воронцов. Математические методы обучения по прецедентам (теория обучения машин). Москва, 2011.
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| Тема: | Рекомендательные системы |
| Артикул: | 1506053 |
| Дата написания: | 30.12.2016 |
| Тип работы: | Контрольная работа |
| Предмет: | Прикладная информатика |
| Оригинальность: | Антиплагиат.ВУЗ — 86% |
| Количество страниц: | 27 |
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