$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

Abstract AI-Helper 아이콘AI-Helper

The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems. Recommender systems are personalized information filtering used to identify a set of items that will be of interest to a certain user. This paper reviews recommender systems ...

주제어

참고문헌 (111)

  1. Adomavicius, G., Sankaranarayanan, R., Sen, S., and Tuzhilin, A. (2005), Incorporatin g contextual information in recommender systems using a multidimensional approach, ACM Transactions on Information Systems (TOIS), 23(1), 103-145. 

  2. Adomavicius, G. and Tuzhilin, A. (2005), Toward the next generation of recommende r systems : A survey of the state-of-the-art and possible extensions, Knowledge and Data Engineering, IEEE Transactions on, 17(6), 734-749. 

  3. Adomavicius, G. and Kwon, Y. (2012), Improving aggregate recommendation diversity using ranking-based techniques, Knowledge and Data Engineering, IEEE Transactions on, 24(5), 896-911. 

  4. Ahsaee, M. G., Naghibzadeh, M., and Naeini, S. E. Y. (2014), Semantic similarity asse ssment of words using weighted WordNet, International Journal of Machine Learning and Cybernetics, 5(3), 479-490. 

  5. Al Mamunur Rashid, S. K. L., Karypis, G., and Riedl, J. (2006), Clust KNN : a highly scalable hybrid model-and memory-based CF algorithm, Proceeding of WebKDD. 

  6. Balabanovic, M. and Shoham, Y. (1997), Fab : content-based, collaborative recommendation, Communications of the ACM, 40(3), 66-72. 

  7. Basilico, J. and Hofmann, T. (2004), Unifying collaborative and content-based fil tering, In Proceedings of the twenty-first international conference on Machine learning, 9. 

  8. Baluja, S., Seth, R., Sivakumar, D., Jing, Y., Yagnik, J., Kumar, S., and Aly, M. (2008), Video suggestion and discovery for youtube : taking random walks through the view graph, In Proceedings of the 17th international conference on World Wide Web, 895-904. 

  9. Barragans-Martinez, A. B., Costa-Montenegro, E., Burguillo, J. C., Rey-Lopez, M., M ikic-Fonte, F. A., and Peleteiro, A. (2010), A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition, Information Sciences, 180(22), 4290-4311. 

  10. Berry, M. W., Dumais, S. T., and O'Brien, G. W. (1995), Using linear algebra for intelligent information retrieval, SIAM review, 37(4), 573-595. 

  11. Billsus, D. and Pazzani, M. J. (1998), Learning Collaborative Information Filters, In ICML, 98, 46-54. 

  12. Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003), Latent dirichlet allocation, the Journal of machine Learning research, 3, 993-1022. 

  13. Bobadilla, J., Ortega, F., Hernando, A., and Gutierrez, A. (2013), Recommender system s survey, Knowledge-Based Systems, 46, 109-132. 

  14. Breese, J. S., Heckerman, D., and Kadie, C. (1998), Empirical analysis of predictive algorithms for collaborative filtering, In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, 43-52. 

  15. Burke, R. (2002), Hybrid recommender systems : Survey and experiments, User modeling and user-adapted interaction, 12(4), 331-370. 

  16. Burke, R. (2007), Hybrid web recommender systems, In The adaptive web, 377-4 08. 

  17. Carrillo, D., Lopez, V. F., and Moreno, M. N. (2013), Multi-label Classification for Recommender Systems, In Trends in Practical Applications of Agents and Multi agent Systems, 181-188. 

  18. Celma, O. and Herrera, P. (2008), A new approach to evaluating novel recommendations, In Proceedings of the 2008 ACM conference on Recommender sys tems, 179-186. 

  19. Chow, A. and Manai, G. (2014), HybridRank : A Hybrid Content-Based Approach To Mobile Game Recommendations, CBRecSys. 

  20. Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., and Sartin, M. (1999), Combining content-based and collaborative filters in an online newspaper, In Proceedings of ACM SIGIR workshop on recommender systems , 60. 

  21. Cremonesi, P., Koren, Y., and Turrin, R. (2010), Performance of recommender algorithms on top-n recommendation tasks, In Proceedings of the fourth ACM conference on Recommender systems, 39-46. 

  22. Das, M., De Francisci Morales, G., Gionis, A., and Weber, I. (2013), Learning to question : Leveraging user preferences for shopping advice, In Proceedings o f the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, 203-211. 

  23. Dean, D., Felten, E. W., and Wallach, D. S. (1996), Java security : From HotJava to Netscape and beyond, In Security and Privacy, Proceedings, IEEE Symposium, 190-200. 

  24. Deerwester, S. C., Dumais, S. T., Landauer, T. K., Furnas, G. W., and Harshman, R. A. (1990), Indexing by latent semantic analysis, JASIS, 41(6), 391-407. 

  25. Deshpande, M. and Karypis, G. (2004), Item-based top-n recommendation algorithms, ACM Transactions on Information Systems (TOIS), 22(1), 143-177. 

  26. Domingos, P. and Pazzani, M. (1997), On the optimality of the simple Bayesian classifier under zero-one loss, Machine learning, 29(2/3), 103-130. 

  27. Ducheneaut, N., Partridge, K., Huang, Q., Price, B., Roberts, M., Chi, E. H., and Begole, B. (2009), Collaborative filtering is not enough? Experiments with a mixed- model recommender for leisure activities, In User Modeling, Adaptation, and Per sonalization, 295-306. 

  28. Fan, J. and Pan, W. (2014), An Improved Collaborative Filtering Algorithm Combining Content-based Algorithm and User Activity, Big Data and Smart Computing (BIGCOMP), 88-91. 

  29. Fleder, D. and Hosanagar, K. (2009), Blockbuster culture's next rise or fall : The impact of recommender systems on sales diversity, Management science, 55(5), 697-712. 

  30. Fortuna, B., Fortuna, C., and Mladenic, D. (2010), Real-time news recommender system, In Machine Learning and Knowledge Discovery In Databases, 583-586. 

  31. Frakes, W. B. and Baeza-Yates, R. (1992), Information retrieval : data structures and algorithms. 

  32. Ganu, G., Elhadad, N., and Marian, A. (2009), Beyond the Stars : Improving Rating Predictions using Review Text Content, In WebDB. 

  33. Ge, M., Delgado-Battenfeld, C., and Jannach, D. (2010), Beyond accuracy : evaluating recommender systems by coverage and serendipity, In Proceedings of the fourth ACM conference on Recommender systems, 257-260. 

  34. Ghazanfar, M. and Prugel-Bennett, A. (2010), An Improved Switching Hybrid Recomm ender System Using Naive Bayes Classifier and Collaborative Filtering. 

  35. Goldberg, D., Nichols, D., Oki, B. M., and Terry, D. (1992), Using collaborative filterin g to weave an information tapestry, Communications of the ACM, 35(12), 61-70. 

  36. Goldberg, K., Roeder, T., Gupta, D., and Perkins, C. (2001), Eigentaste : A constant time collaborative filtering algorithm, Information Retrieval, 4(2), 133-151. 

  37. Good, N., Schafer, J. B., Konstan, J. A., Borchers, A., Sarwar, B., Herlocker, J., and Riedl, J. (1999), Combining collaborative filtering with personal agents for better recommendations, In AAAI/IAAI, 439-446. 

  38. Gupta, P., Goel, A., Lin, J., Sharma, A., Wang, D., and Zadeh, R. (2013), Wtf : Th e who to follow service at twitter, In Proceedings of the 22nd international conference on World Wide Web, 505-514. 

  39. Han, J., Kamber, M., and Pei, J. (2006), Datamining : concepts and techniques. 

  40. Hahsler, M. (2011), Recommenderlab : A Framework for Developing and Testing Recommendation Algorithms. 

  41. Herlocker, J. L., Konstan, J. A., Borchers, A., and Riedl, J. (1999), An algorithmic framework for performing collaborative filtering, In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, 230-237. 

  42. Herlocker, J. L., Konstan, J. A., and Riedl, J. (2000), Explaining collaborativ e filtering recommendations, In Proceedings of the ACM conference on Computer supported cooperative work, 241-250. 

  43. Herlocker, J. L., Konstan, J. A., Terveen, L. G., and Riedl, J. T. (2004), Evaluating collaborative filtering recommender systems, ACM Transactions on Information Systems (TOIS), 22(1), 5-53. 

  44. Hofmann, T. (2004), Latent semantic models for collaborative filtering. ACM Transa ctions on Information Systems (TOIS), 22(1), 89-115. 

  45. Jeh, G. and Widom, J. (2002), SimRank : a measure of structural-context similarity, In Proceedings of the eighth ACM SIGKDD international conference on Knowl edge discovery and data mining, 538-543. 

  46. Jeong, J. and Rhee, P. K. (2003), A Collaborative Filtering using SVD on Low-Dimensional Space, Korea Information Processing Society, 10B(3), 273-280. 

  47. Jiang, J. J. and Conrath, D. W. (1997), Semantic similarity based on corpus statistics and lexical taxonomy, arXiv preprint cmp-lg/9709008. 

  48. Jin, B. W., Cho, Y. S., and Ryu, K. H. (2010), Personalized e-Commerce Recommendation System using RFM method and Association Rules, Korea society of computer information, 15(12), 227-235. 

  49. Karypis, G. (2001), Evaluation of item-based top-n recommendation algorithms, In Proceedings of the tenth international conference on Information and knowledge management, 247-254. 

  50. Kim, B. M., Li, Q., Kim, S. G., Lim, E. K., and Kim, J. Y. (2003), A New Approach Combining Content-based Filtering and Collaborative Filtering for Recommender Systems, Journal of Korean Institute of Intelligent Systems, 31(3), 332-342. 

  51. Kim, J. D. (2013), Development of Social Commerce and Recommendations, Korea Multimedia Society, 17(1), 28-36. 

  52. Kim, N. K. and Yong, S. Y. (2013), Bayesian network based Music Recommendation System considering Multi-Criteria Decision Making, The Journal of digital policy and managemen, 11(3), 345-352. 

  53. Kim, Y. (2010), A Study on Design and Implementation of Personalized Information Recommendation System based on Apriori Algorithm, Korea Biblia Society for Library and Information Science, 23(4), 283-308. 

  54. Ko, H. G., Kim, E., Ko, I. Y., and Chang, D. (2014), Semantically-based Recommendati on by using Semantic Clusters of Users' Viewing History, Big Data and Smart Computing (BIGCOMP), 83-87. 

  55. Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., and Riedl, J. (1997), GroupLens : applying collaborative filtering to Usenet news, Communications of the ACM, 40(3), 77-87. 

  56. Koren, Y. (2010), Collaborative filtering with temporal dynamics, Communications of the ACM, 53(4), 89-97. 

  57. Lang, K. (1995), Newsweeder : Learning to filter netnews, In Proceedings of the Twelfth International Conference on Machine Learning. 

  58. Lathia, N., Hailes, S., and Capra, L. (2009), Temporal collaborative filtering with adaptive neighbourhoods, In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, 796-797. 

  59. Lathia, N., Hailes, S., Capra, L., and Amatriain, X. (2010), Temporal diversity in recommender systems, In Proceedings of the 33rd international ACM SIGIR conf erence on Research and development in information retrieval, 210-217. 

  60. Lee, J., Bengio, S., Kim, S., Lebanon, G., and Singer, Y. (2014), Local collaborative ranking, In Proceedings of the 23rd international conference on World wide web, 85-96. 

  61. Lee, S. G., Lee, B. S., Bak, B. Y., and Hwang, H. K. (2010), A Study of Intelligent Recommendation System based on Naive Bayes Text Classification and Collaborative Filtering, Journal of Information Management, 41(4), 227-249. 

  62. Lee, S. I. and Lee, S. Y. (2010), A Collaborative Filtering-based Recommendation System with Relative Classification and Estimation Revision based on Time, Journal of Korean Institute of Intelligent Systems, 20(2), 189-194. 

  63. Lee, Y. S. and Lee, S. (2002), Cluster Feature Selection using Entropy Weighting and SVD, Journal of The Korean Institute of Information Scientists and Engineers, 29(4), 248-257. 

  64. Linden, G., Smith, B., and York, J. (2003), Amazon.comrecommendations : Item-to-it em collaborative filtering, Internet Computing, IEEE, 7(1), 76-80. 

  65. Ling, G., Lyu, M. R., and King, I. (2014), Ratings meet reviews, a combined approach to recommend, In Proceedings of the 8th ACM Conference on Recommender systems, 105-112. 

  66. Lops, P., De Gemmis, M., and Semeraro, G. (2011), Content-based recommender systems : State of the art and trends, In Recommender systems handbook, 73-105. 

  67. Lu, L., Medo, M., Yeung, C. H., Zhang, Y. C., Zhang, Z. K., and Zhou, T. (2012), Recommender systems, Physics Reports, 519(1), 1-49. 

  68. McAuley, J. and Leskovec, J. (2013), Hidden factors and hidden topics : unde rstanding rating dimensions with review text, In Proceedings of the 7th ACM co nference on Recommender systems, 165-172. 

  69. McNee, S. M., Riedl, J., and Konstan, J. A. (2006), Being accurate is not enough : how accuracy metrics have hurt recommender systems. In CHI extended a bstracts on Human factors in computing systems, 1097-1101. 

  70. McNee, S. M., Kapoor, N., and Konstan, J. A. (2006), Don't look stupid : avoiding pitfalls when recommending research papers, In Proceedings of the 20th anniversary conference on Computer supported cooperative work, 171-180. 

  71. Melville, P., Mooney, R. J., and Nagarajan, R. (2002), Content-boosted collaborative filtering for improved recommendations, In AAAI/IAAI, 187-192. 

  72. Miller, G. A. (1995), WordNet : a lexical database for English, Communications of the ACM, 38(11), 39-41. 

  73. Miyahara, K. and Pazzani, M. J. (2000), Collaborative filtering with the simple Bayesian classifier, In PRICAI 2000 Topics in Artificial Intelligence, 679-689. 

  74. Murakami, T., Mori, K., and Orihara, R. (2008), Metrics for evaluating the serendipity of recommendation lists, In New frontiers in artificial intelligence, 40-46. 

  75. Netflix Prize, http://netflixprize.com. 

  76. Nikovski, D. and Kulev, V. (2006), Induction of compact decision trees for personalized recommendation, In Proceedings of the ACM symposium on Applied computing, 575-581. 

  77. Noh, Y., Oh, Y., and Park, S. (2014), A Location-based Personalized News Recommendation, Big Data and Smart Computing (BIGCOMP), 99-104. 

  78. O'Connor, M. and Herlocker, J. (1999), Clustering items for collaborative filtering, In Proceedings of the ACM SIGIR workshop on recommender systems, 128. 

  79. Oh, J. Y. (2004), Design of Recommendation system Using Association Rule in e-Commerce, Myongji University. 

  80. Park, K. S. and Moon, N. M. (2012), Multidimensional Optimization Model of Music Recommender Systems, information processing society journal, 19B(3), 155-164. 

  81. Park, Y. J. and Tuzhilin, A. (2008), The long tail of recommender systems a nd how to leverage it, In Proceedings of the ACM conference on Recommender systems, 11-18. 

  82. Park, Y., Park, S., and Lee, S. (2014), Fast Collaborative Filtering with a k-Nearest Neighbor Graph, KDD'13. 

  83. Pazzani, M. J. and Billsus, D. (2007), Content-based recommendation systems, In The adaptive web, 325-341. 

  84. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J. (1994), GroupLens : an open architecture for collaborative filtering of netnews, In Proceedin gs of the ACM conference on Computer supported cooperative work, 175-186. 

  85. Salton, G. and McGill, M. J. (1983), Introduction to modern information retrieval. 

  86. Salton, G. and Buckley, C. (1988), Term-weighting approaches in automatic text retrieval, Information processing and management, 24(5), 513-523. 

  87. Sarwar, B. M., Konstan, J. A., Borchers, A., Herlocker, J., Miller, B., and Riedl, J. (1998), Using filtering agents to improve prediction quality in the grouplens research collaborative filtering system, In Proceedings of the 1998 ACM conference on Computer supported cooperative work, 345-354. 

  88. Sarwar, B., Karypis, G., Konstan, J., and Riedl, J. (2000), Application of dimension ality reduction in recommender system-a case study (No. TR-00-043), Minnesota Univ Minneapolis Dept of Computer Science. 

  89. Sarwar, B., Karypis, G., Konstan, J., and Riedl, J. (2000), Analysis of recomm endation algorithms for e-commerce, In Proceedings of the 2nd ACM conference on Electronic commerce, 158-167. 

  90. Sarwar, B., Karypis, G., Konstan, J., and Riedl, J. (2001), Item-based collaborative filtering recommendation algorithms, In Proceedings of the 10th international conference on World Wide Web, 285-295. 

  91. Sarwar, B. M., Karypis, G., Konstan, J., and Riedl, J. (2002), Recommender systems for large-scale e-commerce : Scalable neighborhood formation using clu stering, In Proceedings of the fifth international conference on computer and in formation technology, 1. 

  92. Sawant, S. (2013), Collaborative Filtering using Weighted BiPartite Graph Projection, A Recommendation System for Yelp. 

  93. Schafer, J. B., Konstan, J., and Riedl, J. (1999), Recommender systems in e-commerce, In Proceedings of the 1st ACM conference on Electronic commerce, 158-166. 

  94. Schafer, J. B., Konstan, J. A., and Riedl, J. (2001), E-commerce recommendation applications, In Applications of Data Mining to Electronic Commerce, 115-153. 

  95. Schein, A. I., Popescul, A., Ungar, L. H., and Pennock, D. M. (2002), Methods and metrics for cold-start recommendations, In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, 253-260. 

  96. Sheth, B. and Maes, P. (1993), Evolving agents for personalized information filtering, In Artificial Intelligence for Applications, 345-352. 

  97. Shin, C. H., Lee, J. W., Yang, H, N., and Choi, I. Y. (2012), The Research on Recom mender for New Customers Using Collaborative Filtering and Social Network An alysis, Korea Intelligent Information System Society, 18(4), 19-42. 

  98. Su, X. and Khoshgoftaar, T. M. (2006), Collaborative filtering for multi-class data using belief nets algorithms, In Tools with Artificial Intelligence, 497-504. 

  99. Su, X. and Khoshgoftaar, T. M. (2009), A survey of collaborative filtering techniques, Advances in artificial intelligence, 4. 

  100. Tata, S. and Patel, J. M. (2007), Estimating the selectivity of tf-idf based cosine simil arity predicates, ACM SIGMOD Record, 36(2), 7-12. 

  101. Tipping, M. E. and Bishop, C. M. (1999), Probabilistic principal component analysis, Journal of the Royal Statistical Society : Series B (Statistical Methodology), 61(3), 611-622. 

  102. Vargas, S. and Castells, P. (2011), Rank and relevance in novelty and diver sity metrics for recommender systems, In Proceedings of the fifth ACM conferen ce on Recommender systems, 109-116. 

  103. Vozalis, M. and Margaritis, K. G. (2004), Collaborative filtering enhanced by demographic correlation, In AIAI Symposium on Professional Practice in AI, of the 18th World Computer Congress. 

  104. Wartena, C., Slakhorst, W., Wibbels, M., Gantner, Z., Freudenthaler, C., Newell, C., and BBC R&D, L. (2011), Keyword-Based TV Program Recommendation, In Work shop chairs, 15. 

  105. Wu, Y. H. and Chen, A. L. (2000), Index structures of user profiles for efficien t web page filtering services, In 2012 IEEE 32nd International Conference on Distributed Computing Systems, 644-644. 

  106. Xiang, L., Yuan, Q., Zhao, S., Chen, L., Zhang, X., Yang, Q., and Sun, J. (2010), Temporal recommendation on graphs via long-and short-term preference fusion. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 723-732. 

  107. Yin, H., Sun, Y., Cui, B., Hu, Z., and Chen, L. (2013), LCARS : A Location-Content-Aware Recommender System, KDD. 

  108. Yu, K., Schwaighofer, A., Tresp, V., Xu, X., and Kriegel, H. P. (2004), Probabilistic memory-based collaborative filtering, Knowledge and Data Engineering, IEEE Tran sactions, 16(1), 56-69. 

  109. Zhang, M. and Hurley, N. (2008), Avoiding monotony: improving the diversit y of recommendation lists, In Proceedings of the ACM conference on Recommender systems, 123-130. 

  110. Ziegler, C. N., McNee, S. M., Konstan, J. A., and Lausen, G. (2005), Improving recommendation lists through topic diversification, In Proceedings of the 14th in ternational conference on World Wide Web, 22-32. 

  111. Zhou, T., Kuscsik, Z., Liu, J. G., Medo, M., Wakeling, J. R., and Zhang, Y. C. (2010), Solving the apparent diversity-accuracy dilemma of recommender systems, Proceedings of the National Academy of Sciences, 107(10), 4511-4515. 

저자의 다른 논문 :

LOADING...

관련 콘텐츠

오픈액세스(OA) 유형

FREE

Free Access. 출판사/학술단체 등이 허락한 무료 공개 사이트를 통해 자유로운 이용이 가능한 논문

섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트

맨위로