Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 10,310

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

Adomavicius, G., Tuzhilin, A.·2005·IEEE Transactions on Knowledge and Data Engineering
DOI 10.1109/tkde.2005.99
Open this paper in CoChat — free account

Abstract

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/tkde.2005.99

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 111

  1. 10.1023/A:1009839827683 · DOI
  2. 10.1007/3-540-45598-1_17 · DOI
  3. 10.1145/1055709.1055714 · DOI · Paper page
  4. 10.1145/312129.312230 · DOI · Paper page
  5. 10.1509/jmkr.37.3.363.18779 · DOI · Paper page
  6. Armstrong, 2001, Principles of Forecasting—A Handbook for Researchers and Practitioners, 10.1007/978-0-306-47630-3 · DOI · Paper page
  7. Baeza-Yates, 1999, Modern Information Retrieval
  8. 10.1145/245108.245124 · DOI · Paper page
  9. Basu, Recommendation as Classification: Using Social and Content-Based Information in Recommendation
  10. 10.1145/138859.138861 · DOI · Paper page
  11. Billsus, Learning Collaborative Information Filters
  12. 10.1145/301136.301208 · DOI
  13. 10.1023/A:1026501525781 · DOI · Paper page
  14. 10.1145/506218.506240 · DOI · Paper page
  15. Breese, Empirical Analysis of Predictive Algorithms for Collaborative Filtering
  16. 10.1017/CBO9780511569616.003 · DOI
  17. Burke, 2000, Knowledge-Based Recommender Systems, 69
  18. 10.1080/088395197118109 · DOI
  19. 10.1145/248603.248616 · DOI
  20. Chien, A Bayesian Model for Collaborative Filtering
  21. Claypool, Combining Content-Based and Collaborative Filters in an Online Newspaper
  22. 10.1613/jair.587 · DOI · Paper page
  23. 10.1007/BF00993277 · DOI · Paper page
  24. 10.1613/jair.295 · DOI · Paper page
  25. Condliff, Bayesian Mixed-Effects Models for Recommender Systems
  26. 10.1145/347090.347094 · DOI
  27. Delgado, Memory-Based Weighted-Majority Prediction for Recommender Systems
  28. 10.1287/mnsc.49.10.1407.17308 · DOI · Paper page
  29. 10.1145/963770.963776 · DOI
  30. Duchon, 1979, Splines Minimizing Rotation-Invariate Semi-Norms in Sobolev Spaces, 85
  31. Duda, 2001, Pattern Classification
  32. Dwyer, 1989, Customer Lifetime Valuation to Support Marketing Decision Making, J. Direct Marketing, 3, 10.1002/(SICI)1522-7138(199723)11:4<6::AID-DIR3>3.0.CO;2-T · DOI
  33. Ehrgott, 2000, Multicriteria Optimization, 10.1007/978-3-662-22199-0 · DOI
  34. Fawcett, Combining Data Mining and Machine Learning for Efficient User Profiling
  35. Freund, An Efficient Boosting Algorithm for Combining Preferences
  36. 10.1023/A:1007330508534 · DOI · Paper page
  37. Getoor, Using Probabilistic Relational Models for Collaborative Filtering
  38. 10.1145/138859.138867 · DOI
  39. 10.1023/A:1011419012209 · DOI · Paper page
  40. Good, Combining Collaborative Filtering with Personal Agents for Better Recommendations, 439
  41. 10.1145/312624.312682 · DOI
  42. 10.1145/358916.358995 · DOI · Paper page
  43. 10.1109/4236.968830 · DOI
  44. 10.1145/963770.963772 · DOI · Paper page
  45. 10.1145/223904.223929 · DOI · Paper page
  46. Hofmann, Probabilistic Latent Semantic Analysis, 289
  47. 10.1145/860435.860483 · DOI · Paper page
  48. 10.1145/963770.963774 · DOI · Paper page
  49. 10.1145/963770.963775 · DOI
  50. 10.1145/344250.344253 · DOI
  51. Jin, Preference-Based Graphic Models for Collaborative Filtering
  52. 10.1145/956863.956922 · DOI
  53. 10.1145/245108.245126 · DOI · Paper page
  54. Konstan, Recommender Systems: A GroupLens Perspective
  55. 10.1006/jcss.2001.1757 · DOI
  56. 10.1016/B978-1-55860-377-6.50048-7 · DOI · Paper page
  57. Lee, Collaborative Learning for Recommender Systems
  58. 10.1016/B978-1-55860-335-6.50026-X · DOI · Paper page
  59. 10.1007/978-3-540-30077-9_31 · DOI
  60. Lilien, 1992, Marketing Models
  61. 10.1109/MIC.2003.1167344 · DOI · Paper page
  62. 10.1006/inco.1994.1009 · DOI
  63. Mannila, Discovering Frequent Episodes in Sequences
  64. Marlin, Modeling User Rating Profiles for Collaborative Filtering
  65. Melville, Content-Boosted Collaborative Filtering for Improved Recommendations
  66. 10.1145/963770.963773 · DOI
  67. 10.1145/604045.604094 · DOI
  68. 10.1023/A:1013232803866 · DOI
  69. 10.1145/336597.336662 · DOI
  70. Mooney, Book Recommending Using Text Categorization with Extracted Information
  71. 10.1287/mnsc.49.10.1344.17313 · DOI · Paper page
  72. Nakamura, Collaborative Filtering Using Weighted Majority Prediction Algorithms
  73. 10.1007/978-3-642-61342-5 · DOI · Paper page
  74. Oard, Implicit Feedback for Recommender Systems
  75. Pavlov, A Maximum Entropy Approach to Collaborative Filtering in Dynamic, Sparse, High-Dimensional Domains
  76. 10.1023/A:1006544522159 · DOI · Paper page
  77. 10.1023/A:1007369909943 · DOI · Paper page
  78. Peddy, 2003, Building Solutions with Microsoft Commerce Server 2002
  79. Pennock, Collaborative Filtering by Personality Diagnosis: A Hybrid Memory And Model-Based Approach
  80. Popescul, Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments
  81. 10.1017/CBO9781139171502 · DOI · Paper page
  82. 10.1109/4236.968832 · DOI
  83. 10.1145/502716.502737 · DOI
  84. 10.1108/EUM0000000007118 · DOI
  85. Rocchio, 1971, Relevance Feedback in Information Retrieval
  86. 10.1145/192844.192905 · DOI · Paper page
  87. 10.1207/s15516709cog0304_3 · DOI · Paper page
  88. 10.1145/775047.775097 · DOI
  89. Salton, 1989, Automatic Text Processing
  90. 10.21236/ada439541 · DOI · Paper page
  91. 10.1145/371920.372071 · DOI · Paper page
  92. 10.1017/CBO9780511569616.002 · DOI
  93. 10.1023/A:1009804230409 · DOI · Paper page
  94. 10.1145/564376.564421 · DOI
  95. Shani, An MDP-Based Recommender System
  96. 10.1145/223904.223931 · DOI
  97. 10.1109/CAIA.1993.366590 · DOI
  98. Si, Flexible Mixture Model for Collaborative Filtering
  99. Soboroff, Combining Content and Collaboration in Text Filtering
  100. 10.1007/3-540-44816-0_32 · DOI
  101. 10.1007/978-1-4615-2089-4 · DOI
  102. 10.1145/245108.245122 · DOI · Paper page
  103. Tran, 2000, Hybrid Recommender Systems for Electronic Commerce
  104. Ungar, Clustering Methods for Collaborative Filtering
  105. Wade, 2003, A Grocery Cart that Holds Bread, Butter, and Preferences, New York Times
  106. Yang, On Evaluating Online Personalization, 35
  107. 10.1137/1.9781611972726.4 · DOI
  108. 10.1109/TKDE.2004.1264822 · DOI
  109. 10.1007/b11784 · DOI
  110. 10.1145/383952.384012 · DOI
  111. 10.1145/564376.564393 · DOI · Paper page

Cited by

See all 10,310

Metadata

Authors
Adomavicius, G., & Tuzhilin, A.
Venue
IEEE Transactions on Knowledge and Data Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2005-06
Volume
17
Issue
6
Pages
734-749
ISSN
1041-4347 (ISSN-L)
DOI
10.1109/tkde.2005.99
OpenAlex
W2171960770
Cited by
10,310
References
1
FWCI
489.36
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.