Latent semantic models for collaborative filtering
|
Tools and Resources
Share: |
||||||||||||||||||
ABSTRACT
AUTHORS
|
|
|||||||||||||||||||||||||||||||||||||||
| View colleagues of Thomas Hofmann | ||||||||||||||||||||||||||||||||||||||||
REFERENCESNote: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
|
1
|
||
|
2
|
Blei, D. M., Ng, A. Y., and Jordan, M. I. 2002. Latent dirichlet allocation. In Advances in Neural Information Processing Systems. MIT Press, Cambridge, Mass.
|
|
|
3
|
||
|
4
|
||
|
5
|
Chien, Y.-H. and George, E. 1999. A Bayesian model for collaborative filtering. In Online Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics.
|
|
|
6
|
Cover, T. M. and Thomas, J. A. 1991. Information Theory. Wiley, New York.
|
|
|
7
|
Deerwester, S. C., Dumais, S. T., Landauer, T. K., Furnas, G. W., and Harshman, R. A. 1990. Indexing by latent semantic analysis. J. ASIS 41, 6, 391--407.
|
|
|
8
|
Dempster, A., Laird, N., and Rubin, D. 1977. Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statist. Soc. B 39, 1--38.
|
|
|
9
|
EachMovie. www.research.digital.com/src/eachmovie/.
|
|
|
10
|
||
| |
11
|
|
|
12
|
||
|
13
|
||
| |
14
|
Jonathan L. Herlocker , Joseph A. Konstan , Al Borchers , John Riedl, An algorithmic framework for performing collaborative filtering, Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, p.230-237, August 15-19, 1999, Berkeley, California, USA [doi>10.1145/312624.312682]
|
| |
15
|
|
|
16
|
||
|
17
|
Hofmann, T. 2001b. What people (don't) want. In Proceedings of the European Conference on Machine Learning (ECML).
|
|
|
18
|
||
| |
19
|
|
|
20
|
||
|
21
|
||
|
22
|
||
| |
23
|
Paul Resnick , Neophytos Iacovou , Mitesh Suchak , Peter Bergstrom , John Riedl, GroupLens: an open architecture for collaborative filtering of netnews, Proceedings of the 1994 ACM conference on Computer supported cooperative work, p.175-186, October 22-26, 1994, Chapel Hill, North Carolina, USA [doi>10.1145/192844.192905]
|
|
24
|
Sarwar, B. M., Karypis, G., Konstan, J. A., and Riedl, J. 2000. Application of dimensionality reduction in recommender system---A case study. In Proceedings of the ACM WebKDD 2000 Web Mining for E-Commerce Workshop. ACM, New York.
|
|
| |
25
|
|
|
26
|
||
|
27
|
Ungar, L. and Foster, D. 1998. Clustering methods for collaborative filtering. In Proceedings of the Workshop on Recommendation Systems. AAAI Press, Menlo Park, Calif.
|
CITED BY342 Citations
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
INDEX TERMSThe ACM Computing Classification System (CCS rev.2012)
PUBLICATION| Title | ACM Transactions on Information Systems (TOIS) TOIS Homepage table of contents archive |
| Volume 22 Issue 1, January 2004 | |
| Pages | 89-115 |
| Publication Date | 2004-01-01 (yyyy-mm-dd) |
| Publisher | ACM New York, NY, USA |
| ISSN: 1046-8188 EISSN: 1558-2868 doi>10.1145/963770.963774 |
Digital Content: The ACM Collection on Digital Content
REVIEWS
COMMENTSBe the first to comment To Post a comment please sign in or create a free Web account
Table of ContentsVolume 22 Issue 1, January 2004
| Introduction to recommender systems: Algorithms and Evaluation | |
| Joseph A. Konstan | |
| Pages: 1-4 | |
| doi>10.1145/963770.963771 | |
Full text: PDF
|
|
| Evaluating collaborative filtering recommender systems | |
| Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, John T. Riedl | |
| Pages: 5-53 | |
| doi>10.1145/963770.963772 | |
Full text: PDF
|
|
|
Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being ...
expand
|
|
| Ontological user profiling in recommender systems | |
| Stuart E. Middleton, Nigel R. Shadbolt, David C. De Roure | |
| Pages: 54-88 | |
| doi>10.1145/963770.963773 | |
Full text: PDF
|
|
|
We explore a novel ontological approach to user profiling within recommender systems, working on the problem of recommending on-line academic research papers. Our two experimental systems, Quickstep and Foxtrot, create user profiles from unobtrusively ...
expand
|
|
| Latent semantic models for collaborative filtering | |
| Thomas Hofmann | |
| Pages: 89-115 | |
| doi>10.1145/963770.963774 | |
Full text: PDF
|
|
|
Collaborative filtering aims at learning predictive models of user preferences, interests or behavior from community data, that is, a database of available user preferences. In this article, we describe a new family of model-based algorithms designed ...
expand
|
|
| Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering | |
| Zan Huang, Hsinchun Chen, Daniel Zeng | |
| Pages: 116-142 | |
| doi>10.1145/963770.963775 | |
Full text: PDF
|
|
|
Recommender systems are being widely applied in many application settings to suggest products, services, and information items to potential consumers. Collaborative filtering, the most successful recommendation approach, makes recommendations based on ...
expand
|
|
| Item-based top-N recommendation algorithms | |
| Mukund Deshpande, George Karypis | |
| Pages: 143-177 | |
| doi>10.1145/963770.963776 | |
Full text: PDF
|
|
|
The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems---a personalized information filtering technology used to identify a set of items that will be of interest to a certain ...
expand
|