(Enter summary)
Abstract: Predicting items a user would like on the basis of
other users' ratings for these items has become a
well-established strategy adopted by many recommendation
services on the Internet. Although
this can be seen as a classification problem, algorithms
proposed thus far do not draw on results
from the machine learning literature. We propose
a representation for collaborative filtering tasks
that allows the application of virtually any machine
learning algorithm. We identify the... (Update)
Cited by: More
Semantic Peer-to-Peer Recommender Systems - Vladimir Ernesto Daz-Aviles (2005)
(Correct)
A Comparison of Statistical and Machine Learning.. - Goldenberg, Kubica, al. (2003)
(Correct)
A Personalized Collaborative Digital Library Environment: a.. - Renda, Straccia (2002)
(Correct)
Similar documents (at the sentence level):
5.3%: Personalized Agents Based On Case-Based Reasoning And Trust In.. - Montaner (2001)
(Correct)
Active bibliography (related documents): More All
0.1: REFEREE: An open framework for practical testing of.. - Cosley, Lawrence.. (2002)
(Correct)
0.1: Collaborative Recommendation via Adaptive Association Rule.. - Lin, Alvarez, Ruiz (2000)
(Correct)
0.1: An Efficient Boosting Algorithm for Combining Preferences - Freund, Iyer, Schapire.. (1998)
(Correct)
Similar documents based on text: More All
0.1: Learning and Revising User Profiles: The Identification of.. - Pazzani, Billsus (1997)
(Correct)
0.1: Evaluating Adaptive Web Site Agents - Pazzani, Billsus
(Correct)
0.1: Adaptive Web Site Agents - Pazzani, Billsus (1999)
(Correct)
Related documents from co-citation: More All
40: GroupLens: An Open Architecture for Collaborative Filtering of Netnews
- Resnick - 1994
40: Empirical analysis of predictive algorithms for collaborative filtering
- Breese, Heckerman et al. - 1998
28: GroupLens: Applying Collaborative Filtering to Usenet News
- Konstan, Miller et al. - 1997
BibTeX entry: (Update)
D. Billsus and M. J. Pazzani. Learning collaborative information filters. In Proceedings of the Fifteenth International Conference on Machine Learning, pages 46-- 54, Madison, WI, 1998. Morgan Kaufman. http://citeseer.ist.psu.edu/billsus98learning.html More
@inproceedings{ billsus98learning,
author = "Daniel Billsus and Michael J. Pazzani",
title = "Learning collaborative information filters",
booktitle = "Proc. 15th International Conf. on Machine Learning",
publisher = "Morgan Kaufmann, San Francisco, CA",
pages = "46--54",
year = "1998",
url = "citeseer.ist.psu.edu/billsus98learning.html" }
Citations (may not include all citations):
1359
Induction of decision trees (context) - Quinlan - 1986
625
Parallel Distributed Processing: Explorations in the Microst.. (context) - Rumelhart, McClelland - 1986
568
Indexing by latent semantic analysis
- Deerwester, Dumais et al. - 1990
490
Pattern Recognition and Neural Networks
- Ripley - 1996
254
GroupLens: An Open Architecture for Collaborative Filtering ..
- Resnick, Neophytos et al. - 1994
192
Using linear algebra for intelligent information retrieval
- Berry, Dumais et al. - 1995
135
A sequential algorithm for training text classifiers
- Lewis, Gale - 1994
91
Learning and Revising User Profiles: The identification of i..
- Pazzani, Billsus - 1997
88
Adaptive Control Processes: A Guided Tour (context) - Bellman - 1961
40
Large scale singular value computations (context) - Berry - 1992
10
EachMovie collaborative filtering data set (context) - McJones - 1997
The graph only includes citing articles where the year of publication is known.
Documents on the same site (http://www-csli.stanford.edu/cll/schedule.html): More
Syskill Webert: Identifying interesting web sites - Michael Pazzani (1996)
(Correct)
Eager: Programming Repetitive Tasks By Example - Allen Cypher Advanced (1991)
(Correct)
Predicting UNIX Command Lines: Adjusting to User Patterns - Korvemaker, Greiner (2000)
(Correct)
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC