December 2008
Hancock: a language for extracting signatures from... →
(2000), pp. 9-17. Corinna Cortes, Kathleen Fisher, Daryl Pregibon, Anne Rogers
Collaborative Filtering by Personality Diagnosis:... →
(2000), pp. 473-480. D Pennock, E Horvitz, S Lawrence, CL Giles
Mining Frequent Patterns in Data Streams at... →
(2002) Although frequent-pattern mining has been widely studied and used, it is challenging to extend it to data streams. Compared to mining from a static transaction data set, the streaming case…
Mining Sequential Patterns: Generalizations and... →
Vol. 1057 (FebruaryMay—FebruarySeptember~ 1996), pp. 3-17. : The problem of mining sequential patterns was recently introduced in [AS95]. We are given a database of sequences, where each sequence…
Lessons on Applying Automated Recommender Systems... →
Vol. 21, No. 2. (2006) JA Konstan, SM Mcnee, C Ziegler, R Torres, N Kapoor, JT Riedl
A Similarity Measure for Collaborative Filtering... →
Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence (2007), pp. 385-397. Collaborative Filtering(CF) is a widely accepted method of creating…
Alambic : a privacy-preserving recommender system... →
International Journal of Information Security, Vol. 7, No. 5. (1 October 2008), pp. 307-334. Abstract Recommender systems enable merchants to assist customers in finding products that best satisfy…
A personal news agent that talks, learns and... →
(1999), pp. 268-275. Daniel Billsus, Michael Pazzani
Explaining collaborative filtering recommendations →
(2000), pp. 241-250. Jonathan Herlocker, Joseph Konstan, John Riedl
The Digitization of Word-of-Mouth: Promise and... →
Social Science Research Network Working Paper Series (March 2003) Online feedback mechanisms harness the bi-directional communication capabilities of the Internet in order to engineer large-scale…
Privacy Risks in Recommender Systems →
IEEE Internet Computing, Vol. 5, No. 6. (2001), pp. 54-62. Naren Ramakrishnan, Benjamin Keller, Batul Mirza, Ananth Grama, George Karypis
An algorithmic framework for performing... →
(1999), pp. 230-237. Jonathan Herlocker, Joseph Konstan, Al Borchers, John Riedl
Analysis of recommendation algorithms for... →
(2000), pp. 158-167. Badrul Sarwar, George Karypis, Joseph Konstan, John Riedl
Collaborative filtering via gaussian probabilistic... →
(2003), pp. 259-266. Collaborative filtering aims at learning predictive models of user preferences, interests or behavior from community data, i.e. a database of available user preferences. In…
An MDP-Based Recommender System →
J. Mach. Learn. Res., Vol. 6 (2005), pp. 1265-1295. Typical recommender systems adopt a static view of the recommendation process and treat it as a prediction problem. We argue that it is more…
A bayesian model for collaborative filtering →
(1999) YH Chien, EI George
A Maximum Entropy Approach for Collaborative... →
The Journal of VLSI Signal Processing, Vol. 37, No. 2. (2004), pp. 199-209. J Browning, DJ Miller
Modeling user rating profiles for collaborative... →
Advances in Neural Information Processing Systems, Vol. 16 (2004), pp. 627-634. B Marlin
Recommendation Systems: A Probabilistic Analysis →
Journal of Computer and System Sciences, Vol. 63, No. 1. (August 2001), pp. 42-61. A recommendation system tracks past actions of a group of users to make recommendations to individual members of…
Learning Collaborative Information Filters →
(1998), pp. 46-54. Daniel Billsus, Michael Pazzani
Using probabilistic relational models for... →
(1999) L Getoor, M Sahami
Clustering Methods For Collaborative Filtering →
(1998) L Ungar, D Foster
Item-based Top-N recommendation algorithms →
ACM Trans. Inf. Syst., Vol. 22, No. 1. (January 2004), pp. 143-177. Mukund Deshpande, George Karypis
Memory-Based Weighted-Majority Prediction →
(1999) J Delgado, N Ishii
Collaborative Filtering Using Weighted Majority... →
(1998), pp. 395-403. Atsuyoshi Nakamura, Naoki Abe
Collaborative Filtering and Inference Rules for... →
Interactive Technology and Smart Education, Vol. 2 D Lemire, H Boley, S Mcgrath, M Ball
PHOAKS: A System for Sharing Recommendations →
Communications of the ACM, Vol. 40, No. 3. (1997), pp. 59-62. blem through a collaborative filtering approach. PHOAKS works by automatically recognizing, tallying, and redistributing…
Eigentaste: A Constant Time Collaborative... →
Information Retrieval, Vol. 4, No. 2. (2001), pp. 133-151. K Goldberg, T Roeder, D Gupta, C Perkins
MovieLens Unplugged: Experiences with an... →
(2003) Recommender systems have changed the way people shop online. Recommender systems on wireless mobile devices may have the same impact on the way people shop in stores. We present our…
Customer lifetime value modeling and its use for... →
(2002), pp. 332-340. We present and discuss the important business problem of estimating the effect of retention efforts on the Lifetime Value of a customer in the Telecommunications industry. We…
Recommending and evaluating choices in a virtual... →
(1995), pp. 194-201. Will Hill, Larry Stead, Mark Rosenstein, George Furnas
Probabilistic Models for Unified Collaborative and... →
(2001), pp. 437-444. Alexandrin Popescul, Lyle Ungar, David Pennock, Steve Lawrence
Combining collaborative filtering with personal... →
(1999), pp. 439-446. N Good, JB Schafer, JA Konstan, B Sarwar, J Herlocker, J Riedl
Recommendation as classification: using social and... →
(1998), pp. 714-720. Chumki Basu, Haym Hirsh, William Cohen
Data Mining →
(16 June 1996) Deals with discovering hidden data and unexpected patterns and rules in large databases. Offers a comprehensive introduction to data mining. Paper. Pieter Adriaans, Dolf Zantinge
User Modeling for Adaptive News Access →
User Modeling and User-Adapted Interaction, Vol. 10, No. 2-3. (2000), pp. 147-180. D Billsus, MJ Pazzani
Encyclopedia of library and information science →
(1968) A Kent, H Lancour, WZ Nasri
A personalized recommender system based on web... →
Expert Systems with Applications, Vol. 23, No. 3. (1 October 2002), pp. 329-342. A personalized product recommendation is an enabling mechanism to overcome information overload occurred when…
A Framework for Collaborative, Content-Based and... →
Artificial Intelligence Review, Vol. 13, No. 5. (1999), pp. 393-408. MJ Pazzani
Social Information Filtering: Algorithms for... →
Vol. 1 (1995), pp. 210-217. This paper describes a technique for making personalized recommendations from any type of database to a user based on similarities between the interest profile of that…
Novelty and redundancy detection in adaptive... →
(2002), pp. 81-88. Yi Zhang, Jamie Callan, Thomas Minka
Adaptive Lightweight Text Filtering →
(2001), pp. 319-329. Gabriel Somlo, Adele Howe
Methods and metrics for cold-start recommendations →
(2002), pp. 253-260. We have developed a method for recommending items that combines content and collaborative data under a single probabilistic framework. We benchmark our algorithm against a nave…
Combining content and collaboration in text... →
Vol. 99, pp. 86-91. IM Soboro, CK Nicholas
Combining content-based and collaborative filters... →
(1999) M Claypool, A Gokhale, T Miranda, P Murnikov, D Netes, M Sartin
Recommender Systems: A GroupLens Perspective →
Vol. 8, pp. 60-64. JA Konstan, J Riedl, A Borchers, JL Herlocker
Human-Computer Collaboration in Recommender... →
HCI in the New Millennium, Addison Wesley (2001), pp. 223-242. L Terveen, W Hill
Information filtering and information retrieval:... →
Commun. ACM, Vol. 35, No. 12. (December 1992), pp. 29-38. Nicholas Belkin, Bruce Croft
WebWatcher: A Tour Guide for the World Wide Web →
(1997) T Joachims, D Freitag, T Mitchell
Maximum likelihood estimation for filtering... →
(2001), pp. 294-302. Information filtering systems based on statistical retrieval models usually compute a numeric score indicating how well each document matches each profile. Documents with…