Open AccessProceedings Article
Using temporal data for making recommendations
Andrew Zimdars,David Maxwell Chickering,Christopher A. Meek +2 more
- pp 580-588
TLDR
This work describes two families of methods for transforming data to encode time order in ways amenable to off-the-shelf classification and density estimation tools and compares the results to collaborative filtering without ordering information.Abstract:
We treat collaborative filtering as a univariate time series problem: given a user's previous votes, predict the next vote. We describe two families of methods for transforming data to encode time order in ways amenable to off-the-shelf classification and density estimation tools. Using a decision-tree learning tool and two real-world data sets, we compare the results of these approaches to the results of collaborative filtering without ordering information. The improvements in both predictive accuracy and in recommendation quality that we realize advocate the use of predictive algorithms exploiting the temporal order of data.read more
Citations
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References
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Proceedings ArticleDOI
GroupLens: an open architecture for collaborative filtering of netnews
TL;DR: GroupLens is a system for collaborative filtering of netnews, to help people find articles they will like in the huge stream of available articles, and protect their privacy by entering ratings under pseudonyms, without reducing the effectiveness of the score prediction.
Proceedings Article
Empirical analysis of predictive algorithms for collaborative filtering
TL;DR: Several algorithms designed for collaborative filtering or recommender systems are described, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods, to compare the predictive accuracy of the various methods in a set of representative problem domains.
Journal ArticleDOI
GroupLens: applying collaborative filtering to Usenet news
Joseph A. Konstan,Bradley N. Miller,David A. Maltz,Jonathan L. Herlocker,Lee R. Gordon,John Riedl +5 more
TL;DR: The combination of high volume and personal taste made Usenet news a promising candidate for collaborative filtering and the potential predictive utility for Usenets news was very high.
Journal ArticleDOI
An empirical study of smoothing techniques for language modeling
TL;DR: This work surveys the most widely-used algorithms for smoothing models for language n -gram modeling, and presents an extensive empirical comparison of several of these smoothing techniques, including those described by Jelinek and Mercer (1980), and introduces methodologies for analyzing smoothing algorithm efficacy in detail.