Bayesian Mixed-Effects Models for Recommender Systems (1999)
Cached
Download Links
| Venue: | In ACM SIGIR ’99 Workshop on Recommender Systems: Algorithms and Evaluation |
| Citations: | 34 - 3 self |
BibTeX
@INPROCEEDINGS{Condliff99bayesianmixed-effects,
author = {Michelle Keim Condliff and David D. Lewis and David Madigan},
title = {Bayesian Mixed-Effects Models for Recommender Systems},
booktitle = {In ACM SIGIR ’99 Workshop on Recommender Systems: Algorithms and Evaluation},
year = {1999}
}
Years of Citing Articles
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
Abstract
We propose a Bayesian methodology for recommender systems that incorporates user ratings, user features, and item features in a single unified framework. In principle our approach should address the cold-start issue and can address both scalability issues as well as sparse ratings. However, our early experiments have shown mixed results. 1 Introduction Recommender systems have emerged as an important application area and have been the focus of considerable recent academic and commercial interest. The 1997 special issue of the Communications of the ACM [14] contains some key papers. Other important contributions include [2], [4], [8], [13], [16], [9], [1], [12], and [15]. In addition, many online retailers are using this technology to recommend new items to their customers, based on what they have bought in the past. Currently, most recommender systems are either content-based or collaborative, depending on the type of information that the system uses to recommend items to a user. Co...
