| Authors: |
G. Linden
and B. Smith
and J. York
|
| URL: |
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1167344 |
| Description: |
Dissertation |
| Tags: |
master
matchmaking
recommender
|
| Abstract: |
Recommendation algorithms are best known for their use on e-commerce
Web sites, where they use input about a customer's interests to generate
a list of recommended items. Many applications use only the items
that customers purchase and explicitly rate to represent their interests,
but they can also use other attributes, including items viewed, demographic
data, subject interests, and favorite artists. At Amazon.com, we
use recommendation algorithms to personalize the online store for
each customer. The store radically changes based on customer interests,
showing programming titles to a software engineer and baby toys to
a new mother. There are three common approaches to solving the recommendation
problem: traditional collaborative filtering, cluster models, and
search-based methods. Here, we compare these methods with our algorithm,
which we call item-to-item collaborative filtering. Unlike traditional
collaborative filtering, our algorithm's online computation scales
independently of the number of customers and number of items in the
product catalog. Our algorithm produces recommendations in real-time,
scales to massive data sets, and generates high quality recommendations. |
@article{linden2003,
title = {Amazon.com recommendations: item-to-item collaborative filtering},
author = {G. Linden and B. Smith and J. York},
journal = {Internet Computing, IEEE},
number = {1},
pages = {76--80},
url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1167344},
volume = {7},
year = {2003},
description = {Dissertation},
abstract = {Recommendation algorithms are best known for their use on e-commerce
Web sites, where they use input about a customer's interests to generate
a list of recommended items. Many applications use only the items
that customers purchase and explicitly rate to represent their interests,
but they can also use other attributes, including items viewed, demographic
data, subject interests, and favorite artists. At Amazon.com, we
use recommendation algorithms to personalize the online store for
each customer. The store radically changes based on customer interests,
showing programming titles to a software engineer and baby toys to
a new mother. There are three common approaches to solving the recommendation
problem: traditional collaborative filtering, cluster models, and
search-based methods. Here, we compare these methods with our algorithm,
which we call item-to-item collaborative filtering. Unlike traditional
collaborative filtering, our algorithm's online computation scales
independently of the number of customers and number of items in the
product catalog. Our algorithm produces recommendations in real-time,
scales to massive data sets, and generates high quality recommendations.},
citeulike-article-id = {615956}, priority = {3},
keywords = {master matchmaking recommender }
}
::