svd++

最新推荐文章于 2022-07-17 22:06:56 发布

fengyueqq

最新推荐文章于 2022-07-17 22:06:56 发布

阅读量4.1k

点赞数

分类专栏：个性化推荐文章标签：机器学习 python matrix svd++ 推荐系统

本文链接：https://blog.youkuaiyun.com/wanglulu2014/article/details/21170111

版权

个性化推荐专栏收录该内容

2 篇文章

订阅专栏

svd++

分类：推荐系统机器学习 2013-05-31 20:50 836人阅读评论(0) 收藏举报

目录(?)[-]
CONTENTS
MODEL FORMALIZATION
GENERAL FORMALIZATION FOR USER FEEDBACK INFORMATION
LEARNING
LITERATURE
IMPLEMENTATIONS

SVD++ refers to a matrix factorization model which makes use of implicit feedback information. In general, implicit feedback can refer to any kinds of users' history information that can help indicate users' preference.

[hide]

MODEL FORMALIZATION

The SVD++ model is formally described as following equation:

$r_{ui} = \mu + b_u + b_i + \left(p_u + \frac{1}{\sqrt{|N(u)|}}\sum_{j\in N(u)} y_j \right)^T q_i,$

where $N(u)$ is the set of implicit information( the set of items user u rated ).

GENERAL FORMALIZATION FOR USER FEEDBACK INFORMATION

A more general form of utilizing implicit/explicit information as user factor can be described in following equation

$r_{ui} = \mu + b_u + b_i + \left(p_u + \sum_{i\in Ufeed(u)} \alpha_i y_i \right)^T q_i.$

Here $Ufeed(u)$ is the set of user feedback information( e.g: the web pages the user clicked, the music on users' favorite list, the movies user watched, any kinds of information that can be used to describe the user). $\alpha_i$ is a feature weightassociates with the user feedback information. With the most two common choices: (1) $\frac{1}{\sqrt{|N(u)|}}$ for implicit feedback, (2) $\frac{r_{uj} - b_u}{\sqrt{|R(u)|}}$ for explicit feedback.

（一直搞不清楚上面公式当中yi到底是什么，现在清楚了，yi就是上面所写出来的隐式反馈！）

LEARNING

SVD++ can be trained using ALS.

It is slow to train a SVD++-style model using stochastic gradient descent due to the size of user feedback information, however, an efficient SGD training algorithm can be used. [1] describes efficient training with user feedback information in section 4

LITERATURE

Yehuda Koren: Factorization meets the neighborhood: a multifaceted collaborative filtering model, KDD 2008, http://portal.acm.org/citation.cfm?id=1401890.1401944

IMPLEMENTATIONS

The GraphLab Collaborative Filtering Library has implemented SVD++ for multicore: http://graphlab.org/pmf.html
SVDFeature is a toolkit designed for feature-based matrix factorization, can be used to implement SVD++ and its extensions.
LibFM can also be used to implement SVD++
wooflix is a (not very fast) Python implementation of SVD++
MyMediaLite: SVD++ source code on GitHub; see also [2]