thierrysilbermann.wordpress.com
February 2015 – Recommended
https://thierrysilbermann.wordpress.com/2015/02
Simple libFM example, part1. February 11, 2015. February 11, 2015. I often get email of people asking me how to run libFM. And having trouble to understand the whole pipeline. If you are verse in Machine Learning, a first step is to take a look at Steffen Rendle’s paper ‘ Factorization Machines. 8216; and this one too: Factorization Machines with libFM. But first, here is a toy example of how each file should be. (Was posted in the libfm google group). I one-hot encoded the users:. Same thing for items,.
diaorui.net
Dora Blog | Author Archives
http://diaorui.net/archives/author/doraemonok
六月 26th, 2014 · diaorui. 请将英文简历PDF发至这个邮箱 diaorui1987 at gmail.com. 三月 2nd, 2014 · diaorui. 最近一直在写毕业论文, 也想不起来更新博客. 发现很久没写了, 上来补一篇. 过年的时候和好友聚餐, 去吃回转寿司(配图从网上随便找的, 非去过的店铺, 仅供参考). 回转寿司里有一个很长的转盘, 厨师把寿司放到传送带上, 然后传送带不断旋转, 这样顾客能够随意挑选自己想要的寿司. 我的好朋友吃饱了以后就开始犯强迫症了. 他看转盘上的寿司摆放不均匀, 每次看到一个寿司到左边的寿司和到右边的寿司距离不相等, 他就把那个寿司挪一下位置, 让它刚好在左右两个寿司的正中间. 挪着挪着他就问我: 你说我这么一直挪着, 如果没其他人放寿司或者拿走寿司, 最终这些寿司能摆放均匀么? 我说我感觉上可以, 他也说感觉上可以. 我们怎么证明呢? 当时给了他一个解答, 后来又想到一个更有意思的解答. 仅仅用到一些数值代数知识. 05 & 0.5 &0. 05& 0.5 &0. 0 & 0 & 1. 1 & 0 & 0. 所以, 除非$d$和所有的...
diaorui.net
Dora Blog | Archive | 数学规划
http://diaorui.net/archives/category/数学规划
八月 31st, 2013 · diaorui. Techniques for Optimization Methods in Applications. 七月 14th, 2013 · diaorui. Tags: 0-1 Knapsack Problem. Interval Subset Sum Problem. 0-1 Knapsack Problem 0-1背包问题,下面简称KP 和Subset Sum Problem 子集合加总问题,下面简称SSP 是经典的NP完全问题。 KP 有$n$个物品要放入背包,第$i$个物品的价值为$c i$,占据体积为$v i$,背包的总容积为$V$,要选择一部分物品放入背包,使得他们的总价值最大。 Max {x i} sum c i * x i $. Mbox{s.t.} sum v i * x i le V, x i in { 0, 1 } $. SSP: 给一个集合$ {c 1, c 2, ldots, c n }$,还有一个目标值$V$,问能否选出一个子集,使得子集中元素求和刚好等于$V$。 Max {x i} sum c i * x i $. 这样我们的...
thierrysilbermann.wordpress.com
Simple libFM example, part1 – Recommended
https://thierrysilbermann.wordpress.com/2015/02/11/simple-libfm-example-part1
Simple libFM example, part1. February 11, 2015. February 11, 2015. I often get email of people asking me how to run libFM. And having trouble to understand the whole pipeline. If you are verse in Machine Learning, a first step is to take a look at Steffen Rendle’s paper ‘ Factorization Machines. 8216; and this one too: Factorization Machines with libFM. But first, here is a toy example of how each file should be. (Was posted in the libfm google group). I one-hot encoded the users:. Same thing for items,.
thierrysilbermann.wordpress.com
ThierryS – Recommended
https://thierrysilbermann.wordpress.com/author/gadjo95
Deal with relational data using libFM with blocks. September 17, 2015. September 17, 2015. An answer for this question: [Example] Files for Block Structure. There is a quick explanation in the README doc here: libFM1.42 Manual. Quick explanation is case you don’t want to read this whole blog post. I’ll take back the toy dataset I used in this previous blog post. Look at it to get the features meaning. 5 0:1 2:1 6:1 9:12.5. 5 0:1 3:1 6:1 9:20. 4 0:1 4:1 6:1 9:78. 1 1:1 2:1 8:1 9:12.5. 1 1:1 3:1 8:1 9:20.
semocean.com
推荐系统经典论文文献及业界应用 | Dustinsea
http://semocean.com/推荐系统经典论文文献及业界应用
列了一些之前设计开发百度关键词搜索推荐引擎时, 参考过的论文, 书籍, 以及调研过的推荐系统相关的工具 同时给出参加过及未参加过的业界推荐引擎应用交流资料 有我网盘的链接 , 材料组织方式参考了厂里部分同学的整理。 Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. J] Knowledge and Data Engineering, IEEE Transactions on, 2005, 17(6): 734-749. 2005年的state-of-the-art的推荐综述,按照content-based, CF, Hybrid的分类方法进行组织,并介绍了推荐引擎设计时需要关注的特性指标,内容非常全。 Marlin B. Collaborative filtering: A machine learning perspective. Burke R. Hybrid recomm...
bickson.blogspot.com
Large Scale Machine Learning and Other Animals: Collaborative filtering with GraphChi
http://bickson.blogspot.com/2012/12/collaborative-filtering-with-graphchi.html
Large Scale Machine Learning and Other Animals. Monday, December 3, 2012. Collaborative filtering with GraphChi. A couple of weeks ago I covered GraphChi. Here is a quick tutorial for trying out GraphChi. New: Join our GraphLab& GraphChi LinkedIn group. Here are papers which explain the algorithms in more detail:. Alternating Least Squares (ALS). Alternating Least Squares (ALS) - parallel coordinate descent (a.k.a. CCD ). Stochastic gradient descent (SGD). Matrix Factorization Techniques for Recommender ...