A lot of research has been done on collaborative filtering (CF), and most popular approaches are based on low-dimensional factor models (model based matrix factorization. I will discuss these in detail). The CF techniques are broadly divided into 2-types:——Prince Grover
Memory-Based CF methods can be divided into two sections: user-item filtering and item-item filtering. Here is the difference:
Item-Item Collaborative Filtering: “Users who liked this item also liked …”
User-Item Collaborative Filtering: “Users who are similar to you (kinda like the twin you never knew you had) also liked …”
Both methods require user-item matrix that contain the ratings for user uu for item ii. From that, you can calculate the similarity matrix.
The similarity values in Item-Item Collaborative Filtering are calculated by taking into account all users who have rated a pair of items.
For User-Item Collaborative Filtering, the similarity values are calculated by observing all items that are rated by a pair of users.
Model-Based Collaborative Filtering
Model-based CF methods are based on matrix factorization (MF). MF methods are used as an unsupervised learning method for latent variable decomposition and dimensionality reduction. They can handle scalability and sparsity problems better than Memory-based CF.
The goal of MF is to learn latent user preferences and item attributes from known ratings. Then use those variable to predict unknown ratings through the dot product of the latent features of users and items.
Matrix factorization restructures the user-item matrix into a low-rank matrix. You can represent it by the multiplication of two low-rank matrices, where the rows contain a vector of latent variables. You want this matrix to approximate the original matrix, as closely as possible, by multiplying the low-rank matrices together. That way, you predict the missing entries in the original matrix.
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import graphlab as gl import pandas as pd import numpy as np import math from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score from math import sqrt from sklearn.metrics import mean_squared_error
Et jamais je n’ai senti, si avant, à la fois mon détachement demoi-même et ma présence au monde.——Albert Camus《Le vent à Djémila》
Also known as
I have never felt so deeply that I am far apart from the soul, but my existence is so real.
First of all I’d like to mention that most of the writings here is not regular.I used to think about adapting the I18N「internationalization」but I won’t.The reason is quite simple cause i dont wanana(now).Lots of times I spoke to myself in English,both pronouncedly and silently.Just like at this moment I wrote in English but I will express the remaining content with Mandarin.
Alright，我发现自己使用「Alright」的次数越来越多了，both pronouncedly and silently,still.我认为这是件好事，或许这代表着自己正在逐步接受当下发生的事实，或许没办法能那么快抽身出去，不过或许能够在一定程度上控制自己的情绪，Listen,I wanana say control my temper but seems that thats Chinglish maybe idont know fuck ha. Alright,listen I wont care about your feelings cause i got mental disease(I mean in my website’s writings only).
就像电影里片名出现时往往代表着正片开始，本文亦如此，想象一下，I believe u’ll make it.
本段或作为结尾 It’s like you said we’re brought up to this, there is nothing left, nothing but to realize and how fuck up the things are. It’s not enough, I won’t last. Listen to me, just listen. We are all the same, we all feel pain, and we all have chaos in our life. Life is very very confusing, I know. I don’t have the answers, but I know if you write it out, it all be okay.
由于各种各样的原因无法完全从Windows转向Linux，在尝试过双系统、VMware以及Hyper-V后，我个人感觉双系统便利性不高，而虚拟化技术资源占用较高，于是便想到了wsl。众所周知，「Windows Subsystem for Linux」即「windows下的Linux子系统」已经问世相当一段时间。作为微软与Canonical公司合作开发的兼容层，我(主观)认为wsl对Ubuntu的支持及优化可能要胜于其他Linux发行版，于是在考虑过微软官方列出的支持发行版本后，选择了Ubuntu。题外话，我认为人们在选择操作系统时应完全按照自己的意愿及使用习惯，对于所谓的操作系统鄙视链我本人嗤之以鼻。
sudo vim /etc/apt/sources.list deb http://mirrors.aliyun.com/ubuntu/ bionic main restricted universe multiverse deb-src http://mirrors.aliyun.com/ubuntu/ bionic main restricted universe multiverse deb http://mirrors.aliyun.com/ubuntu/ bionic-security main restricted universe multiverse deb-src http://mirrors.aliyun.com/ubuntu/ bionic-security main restricted universe multiverse deb http://mirrors.aliyun.com/ubuntu/ bionic-updates main restricted universe multiverse deb-src http://mirrors.aliyun.com/ubuntu/ bionic-updates main restricted universe multiverse deb http://mirrors.aliyun.com/ubuntu/ bionic-backports main restricted universe multiverse deb-src http://mirrors.aliyun.com/ubuntu/ bionic-backports main restricted universe multiverse deb http://mirrors.aliyun.com/ubuntu/ bionic-proposed main restricted universe multiverse deb-src http://mirrors.aliyun.com/ubuntu/ bionic-proposed main restricted universe multiverse