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Recommender System: Matrix Factorization

2017-04-242019-10-22VintaMachine Learning

只要用戶對物品有過行為（Rui > 0），我們就假設用戶對該物品有偏好（Pui = 1），用戶對該物品的行為越多（Rui 越大），Cui 就會越大，表示置信度越高，假設越可信；如果用戶對該物品沒有行為（Rui = 0），就假設用戶對該物品沒有偏好（Pui = 0），但是因為 Cui = 1 + alpha x 0，所以這個假設相對來說置信度較低，比較不可信。

Linux commands cookbook

2017-03-132019-10-22VintaDevOps

The cookbook of useful Linux commands.

Functional Programming in Python

2017-03-022019-10-22VintaPython

map() 會讓 list 的每一個元素都會各自經過 func(x) 去處理，最後得到的會是一個新的數量相同的 list。

MySQL system error codes

2017-02-012019-10-22VintaDatabase, DevOps

Print all OS error codes and MySQL error codes using the perror command.

Calculate the similarity of two vectors

2017-02-012019-10-22VintaMachine Learning, Python

You should only calculate Pearson Correlations when the number of items in common between two users is > 1, preferably greater than 5/10. Only calculate the Pearson Correlation for two users where they have commonly rated items.