MLAI
-
Matrix FactorizationMLAI/RecommendSystem 2022. 7. 12. 18:58
Introduction Split the matrix into the product of 2 other matrices We call it R hat because it only approximates R - it is our model of R We would like W and U to be very skinny $W(N \times K)$ - users matrix, $U(M \times K)$ - movie matrix K somewhere from 10-50 The scale of matrix R, W, and U Key: $W$ and $U$ should be much smaller than $R$ $R$ is $N \times M$ Generally, we can't store $R$ in ..
-
Item-Item Collaborative FilteringMLAI/RecommendSystem 2022. 7. 12. 14:49
Intuition The correlation between the column vectors is high If you like Power Rangers, you'll also like Transformers because users give them similar ratings Power Rangers Transformers Ninja Turtles User 1 4.5 5 4 User 2 5 5 4.5 User 3 1 2 0.5 User 4 2 2 0.5 User-User Collaborative Filtering For user-user CF, I want to find "users like me" The movies that those users have seen, that I haven't se..
-
Sigmoid and SoftmaxMLAI/DeepLearning 2022. 7. 7. 18:32
1. Softmax Softmax function calculates the probability distribution of the event over k different events. This function will calculate the probabilities of each target class over all possible target classes. Equation $$P(y=j | x) = \frac{e^{x_j}}{\sum_{k=1}^K e^{x_k}}$$ Plot x = np.arange(-2.0, 6.0, 0.1) input = np.vstack([x, np.ones_like(x), 0.2 * np.ones_like(x)]) Characteristic It normalizes ..
-
AWS PersonalizeMLAI/RecommendSystem 2022. 7. 7. 17:59
Built-in Recipe (Model) Sims Based on collaborative filtering. SIMS identifies the co-occurrence of the item in user histories in your Interaction dataset to recommend similar items. For example, with SIMS Amazon Personalize could recommend coffee shop items customers frequently bought together or movies that different users also watched. Recommended for improved item searchability and faster pe..
-
User-User Collaborative FilteringMLAI/RecommendSystem 2022. 7. 7. 17:41
Batman X-Men Star Wars The Notebook Bridget Jones' Diary Alice 5 4.5 5 2 1 Bob 4.5 4 2 2 Carol 2 3 1 5 5 Intuitively, we see that Bob’s ratings are similar to Alice’s, thus he is likely to also like Star Wars. In math-speak, Alice’s and Bob’s ratings are highly correlated. Average Rating Limitations It treats everyone’s rating of the movie. equally. Bob’s s(i, j) equally depends on Alice’s ratin..
-
Association AnalysisMLAI/RecommendSystem 2022. 7. 7. 17:31
Definition As a role-based model, it is an algorithm that finds out how an item relates to another item. This association exists in two forms. How often are they purchased together? (Frequent) If someone bought item A, will he also buy item B? It is also expressed as a shopping Market basket analysis because it is similar to looking at which products are contained in one shopping basket. Ex) Fam..
-
Activation FunctionsMLAI/DeepLearning 2020. 1. 30. 14:32
1. Overview In artificial neural networks, the activation function of a node defines the output of that node given an input or set of inputs. A standard computer chip circuit can be seen as a digital network of activation functions that can be "ON" (1) or "OFF" (0), depending on the input. This is similar to the behavior of the linear perceptron in neural networks. However, only nonlinear activa..
-
Canonical Correlation AnalysisMLAI/DimensionalityReduction 2020. 1. 25. 17:57
1. Overview Canonical Correlation Analysis (CCA) as a good prediction model. Because CCA well explains data dependency between input and output. So CCA can minimize the prediction error. CCA finds pairs of basis that maximize the correlation between two variables x and y in subspace. When we perform the regression in the reduced space, the fitting errors are minimized because two variables are h..