Mealionaire: A context-aware and ontology-based mobile recommender system for...Carter Chen
The night markets and the small eats in Taiwan are famous all over the world, eating is the big thing in Taiwan. No matter what time is it, people here in Taiwan can go outdoors, accessing vendors and get something to eat. Although those food vendors and restaurants provide a great variety of fresh food for people to choose, people have a hard time to make decisions sometimes. In this study, we have proposed a mobile recommender system, which is called Mealionaire. The recommender system is context-aware, which means it can perceive the context around the user and the recommendations it produced will adapt to the context whenever the context changes. In addition, it also introduces ontology technologies to analyze the relations between the user, the restaurants and the dishes which are served by the restaurants, in order to provide tailored, personalized results for the user in real-time fashion. The evaluation results show that Mealionaire can provide high user satisfaction, and it is capable to solve the problems that users may face when they are looking for appropriate dishes to eat in their daily lives.
Impressed Structure In Deep Learning
Multi-task Learning
Domain adaptation
Transfer learning
GAN
AnoGAN, Anomaly GAN
Reinforcement learning
Capsule, Spiking NN
Black-box In Deep Learning
LIME, Local interpretable model-agnostic explanations
LRP, Layer-wise relevance propagation
Advantage of Statistics In Deep Learning
Mealionaire: A context-aware and ontology-based mobile recommender system for...Carter Chen
The night markets and the small eats in Taiwan are famous all over the world, eating is the big thing in Taiwan. No matter what time is it, people here in Taiwan can go outdoors, accessing vendors and get something to eat. Although those food vendors and restaurants provide a great variety of fresh food for people to choose, people have a hard time to make decisions sometimes. In this study, we have proposed a mobile recommender system, which is called Mealionaire. The recommender system is context-aware, which means it can perceive the context around the user and the recommendations it produced will adapt to the context whenever the context changes. In addition, it also introduces ontology technologies to analyze the relations between the user, the restaurants and the dishes which are served by the restaurants, in order to provide tailored, personalized results for the user in real-time fashion. The evaluation results show that Mealionaire can provide high user satisfaction, and it is capable to solve the problems that users may face when they are looking for appropriate dishes to eat in their daily lives.
Impressed Structure In Deep Learning
Multi-task Learning
Domain adaptation
Transfer learning
GAN
AnoGAN, Anomaly GAN
Reinforcement learning
Capsule, Spiking NN
Black-box In Deep Learning
LIME, Local interpretable model-agnostic explanations
LRP, Layer-wise relevance propagation
Advantage of Statistics In Deep Learning
2023 Supervised Learning for Orange3 from scratchFEG
This document provides an overview of supervised learning and decision tree models. It discusses supervised learning techniques for classification and regression. Decision trees are explained as a method that uses conditional statements to classify examples based on their features. The document reviews node splitting criteria like information gain that help determine the most important features. It also discusses evaluating models for overfitting/underfitting and techniques like bagging and boosting in random forests to improve performance. Homework involves building a classification model on a healthcare dataset and reporting the results.
This document provides an overview of unsupervised learning techniques including k-means clustering and association rule mining. It begins with introductions to the speaker and tutorial topics. It then contrasts supervised vs unsupervised learning, describing how k-means is used for clustering without labels and how association rules can discover relationships between items. The document provides examples of applying these techniques in domains like retail, sports, email marketing and healthcare. It also includes visualizations and discusses important concepts for k-means like data transformation and for association rules like support, confidence and lift. Homework questions are asked about preparing data for these algorithms in Orange.
202312 Exploration Data Analysis Visualization (English version)FEG
This document provides an overview of exploratory data analysis (EDA) and visualization techniques that can be performed before building a machine learning model. It introduces the Iris dataset as an example and outlines the key steps of EDA, including loading the data, examining correlations, creating scatter plots, and generating distribution and box plots to understand feature statistics. As homework, students are asked to explore another dataset with a numeric target feature called "housing.tab" and explain the visualizations.
202312 Exploration of Data Analysis VisualizationFEG
This document provides a tutorial on data visualization and analysis using Orange 3. It discusses different types of charts like pie charts, line charts, histograms, bar charts, scatter plots, box plots, and pivot tables. It demonstrates how to visualize survival rates from the Titanic dataset based on features like sex, passenger class, age, and fare paid. Key findings are that women and higher class passengers had higher survival rates, and survival rates also depended on combinations of these features.
Transfer learning (TL) is a research problem in machine learning (ML) that focuses on applying knowledge gained while solving one task to a related task
This document provides a summary of image classification using deep learning techniques. It begins with an introduction to the speaker and their background. It then discusses the main types of image AI tasks like classification, detection, and segmentation. The document reviews the history and timeline of deep learning, important datasets like ImageNet, and algorithms such as convolutional neural networks. It presents the typical process flow for image-based deep learning including feature extraction using convolutional and pooling layers, classification layers, and different network architectures. The document concludes by discussing a homework assignment on building a multi-class image classification model using a dataset of dog, cat, and bird images.
This document provides an introduction and tutorial on using Google Colab. It discusses the speaker's background and experience, then demonstrates how to run sample Python codes in a Colab notebook. It shows how to open an existing Colab file, access computing resources on Colab including GPUs and TPUs, create a new Colab file, and interact with a Google Drive folder to access and save files. The document concludes by providing a homework assignment to have students run Python code in Colab and interact with their Google Drive.
This document provides an introduction and overview of sequence-to-sequence (seq2seq) models, transformer models, attention mechanisms, and BERT for natural language processing. It discusses applications of seq2seq models like language translation and text summarization. Key aspects covered include the encoder-decoder architecture of seq2seq models, how attention improves seq2seq by allowing the model to focus on relevant parts of the context, and the transformer architecture using self-attention rather than recurrent layers. BERT is introduced as a bidirectional transformer model pre-trained on large unlabeled text that achieves state-of-the-art results for a range of NLP tasks. Code examples and homework suggestions are also provided.
This document provides a tutorial on recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. It begins with introductions to the speaker and an overview of the content. It then explains RNNs and how they work sequentially through hidden layers. Issues like vanishing gradients are discussed. LSTMs are introduced as an advanced RNN that can retain information over longer periods of time using gates. Pre-trained word embeddings like Word2Vec, GloVe, and FastText are briefly explained. Finally, homework is assigned to build a sentiment analysis model using an LSTM and pre-trained word embeddings on a Chinese text dataset.
2. About me
• Education
• NCU (MIS)、NCCU (CS)
• Work Experience
• Telecom big data Innovation
• AI projects
• Retail marketing technology
• User Group
• TW Spark User Group
• TW Hadoop User Group
• Taiwan Data Engineer Association Director
• Research
• Big Data/ ML/ AIOT/ AI Columnist
2
27. Hard clustering v.s. Soft clustering
• Hard clustering
• Each point is assigned to a one and only one cluster (hard assignment)
• With K-means we try to find K centroids {μ1,…,μK} and the corresponding
• Soft clustering (Fuzzy C-Means Clustering: FCM)
• Each point is assigned to all the clusters with different weights or probabilities
(soft assignment)
27
28. Hard clustering v.s. Soft clustering
• 演算法步驟
• 透過模糊分群的方式,計算每個訓練樣本屬於各群的程度
• 該方法利用了隸屬值 (membership value) 進行模糊化
• 類似機率的概念,每個樣本最終的隸屬值之和為1
• 演算法步驟 (1)
• 訓練樣本 x
• 劃分 c 群
• ci 為該群的中心點
• 某樣本 xj 屬於 ci ,隸屬值表示為 uij
28