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A recommender system for basket items / by Nilanjan Das ; Thesis Supervisor: Prof. Dr. Axel-Cyrille Ngonga Ngomo, Prof. Dr. Gregor Engels, Advisor: Michael Röder, M. Sc. Paderborn, 2020
Inhalt
Introduction
Recommender System
Problem Definition
Objectives and Challenges
Related Work
Structure of the Thesis
Background
Artificial Neural Network
Feed Forward Neural Network
Recurrent Neural Network (RNN)
Long Short Term Memory (LSTM)
Different optimizers used in LSTM
RDF Graph
Graph-Based Data Model (GBDM)
URI and IRI
Literal
Blank node
RDF Graph
Knowledge Graph Embedding
XML Parsing
Types of XML Parser
Types of Recommender Systems
Approach
Proposed Approach Overview
XML Parsing
Data
Data Cleansing and Feature Engineering
Physical Embedding Model
Pyke
Conex
LSTM
Model Selection
LSTM Architecture
Baseline Algorithm
Implementation
Overview
Technologies and Packages
General Workflow
System Design and Architecture
Triples Generation
Vector representation of triples
Prediction Generator
Baseline Prediction Generator
Evaluation
Evaluation Objectives
Preparation
Data
Environment Setup
Experimental Design
Experiment Results
Pyke Analysis
Prediction Performance Comparison
Vector Analysis
Code Improvement Analysis
Discussion
Summary
Significance of Proposed Approach
Advantages
Limitation
Future Work
Conclusion
Bibliography
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