Developing Sequence-Aware Recommender Systems with Hidden Markov Models

General Information:

Cover Page:

Outline:

Acknowledgment
Acronyms
General introduction
Background
Problem Statement
Delimitations
Approach
Outline
1 Sequence-Aware Recommender Systems
1 Introduction
2 Recommender Systems
2.1 Recommender Systems categories
2.1.1 Collaborative Filtering Models
Content-Based Recommender Systems
2.1.3 Knowledge-Based Recommender Systems
2.1.4 Utility-Based Recommender Systems
2.1.5 Demographic Recommender Systems
2.1.6 Hybrid Recommender Systems
2.2 Challenges of Recommender systems
2.3 Goals of Recommender Systems.
2.4 Sequential and classical recommender systems
3 Sequence-Aware Recommender Systems
3.1 Characterizing Sequence-Aware Recommender Systems
3.2 Sequential Recommendation Tasks
3.3 Behavior sequence types
3.4 Approaches
4 Conclusion.
2 Hidden Markov Models
1 Introduction.
2 Artificial Intelligence
3 Machine Learning
3.1 Developing a Machine Learning Model
3.2 Machine Learning Types
3.3 Machine Learning Challenges
4 System models
5 Markov chains.
6 Hidden Markov Model
6.1 The Problems of HMM
6.2 Solutions to the Three Basic Problems of HMM
6.2.1 Solution to Problem 1
6.2.2 Solution to Problem 2
6.2.3 Solution to Problem 3
6.3 Types of Hidden Markov Models
7 HMM application example
Conclusion.
An HMM Architecture for SARS Development
Introduction . . . .
2.1 HMM Model architecture
2.2 DataSet
2.3 Development process
2.3.1 Data processing
2.3.2 Parameter initialization
2.3.3 Next-Item Recommendation
Implementation
2.4.1 Software
2.4.1.1 Python
2.4.1.2 Pomegranate
2.4.1.3 Anaconda Navigator
2.4.1.3.1 The Jupiter Notebook
2.4.2 Hardware
2.5 Experiments
2.5.1 Evaluation Metrics
2.6 Results.
2.7 Discussion
3 Conclusion.
General conclusion
Summary
Directions for future research
Bibliography

Download The Thesis:

📥 تحميل الملف

For more
academic sources and references,
including theses and dissertations from Algerian universities,
, visit our main website.