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Introduction:

Causal inference has become increasingly important in the field of recommendation systems, as it allows us to understand not just correlations between user preferences and recommendations, but also the causal relationships that drive user choices. By using causal inference techniques, we can better understand why users make certain choices and how recommendations can influence these choices. This thesis aims to explore the use of causal inference in recommendation systems and its implications for improving the effectiveness of recommendation algorithms.

Table of Contents:

Chapter 1: Introduction
1.1 Background
1.2 Problem Statement
1.3 Objectives of Study
1.4 Limitations of Study
1.5 Scope of Study

Chapter 2: Literature Review
2.1 Overview of Recommendation Systems
2.2 Causal Inference Techniques
2.3 Previous Studies on Causal Inference in Recommendation Systems

Chapter 3: Research Methodology
3.1 Data Collection
3.2 Data Analysis
3.3 Causal Inference Models
3.4 Experimental Design

Chapter 4: Discussion of Findings
4.1 Causal Relationships in Recommendation Systems
4.2 Implications for Recommendation Algorithms
4.3 Challenges and Limitations
4.4 Future Research Directions

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Practical Implications
5.4 Recommendations for Future Research

Thesis Overview:

Causal inference is a powerful tool that can help us understand the underlying mechanisms behind user preferences and recommendation choices in recommendation systems. By going beyond simple correlations and identifying causal relationships, we can improve the effectiveness of recommendation algorithms and provide more personalized recommendations to users. This thesis will explore the use of causal inference in recommendation systems, with a focus on its implications for algorithm design and user experience. Through a literature review, research methodology, and discussion of findings, we will examine the current state of the field and propose future research directions. By the end of this thesis, we hope to provide a comprehensive overview of the role of causal inference in recommendation systems and its potential for shaping the future of personalized recommendation technology.

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