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Introduction:

Collaborative filtering is a popular technique used in recommendation systems to provide personalized suggestions to users based on their preferences. In recent years, research has focused on improving collaborative filtering for sequential recommendation, where the goal is to recommend a sequence of items that are likely to be of interest to the user based on their historical interactions.

Objective of Study:

The objective of this thesis is to explore the effectiveness of collaborative filtering for sequential recommendation and to develop algorithms that can accurately predict the next item in a user’s sequence. The study aims to compare different collaborative filtering approaches and evaluate their performance on real-world datasets.

Limitation of Study:

This study will focus on collaborative filtering techniques for sequential recommendation and will not consider other types of recommendation algorithms. Additionally, the study will be limited to evaluating the performance of the algorithms on a specific set of datasets and may not generalize to all recommendation scenarios.

Scope of Study:

The scope of this study includes reviewing the existing literature on collaborative filtering for sequential recommendation, designing and implementing novel algorithms, evaluating their performance on benchmark datasets, and discussing the findings in relation to existing research.

Table of Contents:

Chapter 1: Introduction
– Introduction
– Objective of Study
– Limitation of Study
– Scope of Study

Chapter 2: Literature Review
– Overview of Recommendation Systems
– Collaborative Filtering for Sequential Recommendation
– Existing Algorithms
– Evaluation Metrics

Chapter 3: Research Methodology
– Data Collection
– Algorithm Design
– Experimental Setup

Chapter 4: Discussion of Findings
– Performance Comparison
– Analysis of Results
– Limitations and Future Directions

Chapter 5: Conclusion and Summary
– Summary of Findings
– Contributions to the Field
– Recommendations for Future Research

Thesis Overview:

Collaborative filtering is a widely used technique in recommendation systems that aims to provide users with personalized suggestions based on their past interactions. In the context of sequential recommendation, the goal is to recommend a sequence of items to users in a way that reflects their preferences and interests. This thesis will explore the effectiveness of collaborative filtering for sequential recommendation by reviewing the existing literature, developing novel algorithms, and evaluating their performance on real-world datasets. The study will focus on comparing different collaborative filtering approaches and analyzing their strengths and weaknesses. By the end of this thesis, we hope to provide insights into how collaborative filtering can be applied to sequential recommendation and contribute to the ongoing research in this area.

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