A Fuzzy-Based Approach for Modelling Preferences of Users in Multi-Criteria Recommender Systems.
ABSTRACT
Recommender systems are web-based platforms or software that use various machine learning methods to propose useful items to users. Several techniques have been used to develop such a system for generating a list of recommendations.
Multi-criteria is a new technique that recommends items based on multiple characteristics or attributes of the items. This technique has been used to solve many recommendation problems and its predictive performance has been tested and proven to be more effective than the traditional approach.
However, current research has shown that there is still a need to use some machine learning techniques in modelling the criteria ratings in multi-criteria recommendation techniques. The proposed project aimed to present a model that is based on the architecture and main features of fuzzy sets and systems.
Fuzzy Logic (FL) is a method of reasoning that resembles human reasoning. It is one of the machine learning techniques that is widely known for its effective application in different fields of study.
Its main advantage is that it does not need a lot of data to train, coupled with its ability to combine human heuristics into computer-assisted decision making, which is highly applicable in the domain of recommender systems.
The proposed project is designed to test and provide the predictive performance of the fuzzy-based multi-criteria technique and compare it with some of the existing methods.
The main focus of this research is to model a system that can optimize the prediction accuracy of an RS, increase in ranking accuracy, and thus obtain high correlation between the predicted and actual values.
Experimental results performed on real-world datasets (Yahoo movies) proved that the proposed technique (Fuzzy Multi-criteria Recommender System) remarkably improved the accuracy of prediction in multi-criteria CF RS. The system was implemented using java programming language.
TABLE OF CONTENTS
CERTIFICATION …………….. ii
ABSTRACT ……… v
ACKNOWLEDGEMENTS ………. vii
DEDICATION ………. ix
LIST OF FIGURES …………. xii
LIST OF ACRONYMS ……… xii
CHAPTER ONE INTRODUCTION
1.1 Background of the Study …………………………. 1
1.2 Recommender Systems (RS) ……. 1
1.2.1 Traditional Single rating ……………. 4
1.3 Multi-Criteria Recommender System ………… 6
1.4 Recommendation Techniques ……………. 7
1.5 Reasons for using Recommender Systems: The following are reasons why we need a Recommender system: 8
1.6 Summary of task done by the RS …… 9
1.7 Fuzzy Set and Logic ………….. 9
1.8 Aim and Objectives …….. 10
1.9 Research Question ….. 11
1.10 Thesis Structure ……. 11
CHAPTER TWO LITERATURE REVIEW
2.1 Problems of Collaborative Filtering Recommender System ………. 13
2.1.1 Data Sparsity …………… 13
2.1.2 Cold Start ………………… 15
2.1.3 Scalability ………. 16
2.2 Improvements in Prediction Accuracy …………. 17
2.3 Advantages of Multi-Criteria Recommender System ….. 19
2.4 Fuzzy Logic Recommender Systems …………….. 20
CHAPTER THREE RESEARCH FRAMEWORK AND METHODOLOGY
3.1 Fuzzy logic system ……………….. 23
3.1.1 Basic Fuzzy operations: …………………. 24
3.2 Multi-criteria Recommender System: ………… 26
3.3 Collaborative Filtering: …………………………….. 26
3.3.1 Asymmetric Singular Value Decomposition (AsySVD) ………. 27
3.4 Model based Approach ………… 28
3.4.1 Aggregation function: …………………. 29
3.5 The Proposed System framework ………………. 31
3.5.1 General Algorithm for the Proposed System: …….. 33
3.5.2 Multi-Criteria rating decomposition …………………….. 34
3.5.3 Learning the Function …………… 34
3.5.4 Predicting the overall rating: …………….. 39
CHAPTER FOUR IMPLEMENTATION
4.1 Implementation of the Proposed System ………… 41
4.2 Experimental dataset and setup ………………….. 42
4.3 The Evaluation metrics ……………….. 45
4.4 Results and Discussion ………………. 47
4.4.1 Experiment …………. 47
CHAPTER FIVE CONCLUSION
5.1 Conclusion …….. 51
5.2 Challenges …….. 52
5.3 Future work ……….. 53
REFERENCES 54
INTRODUCTION
This chapter presents a general introduction to the Recommender System, the proposed system frameworks, problem statement, research aim and objectives, research questions, and structure of the thesis.
Background of the Study
The rapid growth of Internet of things (IoT) and fast development of e-commerce websites, has given rise to the pressing need for a recommender system.
Users found it difficult to arrive at the most appropriate choice given the immense variety of items (products and services) that these websites offered.
The explosive growth and variety of information available on the Web and the rapid introduction of new e-business services (selling products, product comparison, auctions, etc.) frequently overwhelmed users, leading them to make poor decisions.
Consequently, the availability of choices, instead of producing a benefit, started to decrease users’ well-being.
It was understood that while choice is good, extra choice is not always the best, as this leads to information overload, which muddles the user of the system on the right choice to make from the increasing number of options available, therefore, the need for recommender system (RS).
Recently, RS has proven to be a valuable means of coping with the information overload problem. Ultimately an RS addresses this phenomenon by pointing a user towards new, not-yet-experienced items that may be relevant to the user’s current task.
REFERENCES
Adomavicius, G., & Kwon, Y. (2007). New Recommendation Techniques for Multi-Criteria. Adomavicius, G., & Tuzhilin, A. (n.d.). Towards the Next Generation of Recommender Systems : A Survey of the State-of-the-Art and Possible Extensions, 1–43.
Adomavicius, G. & A. Tuzhilin. Multidimensional recommender systems: a data warehousing approach. In Proc. of the 2nd Intl. Workshop on Electronic Commerce (WELCOM’01). Lecture Notes in Computer Science, 2232, Springer, 2001b.
Zadeh, A. (1994). Fuzzy Logic, Neural Networks, and Soft Computing. Comm. ACM, 37 (3), 77-84.
Zadeh, A. (1975). The concept of a linguistic variable and its applications to approximate reasoning. Part I. Information Sciences 8, 199–249.
Balabanovic, M., Shoham, Y. (1997). Content-based, collaborative recommendation. Communication of ACM 40(3), 66–72.