Enhancing Prediction Accuracy of A Multi-Criteria Recommender System Using Adaptive Genetic Algorithm

Enhancing Prediction Accuracy of A Multi-Criteria Recommender System Using Adaptive Genetic Algorithm

ABSTRACT

Recommender systems are powerful intelligent systems considered to be the solution to the problems of information overload. They provide users with personalized lists of recommended items, using some machine learning techniques.

Traditionally, existing recommender systems have used single rating techniques to estimate users’ opinions on items.

Because user preferences might depend on the attributes of several items, the efficiency of traditional single-rating recommender systems is considered to be limited, since they cannot account for various items’ attributes.

A multi-criteria recommendation is a new technique that uses ratings of various items’ attributes to make more efficient predictions.

Nevertheless, despite the proven accuracy improvements of multi-criteria recommendation techniques, research needs to be done continuously to establish an efficient way to model criteria ratings.

TABLE OF CONTENTS

CERTIFICATION ………………………………………………………………………………………………..ii
ABSTRACT ……………………………………………………………………………………………………… v
ACKNOWLEDGEMENTS ……………………………………………………………………………………vi
DEDICATION ………………………………………………………………………………………………….viii
LIST OF FIGURES …………………………………………………………………………………………….xii
LIST OF TABLES……………………………………………………………………………………………..xiii
CHAPTER ONE: INTRODUCTION……………………………………………………………………….. 1
1.1 Introduction……………………………………………………………………………………………….. 1
1.2 Background of the study……………………………………………………………………………. 1
1.2.1 Recommender system techniques…………………………………………………………… 2
1.2.2 Multi-criteria recommender system………………………………………………………… 3
1.2.3 Genetic algorithm………………………………………………………………………………. 3
1.3 Statement of the problem…………………………………………………………………………… 5
1.4 Aim and objectives of the study ………………………………………………………………….. 5
1.5 Significance of the study …………………………………………………………………………… 5
1.6 Scope of the study …………………………………………………………………………………… 6
1.7 Expected results ……………………………………………………………………………………… 6
1.8 Thesis structure ………………………………………………………………………………………. 6
CHAPTER TWO: LITERATURE REVIEW ……………………………………………………………… 7
2.1 Introduction …………………………………………………………………………………………… 7
2.2 Overview of Recommender Systems and their applications………………………………… 7
2.3 Ratings…………………………………………………………………………………………………. 9
2.4 Types of rating ……………………………………………………………………………………….. 9
2.5 Measure of accuracy ………………………………………………………………………………. 10
2.6 Overview of collaborative filtering …………………………………………………………….. 11
2.6.1 Types of collaborative filtering……………………………………………………………. 11
2.6.2 Application of collaborative filtering…………………………………………………….. 14
2.7 Genetic Algorithm“………………………………………………………………………………… 15
2.7.1 Initial population……………………………………………………………………………… 16
2.7.2 Fitness evaluation…………………………………………………………………………….. 16
2.7.3 Selection ……………………………………………………………………………………….. 17
2.7.4 Crossover………………………………………………………………………………………. 17
2.7.5 Mutation……………………………………………………………………………………………. 18
2.7.6 Termination……………………………………………………………………………………. 18
2.8 Cold start problem …………………………………………………………………………………. 19
2.9 Multi-criteria recommender systems…………………………………………………………… 19
2.9.1 Similarity-based approach………………………………………………………………….. 20
2.9.2 Aggregation function-based approach …………………………………………………… 21
2.10 Review of related studies………………………………………………………………………. 22
2.10.1 New recommendation techniques for multi-criteria rating systems ……………….. 22
2.10.2 An item-based multi-criteria collaborative filtering algorithm for personalized
recommender systems………………………………………………………………………………….. 23
2.10.3 Multi-criteria collaborative filtering with high accuracy, using higher-order
singular-value decomposition and a neuro-fuzzy system ……………………………………….. 24
2.10.4 Accuracy improvement for multi-criteria recommender systems ………………….. 26
2.10.5 Evaluation of recommender systems: A multi-criteria decision-making approach 26
2.10.6 Using genetic algorithm for measuring similarity values between users in
collaborative filtering recommender systems ……………………………………………………… 27
2.10.7 Improving collaborative filtering recommender system results and performance,
using genetic algorithm ………………………………………………………………………………… 28
2.10.8 Our solution …………………………………………………………………………………… 29
CHAPTER THREE: RESEARCH METHODOLOGy ………………………………………………… 30
3.1 Introduction……………………………………………………………………………………………… 30
3.2 Multi-criteria recommender system ……………………………………………………………. 30
3.3 Data set description………………………………………………………………………………… 31
3.4 Choice of programming language ………………………………………………………………. 34
3.5 Proposed system……………………………………………………………………………………. 34
3.5.1 Predicting N multi-criteria ratings………………………………………………………… 35
3.5.2 Asymmetric singular-value decomposition (ASVD)………………………………….. 35
3.5.3 Learning the function………………………………………………………………………… 36
3.5.4 Predicting the overall rating ……………………………………………………………….. 40
CHAPTER FOUR: IMPLEMENTATION……………………………………………………………….. 44
4.1 Introduction …………………………………………………………………………………………. 44
4.2 Performance evaluation …………………………………………………………………………… 44
4.3 Result and discussion ……………………………………………………………………………… 46
4.4 Conclusion…………………………………………………………………………………………… 49
CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATION ……………….. 50
5.1 Introduction……………………………………………………………………………………………… 50
5.2 Summary and contributions ……………………………………………………………………… 50
5.3 Conclusion…………………………………………………………………………………………… 51
5.4 Recommendation and future work ……………………………………………………………… 52
REFERENCE…………………………………………………………………………………………………… 53

 

INTRODUCTION

Intelligent systems are systems that require knowledge organisation to interpret, test and analyse acquired information.

Intelligent systems are required in most of our day-to-day activities, such as e-commerce, online-booking, social media, e-shopping and other information-rich environments.

Recommender systems interact with users in a personalized way, obtain information about a user’s tastes or preferences and use this knowledge to make suggestions and provide assistance in situations where users have to make a decision between a wide range of possible options.

In this chapter we endeavour to explain the recommender system and its techniques, introduce multi-criteria recommender systems and also a genetic algorithm. Statement of the problem, aims and objectives, significance and scope of this study will also be introduced in this chapter.

REFERENCE

Abdullah, S., & Turabieh, H. (2008). Generating university course timetable using Genetic
Algorithms and local search. In Proceedings – 3rd International Conference
Adomavicius, G., & Kwon, Y. (2007). New recommendation techniques for multicriteria rating
systems. IEEE Intelligent Systems, 22(3), 48–55. https://doi.org/10.1109/MIS.2007.58
Adomavicius, G., & Tuzhilin, a. (2005). Toward the Next Generation of recommender systems:
a Survey of the State of the Art and Possible Extensions.
Adomaviciuszan, G., & Tuzhilin, H. (2014). INFORMS Tutorials in Operations Research
Personalization and recommender systems. https://doi.org/10.1287/educ.1080.0044
Alhijawi, B. (2016). Using genetic algorithms for measuring the similarity values between users
in collaborative filtering recommender
Bobadilla, J., Ortega, F., Hernando, A., & Alcal??, J. (2011). Improving collaborative filtering
recommender system results and performance using genetic algorithms. Knowledge-Based
Systems

 

Comments are closed.

Hey Hi

Don't miss this opportunity

Enter Your Details