The evolving world of data

Categories: Technology World World Wide Web

Essay, Pages 6 (1381 words)

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Introduction

There is data boom going on currently in the world. We are living in the world of data, where data has changed the landscape of doing business. According to a research there are 2.5 quintillion bytes of data is being generated every day [3]. In the last five years, more than 1 billion users have joined the internet. Mobile phones are reshaping the way we are connected to each other. More and more websites are being created every day. About 90 percent of the data has been generated in the last 2 years.

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About half of the world population is online engaging over the social media channels and sharing pictures of their pets. All these users have made internet the biggest community on Earth. The importance of this data cannot be neglected. Countries are now shifting towards smart cities with the help of valuable information this data provides. Every activity online is being recorded in one way or another. There is such vast amount of data available that future predictions are being made on its basis in many areas including business, science and technology.

E-commerce businesses are booming and people are online through their mobile phones and PC searching and buying different products for their different needs. Behavior of these internet users are helping online businesses to conduct operations in a more streamline and organized fashion. Recommendation systems are being designed by business to engage more and more people and provide them with a more robust experience. The recommendations provided by these systems are helping people in making decisions and opening them a new variety of options they never thought of before.

Statement of Problem

More and more data is exploding on this internet every day. While this data provides a lot of useful information, it also confuses an average user. Users are given plantar of resources and options to choose from. Moreover, this information explosion have caused a challenge for users to choose from resulting in information overload. In order to overcome this problem faced by majority of users, recommendation systems were designed and built. These systems not only helped people in their journey online but also helped businesses to generate more revenue. Now a days users are getting personalized recommendations based on their past behavior. Collaborative filtering is used in order to provide these kind of recommendations. However these collaborative filtering techniques also suffer from different type of problems including data sparsity issues and cold start scenarios. Data sparsity problems occur when the system is unable to find many similar users or they are just not enough to provide recommendations .Cold start refers to a problems when a user have just started their online buying journey and have rated an very few items. Both of these problems makes it harder to recommend due to lack of past data and their behavior information. In the research we propose a novel and innovative method of making recommendations which overcomes these problems and provide high quality recommendations by combining trust with traditional collaborative filtering approaches. Trust networks help make find similar and reliable users to overcome fore stated challenges.

Research Significance

This research will help in improvement of recommendations being provided with the help of trust network construction and user similarity based on their past behavior. With the help of trust propagation a new range of users will be addressed and taken into the trust network.

This research will make important and helpful contributions in the online business community and techniques like combining trust with collaborative filtering will help business get more coverage and allow them to provide high quality recommendations to their users.

Methodology of Research

Many research approaches have been used in the past like case studies, surveys and experimentation in order to find desired results. My research will be supported by experimental results and on the basis of these results different evaluations for different scenarios will be made.

Organization of Thesis

This Thesis is organized in the following way

Chapter 1: This chapter provides information for the research area and also some past information on the building of these recommender systems.

Chapter 2: This chapter provides information of the significant and relevant work that has been done in this research area and different techniques used by researchers to overcome problems.

Chapter 3: This chapter gives more detailed information of the proposed trust network construction and merging techniques to be used alongside collaborative filtering.

Chapter 4: This chapter presents the implementation of the proposed methodology and gives experimental results to be evaluated.

Chapter 5: This chapter provides information for the research area and also some past information on the building of these recommender systems.

Chapter 6: This chapter concludes my thesis highlighting this research contributions and opening new research areas for future research.

Literature Review

In this chapter we have discussed in detail of the past research that have been done in the field of recommendation systems, CF and Trust based systems. Both of these systems.

Recommendation Systems

Research on recommendation systems have been going on for a decade now and have gone through many phases. These RS allow users to get more suggestions based on the items and products that have liked or bought before [1]. Online marketplaces like Amazon have stated that RS comprises of 30% of their total revenue. Moreover, social media channels like Facebook, Twitter and YouTube depends mostly on these systems to provide content to their users. Behavior and interests of users heavily impact the recommendations that they are getting and the predictions depends on them entirely [2]. There are many types of approaches used for recommendations including content based, collaborative filtering based and hybrid approaches by combining these two methods [4].

Combining Trust and Collaborating Filtering

The Combining Process

In this chapter we will present our proposed method of combining trust based rating with collaborative filtering approaches. Firstly, the most trusted and nearest neighbors of the user are identified and then trust propagation is used to identify more trusted neighbors, which are useful for cold start scenarios. Secondly, a single value is formed by merging all of the trust rating from trusted neighbors. Every merged rating is calculated for a single item which must be rated by at least one trusted neighbor. In the end, similar users are identified based on the merged rating compiled before. With the help of these ratings recommendations are generated just like any other CF techniques. This whole process is further explained in the following sections.