A SMART MEDIA-BASED RECOMMENDATION SYSTEM

Abstract

Smart media devices such as: smartphones and tablets are getting more powerful, smarter, cheaper and hence more popular. Recommendation systems become very common in e-business and e-Commerce, for example: Amazon, Google, eBay, Facebook, etc. all are using recommendation systems to promote their business. Recommendation systems are rarely used in learning; however it can be very useful.

The proposed project works as follow:

• Send a learning query to sites, sources and repositories across the Web and gather relevant information through the use of recommendation system that filter all the useless or irrelevant materials off the main list of recommended items.         Filter result from other user’s preference using collaborative-filtering, having the current query in mind. 
• Use TFIDF for content-based filtering and ranking of the shortlisted pages or articles.
• Present the shortlist based on their rank to the user of the system.

1.0 Introduction

With the growth of the web, there is an explosion in the size of content available to various users around the world. The materials available are a mixture of useful and useless documents to the interest of the user. This show gives the need for a better way of getting useful document from the web and mostly through the taste of the user of the web. This project aims at using the taste and the behavior of a user to get documents that are relevant to what the user needs. Depending on the choices of the user, they may have people with the same taste as them, and the system is to harness this opportunity and recommend materials, based on interaction of the neighbor of the users with other interesting materials. Using a collaborative filtering approach, the system can find the items (document) that other users with similar interest with the current user have read and rated to a good degree. In the early stages of the system’s life, the system can’t depend on the Collaborative approach alone, or else the system’s recommendation will be inadequate, thus prompting the need for another filtering approach to support the weaknesses of the collaborative approach. The most use filtering approach that has been used with the collaborative filtering approach is the “Content-based filtering” approach. Adding a content-based filter to the result of the collaborative will yield a more concrete recommendation that will be close to what the user requires.

1.1. Context

Recently Recommendation Systems are becoming common and important in e-technologies such as e-business and e-learning. Many major companies such as Amazon, Google, etc. have its own recommendation system. Recommendation systems can help customers to pick up the most relevant products that fit with their needs. In the area of e-learning and learning in general, there is no much research have been done to design and build a reliable recommendation system that can help learners to pick up the most relevant materials that can speed up and enhance their learning process. In this project we plan to design and implement a learning recommendation system, which supports smart media such as smartphone and tablets, as its clients. To achieve this, the system needs to be hosted probably on a remote server with the capability to run codes efficiently, and optimally. A server side will be written in Java Servlets in order to be able to use various libraries available. All communication will be across a network and the source data will be from the internet.

Department: Computer Science (M.Sc Thesis)

Format: MS Word

Chapters: 1 - 5, Preliminary Pages, Abstract, References, Appendix.

No. of Pages: 72

Price: 20,000 NGN

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