Building recommendation engine in social network environment

Dissertant

Tahhan, Ahmad

Thesis advisor

Tadmuri, Sarah
Hattab, Izz al-Din Shakir Hasan

Comitee Members

Atum, Jalal Yusuf
al-Dawud, Ali Asad Ahmad

University

Princess Sumaya University for Technology

Faculty

King Hussein Faculty for Computing Sciences

Department

Department of Computer Sciences

University Country

Jordan

Degree

Master

Degree Date

2014

English Abstract

The widespread use of social networks and the accessibility of personal profile information have made it possible to build recommendation engines that exploit personal profiles.

Collaborative filtering is one of the most commonly used algorithms in recommendation engines.

This research leverages the abilities of collaborative filtering by introducing trust-based and social factors to modify existing similarity and neighborhood measures.

Using this hybrid approach, a 45% reduction in error rate has been shown as measured by root mean squared error, comparing a baseline collaborative filtering with trust-aware collaborative filtering.

This work experiments on multiple variables in collaborative filtering including neighborhood size, and threshold value in neighborhood formation sub-process.

This work also experiments on multiple similarity measures to identify the best combination of neighborhood formation and similarity that gives an optimal solution in a social trust context.

Main Subjects

Information Technology and Computer Science

Topics

• Virtual Community
• Communication
• Treaty on Conventional Armed Forces in Europe (1990)

No. of Pages

94

Table of Contents

• APA
• MLA
• AMA

Language

English

Data Type

Arab Theses

Record ID

BIM-414113