A content boosted collaborative filtering approach for recommender systems based on multi level and bidirectional trust data

Download

index.pdf

Date

2010

Author

Şahinkaya, Ferhat

Metadata

Show full item record

Item Usage Stats

254
views

111
downloads

As the Internet became widespread all over the world, people started to share great amount of data on the web and almost every people joined different data networks in order to have a quick access to data shared among people and survive against the information overload on the web. Recommender systems are created to provide users more personalized information services and to make data available for people without an extra effort. Most of these systems aim to get or learn user preferences, explicitly or implicitly depending to the system, and guess “preferable data” that has not already been consumed by the user. Traditional approaches use user/item similarity or item content information to filter items for the active user; however most of the recent approaches also consider the trustworthiness of users. By using trustworthiness, only reliable users according to the target user opinion will be considered during information retrieval. Within this thesis work, a content boosted method of using trust data in recommender systems is proposed. It is aimed to be shown that people who trust the active user and the people, whom the active user trusts, also have correlated opinions with the active user. This results the fact that the rated items by these people can also be used while offering new items to users. For this research, www.epinions.com site is crawled, in order to access user trust relationships, product content information and review ratings which are ratings given by users to product reviews that are written by other users.

Subject Keywords

Computer enginnering.

URI

http://etd.lib.metu.edu.tr/upload/12612013/index.pdf
https://hdl.handle.net/11511/19624

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

A content boosted collaborative filtering approach for movie recommendation based on local & global similarity and missing data prediction Özbal, Gözde; Alpaslan, Ferda Nur; Department of Computer Engineering (2009) Recently, it has become more and more difficult for the existing web based systems to locate or retrieve any kind of relevant information, due to the rapid growth of the World Wide Web (WWW) in terms of the information space and the amount of the users in that space. However, in today's world, many systems and approaches make it possible for the users to be guided by the recommendations that they provide about new items such as articles, news, books, music, and movies. However, a lot of traditional recommen...
A content based movie recommendation system empowered by collaborative missing data prediction Karaman, Hilal; Alpaslan, Ferda Nur; Department of Computer Engineering (2010) The evolution of the Internet has brought us into a world that represents a huge amount of information items such as music, movies, books, web pages, etc. with varying quality. As a result of this huge universe of items, people get confused and the question “Which one should I choose?” arises in their minds. Recommendation Systems address the problem of getting confused about items to choose, and filter a specific type of information with a specific information filtering technique that attempts to present i...
A singular value decomposition approach for recommendation systems Osmanlı, Osman Nuri; Toroslu, İsmail Hakkı; Department of Computer Engineering (2010) Data analysis has become a very important area for both companies and researchers as a consequence of the technological developments in recent years. Companies are trying to increase their profit by analyzing the existing data about their customers and making decisions for the future according to the results of these analyses. Parallel to the need of companies, researchers are investigating different methodologies to analyze data more accurately with high performance. Recommender systems are one of the most...
Crossing: a framework to develop knowledge-based recommenders in cross domains Azak, Mustafa; Birtürk, Ayşe Nur; Department of Computer Engineering (2010) Over the last decade, excess amount of information is being provided on the web and information filtering systems such as recommender systems have become one of the most important technologies to overcome the „Information Overload‟ problem by providing personalized services to users. Several researches have been made to improve quality of recommendations and provide maximum user satisfaction within a single domain based on the domain specific knowledge. However, the current infrastructures of the recommende...
A hybrid recommendation system capturing the effect of time and demographic data Oktay, Fulya; Alpaslan, Ferda Nur; Department of Computer Engineering (2010) The information that World Wide Web (WWW) provides have grown up very rapidly in recent years, which resulted in new approaches for people to reach the information they need. Although web pages and search engines are indeed strong enough for us to reach what we want, it is not an efficient solution to present data and wait people to reach it. Some more creative and beneficial methods had to be developed for decreasing the time to reach the information and increase the quality of the information. Recommendat...

Citation Formats

F. Şahinkaya, “A content boosted collaborative filtering approach for recommender systems based on multi level and bidirectional trust data,” M.S. - Master of Science, Middle East Technical University, 2010.