A content based movie recommendation system empowered by collaborative missing data prediction

Download

index.pdf

Date

2010

Author

Karaman, Hilal

Metadata

Show full item record

Item Usage Stats

389
views

111
downloads

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 information items that are likely of interest to the user. A variety of information filtering techniques have been proposed for performing recommendations, including content-based and collaborative techniques which are the most commonly used approaches in recommendation systems. This thesis work introduces ReMovender, a content-based movie recommendation system which is empowered by collaborative missing data prediction. The distinctive point of this study lies in the methodology used to correlate the users in the system with one another and the usage of the content information of movies. ReMovender makes it possible for the users to rate movies in a scale from one to five. By using these ratings, it finds similarities among the users in a collaborative manner to predict the missing ratings data. As for the content-based part, a set of movie features are used in order to correlate the movies and produce recommendations for the users.

Subject Keywords

Computer enginnering.

URI

http://etd.lib.metu.edu.tr/upload/12612037/index.pdf
https://hdl.handle.net/11511/19670

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

A content boosted collaborative filtering approach for recommender systems based on multi level and bidirectional trust data Şahinkaya, Ferhat; Alpaslan, Ferda Nur; Department of Computer Engineering (2010) 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 impli...
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 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...
Automatic web service composition with ai planning Kuzu, Mehmet; Çiçekli, Fehime Nihan; Department of Computer Engineering (2009) In this thesis, some novel ideas are presented for solving automated web service composition problem. Some possible real world problems such as partial observability of environment, nondeterministic effects of web services, service execution failures are solved through some mechanisms. In addition to automated web service composition, automated web service invocation task is handled in this thesis by using reflection mechanism. The proposed approach is based on AI planning. Web service composition problem i...
A 3D topological tracking system for augmented reality Ercan, Münir; Can, Tolga; Department of Computer Engineering (2010) Augmented Reality (AR) has become a popular area in computer Science where research studies and technological innovations are extensive. Research in AR first began in the early 1990s and thenceforth, a number of di erent tracking algorithms and methods have been developed. Tracking systems have a critical importance for AR and marker based vision tracking systems became the mostly used tracking systems due to their low cost and ease of use. Basically, marker systems consist of special patterns that are plac...

Citation Formats

H. Karaman, “A content based movie recommendation system empowered by collaborative missing data prediction,” M.S. - Master of Science, Middle East Technical University, 2010.