A hybrid recommendation system capturing the effect of time and demographic data

Oktay, Fulya
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. Recommendation systems are one of the ways for achieving this purpose. The idea is to design a system that understands the information user wants to obtain from user actions, and to find the information similar to that. Several studies have been done in this field in order to develop a recommendation system which is capable of recommending movies, books, web sites and similar items like that. All of them are based on two main principles, which are collaborative filtering and content based recommendations. Within this thesis work, a recommendation system approach which combines both content based (CB) and collaborative filtering (CF) approaches by capturing the effect of time like purchase time or release time. In addition to this temporal behavior, the influence of demographic information of user on purchasing habits is also examined this system which is called “TDRS”. .


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 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 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 distributed graph mining framework based on mapreduce
Alkan, Sertan; Can, Tolga; Department of Computer Engineering (2010)
The frequent patterns hidden in a graph can reveal crucial information about the network the graph represents. Existing techniques to mine the frequent subgraphs in a graph database generally rely on the premise that the data can be fit into main memory of the device that the computation takes place. Even though there are some algorithms that are designed using highly optimized methods to some extent, many lack the solution to the problem of scalability. In this thesis work, our aim is to find and enumerate...
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...
Citation Formats
F. Oktay, “A hybrid recommendation system capturing the effect of time and demographic data,” M.S. - Master of Science, Middle East Technical University, 2010.