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 web service based trust and reputation system for transitory collaboration formation in supply chains
Taşyurt, İbrahim; Doğaç, Asuman; Department of Computer Engineering (2009)
Today, advancements in the information technologies increased the significance of electronic business in the world. Besides the numerous advantages provided by these advancements, competition has also increased for the enterprises. In this competitive environment, companies have to access information faster and response to the changes quickly. In a supply chain, it is a highly possible that one of the partners may defect in providing its services. When these exceptional cases occur, the pending parties have...
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...
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...
Ontology population using human computation
Evirgen, Gencay Kemal; Alpaslan, Ferda Nur; Department of Computer Engineering (2010)
In recent years, many researchers have developed new techniques on ontology population. However, these methods cannot overcome the semantic gap between humans and the extracted ontologies. Words-Around is a web application that forms a user-friendly environment which channels the vast Internet population to provide data towards solving ontology population problem that no known efficient computer algorithms can yet solve. This application’s fundamental data structure is a list of words that people naturally ...
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.