Web Recommendation System with Image Retrieval
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
The amount of information on the Internet has dramatically increased during recent years such that increment causes a problem so called “information overload”, which can only be partially solved by search engines. Although there is a considerable literature on search engine focusing on information overload, it has still not been completely overcome to date due to concerns about commercial interests, individual difference and objective process. Addressing those concerns, recommendation systems, which are information-filtering systems that can recommend information without explicit participation of the user, was designed to aim those problems.
The recommendation system collects the interests of users to create an independent profile for each user. Moreover, it compares the user profile to some reference characteristics, and the system recommends information of potential interest to the user. They redeem from shortcomings of search engines, since recommendation systems focus on the specific characteristics of each user.
Unlike previous literature that focuses on text, this thesis presents an improved recommendation system, which considers the information stored in images. Based on methods of user modeling and user profile expression are analyzed, A new design for user profiles joint with methods for content based image retrieval are presented. In this design, the new user profile contains information from images on the web pages to increase the accuracy of the recommendation. Furthermore, algorithms for updating the user model according to user feedback are also introduced such that the user model can reflect the interest modification of users. Using a real-word deployment, the thesis shows the new system achieves better accuracy comparing to existed text-only methods given small amount of data.
Finally, the thesis argues about the feature selecting in Image analysis is the bottleneck for recommendation system. It appears very hard to significant improve existed system without new features and semantic analysis.
Place, publisher, year, edition, pages
IT, 11 030
IdentifiersURN: urn:nbn:se:uu:diva-156430OAI: oai:DiVA.org:uu-156430DiVA: diva2:431579
Master Programme in Computer Science
Christoff, IvanJansson, Anders