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Author: | Nguyen, Hoang Anh |
Title: | Selection of Trust Mechanism in Recommender Systems |
Publication type: | Master's thesis |
Publication year: | 2009 |
Pages: | (10+) 56 Language: eng |
Department/School: | Tietotekniikan laitos |
Main subject: | Tietokoneverkot (T-110) |
Supervisor: | Tarkoma, Sasu ; Laud, Peeter |
Instructor: | Tavakolifard, Mozhgan |
OEVS: | Electronic archive copy is available via Aalto Thesis Database.
Instructions Reading digital theses in the closed network of the Aalto University Harald Herlin Learning CentreIn the closed network of Learning Centre you can read digital and digitized theses not available in the open network. The Learning Centre contact details and opening hours: https://learningcentre.aalto.fi/en/harald-herlin-learning-centre/ You can read theses on the Learning Centre customer computers, which are available on all floors.
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Location: | P1 Ark Aalto | Archive |
Keywords: | recommender systems coliaborative filtering trust reputation LSI/SVD EigenTrust trust inference |
Abstract (eng): | Recommender Systems (RS) have emerged as an important response to the so-called information overload problem. They enable users to share their opinions and benefit from each other's. Recommender algorithms are best known for their use on e-commerce Web sites to help users find products they would appreciate from huge catalogues. The products may vary from books (e.g., Amazon.com), movies (e.g., Netflix), photographs (e.g. Flickr.com), or web sites (e.g., del.icio.us)... The traditional collaborative filtering techniques are able to provide high-quality recommendations by leveraging the preferences of similar users. However, recent researches have suggested that the traditional focus on user similarity may not be sufficient. Additional factors, especially trust may have an important role when it comes to making recommendations. In this thesis, we study the different algorithms and the use of trust to improve the performance of collaborative filtering recommender systems. Our evaluation on MovieLens dataset shows that the dimensionality reduction method that uses LSI/SVD technique helps in providing better quality of recommendations. Trust also has positive impact on overall prediction error rates, however, giobal trust metrics may not he appropriate for trust-aware recommender systems due to their non-personalized nature. |
ED: | 2009-09-07 |
INSSI record number: 38297
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