Enhancing accuracy of hybrid recommender systems through adapting the domain trends

Download

index.pdf

Date

2010

Author

Aksel, Fatih

Metadata

Show full item record

Item Usage Stats

102
views

31
downloads

Traditional hybrid recommender systems typically follow a manually created fixed prediction strategy in their decision making process. Experts usually design these static strategies as fixed combinations of different techniques. However, people's tastes and desires are temporary and they gradually evolve. Moreover, each domain has unique characteristics, trends and unique user interests. Recent research has mostly focused on static hybridization schemes which do not change at runtime. In this thesis work, we describe an adaptive hybrid recommender system, called AdaRec that modifies its attached prediction strategy at runtime according to the performance of prediction techniques (user feedbacks). Our approach to this problem is to use adaptive prediction strategies. Experiment results with datasets show that our system outperforms naive hybrid recommender.

Subject Keywords

Computer software.

URI

http://etd.lib.metu.edu.tr/upload/12612330/index.pdf
https://hdl.handle.net/11511/19840

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Improving search result clustering by integrating semantic information from Wikipedia Çallı, Çağatay; Üçoluk, Göktürk; Şehitoğlu, Onur Tolga; Department of Computer Engineering (2010) Suffix Tree Clustering (STC) is a search result clustering (SRC) algorithm focused on generating overlapping clusters with meaningful labels in linear time. It showed the feasibility of SRC but in time, subsequent studies introduced description-first algorithms that generate better labels and achieve higher precision. Still, STC remained as the fastest SRC algorithm and there appeared studies concerned with different problems of STC. In this thesis, semantic relations between cluster labels and documents ar...
Modelling and predicting binding affinity of PCP-like compounds using machine learning methods Erdaş, Özlem; Alpaslan, Ferda Nur; Department of Computer Engineering (2007) Machine learning methods have been promising tools in science and engineering fields. The use of these methods in chemistry and drug design has advanced after 1990s. In this study, molecular electrostatic potential (MEP) surfaces of PCP-like compounds are modelled and visualized in order to extract features which will be used in predicting binding affinity. In modelling, Cartesian coordinates of MEP surface points are mapped onto a spherical self-organizing map. Resulting maps are visualized by using values...
A systematic study of probabilistic aggregation strategies in swarm robotic systems Soysal, Onur; Şahin, Erol; Department of Computer Engineering (2005) In this study, a systematic analysis of probabilistic aggregation strategies in swarm robotic systems is presented. A generic aggregation behavior is proposed as a combination of four basic behaviors: obstacle avoidance, approach, repel, and wait. The latter three basic behaviors are combined using a three-state finite state machine with two probabilistic transitions among them. Two different metrics were used to compare performance of strategies. Through systematic experiments, how the aggregation performa...
Merging multi-view feature models Atılgan Aydın, Elçin; Oğuztüzün, Mehmet Halit S.; Doğru, Ali Hikmet; Department of Computer Engineering (2011) Feature models are used for variability management in software product lines. Instead of developing a single feature model, merging small models can be an effective solution to obtain a unified view. Since each stakeholder views the product family from a different perspective, conflicts may occur during merging. In this research, merging of feature models arising from different viewpoints is considered. A normative procedure is proposed to merge feature models by applying local rules. This procedure can mer...
Apply Quantitative Management Now TARHAN, AYÇA; Demirörs, Onur (Institute of Electrical and Electronics Engineers (IEEE), 2012-05-01) The Assessment Approach for Quantitative Process Management (A2QPM) helps identify software process measures for quantitative analysis even when organizations lack formal systems for process measurement. A2QPM is the first approach to quantitative management that offers software organizations a well-defined, detailed guideline for assessing their software processes and applying beneficial quantitative techniques to improve them. All the A2QPM applications we've described resulted in quantitative analysis im...

Citation Formats

F. Aksel, “Enhancing accuracy of hybrid recommender systems through adapting the domain trends,” M.S. - Master of Science, Middle East Technical University, 2010.