Extending singular value decomposition based recommendation systems with tags and ontology

Download

index.pdf

Date

2014

Author

Turgut, Yakup

Metadata

Show full item record

Item Usage Stats

156
views

61
downloads

Due to increase of the volume of data related to user ratings on items, in recent years, recommendation systems became very popular, which uses this data in order to rec- ommend items to users in many different domains. Singular Value Decomposition is one of the most widely studied collaborative ﬁltering recommendation techniques. In some applications users are also allowed to enter (sometimes free) tags in addition to their ratings on items. Adding tags in addition to regular users’ ratings on items have also been studied from different perspectives. In this work, we embedded tags entered by users into SVD technique in a simple but novel way. We also present methods that incorporate ontology to determine relationships between tags into consideration while dealing with movie recommender systems. We have applied our approach on movie recommendation system.

Subject Keywords

Information filtering systems., Information storage and retrieval systems, Recommender systems (Information filtering).

URI

http://etd.lib.metu.edu.tr/upload/12617442/index.pdf
https://hdl.handle.net/11511/23643

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Probabilistic matrix factorization based collaborative filtering with implicit trust derived from review ratings information Ercan, Eda; Taşkaya Temizel, Tuğba; Department of Information Systems (2010) Recommender systems aim to suggest relevant items that are likely to be of interest to the users using a variety of information resources such as user proﬁles, trust information and users past predictions. However, typical recommender systems suffer from poor scalability, generating incomprehensible and not useful recommendations and data sparsity problem. In this work, we have proposed a probabilistic matrix factorization based local trust boosted recommendation system which handles data sparsity, scalabil...
Efficient rating estimation by using similarity in multi-dimensional check-in data Uçar, Behlül; Karagöz, Pınar; Toroslu, İ. Hakkı; Department of Computer Engineering (2014) The usage coverage of location based social networks have boomed in the last years as well as the amount of data produced in them. This data is suitable for processing in order to make prediction. One of the requirements of this process is that the method used should be suitable for very big data sets. We propose a graph-based similarity calculation method in location-based social networks which improves the rating prediction performance of Singular Value Decomposition based collaborative filtering systems....
Evolution of web search results within years Altıngövde, İsmail Sengör; Ulusoy, Özgür (2011-01-01) We provide a first large-scale analysis of the evolution of query results obtained from a real search engine at two distant points in time, namely, in 2007 and 2010, for a set of 630,000 real queries.
A Multi-objective recommendation system Özsoy, Makbule Gülçin; Polat, Faruk; Alhajj, Reda; Department of Computer Engineering (2016) Recommendation systems suggest items to the user by estimating their preferences. Most of the recommendation systems are based on single criterion, such that they evaluate items based on overall rating. In order to give more accurate recommendations, a recommendation system can take advantage of considering multiple criteria. Beside combining multiple criteria from a single data source, multiple criteria from multiple data sources can be combined. Recommendation methods can also be used in various applicati...
Characterizing web search queries that match very few or no results Altıngövde, İsmail Sengör; Cambazoglu, Berkant Barla; Ozcan, Rifat; Sarigil, Erdem; Ulusoy, Özgür (2012-12-19) Despite the continuous efforts to improve the web search quality, a non-negligible fraction of user queries end up with very few or even no matching results in leading web search engines. In this work, we provide a detailed characterization of such queries based on an analysis of a real-life query log. Our experimental setup allows us to characterize the queries with few/no results and compare the mechanisms employed by the major search engines in handling them.

Citation Formats

Y. Turgut, “Extending singular value decomposition based recommendation systems with tags and ontology,” M.S. - Master of Science, Middle East Technical University, 2014.