A Content Boosted Collaborative Filtering Approach for Movie Recommendation Based on Local Global Similarity and Missing Data Prediction

Özbal, Gözde
Kahraman, Hilal
Alpaslan, Ferda Nur
Many recommender systems lack in accuracy when the data used throughout the recommendation process is sparse. Our study addresses this limitation by means of a content boosted collaborative filtering approach applied to the task of movie recommendation. We combine two different approaches previously proved to be successful individually and improve over them by processing the content information of movies, as confirmed by our empirical evaluation results.


A Content-Boosted Collaborative Filtering Approach for Movie Recommendation Based on Local and Global Similarity and Missing Data Prediction
Özbal, Gozde; Karaman, Hilal; Alpaslan, Ferda Nur (Oxford University Press (OUP), 2011-09-01)
Most traditional recommender systems lack accuracy in the case where data used in the recommendation process is sparse. This study addresses the sparsity problem and aims to get rid of it by means of a content-boosted collaborative filtering approach applied to a web-based movie recommendation system. The main motivation is to investigate whether further success can be obtained by combining 'local and global user similarity' and 'effective missing data prediction' approaches, which were previously introduce...
A Framework to Detect Disguised Missing Data
Belen, Rahime; Taşkaya Temizel, Tuğba (2011-01-01)
Many manually populated very large databases suffer from data quality problems such as missing, inaccurate data and duplicate entries. A recently recognized data quality problem is that of disguised missing data which arises when an explicit code for missing data such as NA (Not Available) is not provided and a legitimate data value is used instead. Presence of these values may affect the outcome of data mining tasks severely such that association mining algorithms or clustering techniques may result in bia...
An Embedded spatial statistics toolbox (R techniques) in open source GİS software (uDig)
Çavur, Mahmut; Düzgün, H. Şebnem; Department of Geodetic and Geographical Information Technologies (2016)
It is widely considered that geographic information systems (GIS) should include more spatial data analysis (SDA) techniques. The issues of which techniques should be included and how statistical analysis can be integrated with GIS are still widely debated. However, the typical software does not include all geospatial techniques. In this respect, this thesis focuses on the means to develop a framework which implements R spatial statistical techniques in the uDig GIS so that GIS and spatial statistical analy...
Extending singular value decomposition based recommendation systems with tags and ontology
Turgut, Yakup; Toroslu, İsmail Hakkı; Department of Computer Engineering (2014)
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 a...
A Hypergraph based framework for representing aggregated user profiles, employing it for a recommender system and personalized search through a hypernetwork method
Tarakçı, Hilal; Manguoğlu, Murat; Çiçekli, Fehime Nihan; Department of Computer Engineering (2017)
In this thesis, we present a hypergraph based user modeling framework to aggregate partial profiles of the individual and obtain a complete, semantically enriched, multi-domain user model. We also show that the constructed user model can be used to support different personalization services including recommendation. We evaluated the user model against datasets consisting of user's social accounts including Facebook, Twitter, LinkedIn and Stack Overflow. The evaluation results confirmed that the proposed use...
Citation Formats
G. Özbal, H. Kahraman, and F. N. Alpaslan, “A Content Boosted Collaborative Filtering Approach for Movie Recommendation Based on Local Global Similarity and Missing Data Prediction,” presented at the 25th International Symposium on Computer and Information Sciences, ISCIS 2010, (22 - 24 Eylül 2010), London; United Kingdom, 2010, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/73098.