Using Social Networks to Solve Data Sparsity Problem in One-Class Collaborative Filtering

Date

2010-04-14

Author

KAYA, hamza
Alpaslan, Ferda Nur

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

143
views

0
downloads

One-Class Collaborative Filtering (OCCF) problems are more problematic than traditional collaborative filtering problems, since OCCF datasets lack counter-examples. Social networks can be used to remedy dataset issues faced by OCCF applications. In this work, we compare social networks belong to specific domains and the ones belong to more generic domains in terms of their usability in OCCF problems. Our experiments show that social networks that belong to a specific domain may better be appropriate for use in OCCF application.

Subject Keywords

Data sparsity, Collaborative filtering, k-nn

URI

https://hdl.handle.net/11511/41708

DOI

https://doi.org/10.1109/itng.2010.26

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Using social graphs in one-class collaborative filtering problem Kaya, Hamza; Alpaslan, Ferda Nur; Department of Computer Engineering (2009) One-class collaborative filtering is a special type of collaborative filtering methods that aims to deal with datasets that lack counter-examples. In this work, we introduced social networks as a new data source to the one-class collaborative filtering (OCCF) methods and sought ways to benefit from them when dealing with OCCF problems. We divided our research into two parts. In the first part, we proposed different weighting schemes based on social graphs for some well known OCCF algorithms. One of the weig...
Cluster searching strategies for collaborative recommendation systems Altıngövde, İsmail Sengör; Ulusoy, Ozgur (2013-05-01) In-memory nearest neighbor computation is a typical collaborative filtering approach for high recommendation accuracy. However, this approach is not scalable given the huge number of customers and items in typical commercial applications. Cluster-based collaborative filtering techniques can be a remedy for the efficiency problem, but they usually provide relatively lower accuracy figures, since they may become over-generalized and produce less-personalized recommendations. Our research explores an individua...
Aggregation in confidence-based concept discovery for multi-relational data mining Kavurucu, Yusuf; Senkul, Pinar; Toroslu, İsmail Hakkı (null; 2008-12-01) Multi-relational data mining has become popular due to the limitations of propositional problem definition in structured domains and the tendency of storing data in relational databases. Several relational knowledge discovery systems have been developed employing various search strategies, heuristics, language pattern limitations and hypothesis evaluation criteria, in order to cope with intractably large search space and to be able to generate high-quality patterns. In this work, we describe a method for ge...
MODELLING OF KERNEL MACHINES BY INFINITE AND SEMI-INFINITE PROGRAMMING Ozogur-Akyuz, S.; Weber, Gerhard Wilhelm (2009-06-03) In Machine Learning (ML) algorithms, one of the crucial issues is the representation of the data. As the data become heterogeneous and large-scale, single kernel methods become insufficient to classify nonlinear data. The finite combinations of kernels are limited up to a finite choice. In order to overcome this discrepancy, we propose a novel method of "infinite" kernel combinations for learning problems with the help of infinite and semi-infinite programming regarding all elements in kernel space. Looking...
An ilp-based concept discovery system for multi-relational data mining Kavurucu, Yusuf; Karagöz, Pınar; Department of Computer Engineering (2009) Multi Relational Data Mining has become popular due to the limitations of propositional problem definition in structured domains and the tendency of storing data in relational databases. However, as patterns involve multiple relations, the search space of possible hypothesis becomes intractably complex. In order to cope with this problem, several relational knowledge discovery systems have been developed employing various search strategies, heuristics and language pattern limitations. In this thesis, Induct...

Citation Formats

h. KAYA and F. N. Alpaslan, “Using Social Networks to Solve Data Sparsity Problem in One-Class Collaborative Filtering,” 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/41708.