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

KAYA, hamza
Alpaslan, Ferda Nur
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.


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...
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...
Improving the scalability of ILP-based multi-relational concept discovery system through parallelization
Mutlu, Ayşe Ceyda; Karagöz, Pınar; Kavurucu, Yusuf (2012-03-01)
Due to the increase in the amount of relational data that is being collected and the limitations of propositional problem definition in relational domains, multi-relational data mining has arisen to be able to extract patterns from relational data. In order to cope with intractably large search space and still to be able to generate high-quality patterns. ILP-based multi-relational data mining and concept discovery systems employ several search strategies and pattern limitations. Another direction to cope w...
Interacting multiple model probabilistic data association filter using random matrices for extended target tracking
Özpak, Ezgi; Orguner, Umut; Department of Electrical and Electronics Engineering (2018)
In this thesis, an Interacting Multiple Model – Probabilistic Data Association (IMM-PDA) filter for tracking extended targets using random matrices is proposed. Unlike the extended target trackers in the literature which use multiple alternative partitionings/clusterings of the set of measurements, the algorithm proposed here considers a single partitioning/clustering of the measurement data which makes it suitable for applications with low computational resources. When the IMM-PDA filter uses clustered mea...
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: