Computer Engineering / Bilgisayar Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/10
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Conference Object Citation - WoS: 1Artist Recommendation Based on Association Rule Mining and Community Detection(SCITEPRESS, 2021) Çiftçi, Okan; Tenekeci, Samet; Ülgentürk, CerenRecent advances in the web have greatly increased the accessibility of music streaming platforms and the amount of consumable audio content. This has made automated recommendation systems a necessity for listeners and streaming platforms alike. Therefore, a wide variety of predictive models have been designed to identify related artists and music collections. In this paper, we proposed a graph-based approach that utilizes association rules extracted from Spotify playlists. We constructed several artist networks and identified related artist clusters using Louvain and Label Propagation community detection algorithms. We analyzed internal and external cluster agreements based on different validation criteria. As a result, we achieved up to 99.38% internal and 90.53% external agreements between our models and Spotify's related artist lists. These results show that integrating association rule mining concepts with graph databases can be a novel and effective way to design an artist recommendation system.Conference Object Citation - Scopus: 4Itemset Hiding Under Multiple Sensitive Support Thresholds(SCITEPRESS, 2017) Öztürk, Ahmet Cumhur; Ergenç Bostanoğlu, BelginItemset mining is the challenging step of association rule mining that aims to extract patterns among items from transactional databases. In the case of applying itemset mining on the shared data of organizations, each party needs to hide its sensitive knowledge before extracting global knowledge for mutual benefit. Ensuring the privacy of the sensitive itemsets is not the only challenge in the itemset hiding process, also the distortion given to the non-sensitive knowledge and data should be kept at minimum. Most of the previous works related to itemset hiding allow database owner to assign unique sensitive threshold for each sensitive itemset however itemsets may have different count and utility. In this paper we propose a new heuristic based hiding algorithm which 1) allows database owner to assign multiple sensitive threshold values for sensitive itemsets, 2) hides all user defined sensitive itemsets, 3) uses heuristics that minimizes loss of information and distortion on the shared database. In order to speed up hiding steps we represent the database as Pseudo Graph and perform scan operations on this data structure rather than the actual database. Performance evaluation of our algorithm Pseudo Graph Based Sanitization (PGBS) is conducted on 4 real databases. Distortion given to the nonsensitive itemsets (information loss), distortion given to the shared data (distance) and execution time in comparison to three similar algorithms is measured. Experimental results show that PGBS is competitive in terms of execution time and distortion and achieves reasonable performance in terms of information loss amongst the other algorithms. © 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.