A novel collaborative filtering algorithm by bit mining frequent itemsets
- Published
- Accepted
- Subject Areas
- Algorithms and Analysis of Algorithms, Data Mining and Machine Learning
- Keywords
- collaborative filtering, mining frequent itemsets, bit mining, bit matching
- Copyright
- © 2018 Nguyen et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2018. A novel collaborative filtering algorithm by bit mining frequent itemsets. PeerJ Preprints 6:e26444v1 https://doi.org/10.7287/peerj.preprints.26444v1
Abstract
Collaborative filtering (CF) is a popular technique in recommendation study. Concretely, items which are recommended to user are determined by surveying her/his communities. There are two main CF approaches, which are memory-based and model-based. I propose a new CF model-based algorithm by mining frequent itemsets from rating database. Hence items which belong to frequent itemsets are recommended to user. My CF algorithm gives immediate response because the mining task is performed at offline process-mode. I also propose another so-called Roller algorithm for improving the process of mining frequent itemsets. Roller algorithm is implemented by heuristic assumption “The larger the support of an item is, the higher it’s likely that this item will occur in some frequent itemset”. It models upon doing white-wash task, which rolls a roller on a wall in such a way that is capable of picking frequent itemsets. Moreover I provide enhanced techniques such as bit representation, bit matching and bit mining in order to speed up recommendation process. These techniques take advantages of bitwise operations (AND, NOT) so as to reduce storage space and make algorithms run faster.
Author Comment
The article was submitted and accepted by International Journal of Applied Mathematics and Machine Learning ( ISSN: 2694-2258), Scientific Advances Publishers.