https://j.ideasspread.org/index.php/ijas <p>International Journal of Applied Science (IJAS) is an international, double-blind peer-reviewed, open-access journal, published by IDEAS SPREAD INC. It publishes original research, applied, and educational articles in all areas of applied science. It provides an academic platform for professionals and researchers to contribute innovative work in the field.<br>Authors are encouraged to submit complete, unpublished, original works that are not under review in any other journals. The scopes of the journal include, but are not limited to, the following fields: Agriculture, Biological Engineering and Application, Applied Mathematics and Statistics, Applied Physics and Engineering, Applied Chemistry and Materials Sciences, Civil Engineering and Architecture, Computer and Information Sciences and Application, Energy, Environmental Science and Engineering, Mechanics, Metrology, Military Science, Space Science, Sports Science, Ergonomics, Health Sciences, Fisheries science, Food Science, Forestry and all the fields related to applied science.<br>The journal is published in both print and online versions. The online version is free access and download.</p> IDEAS SPREAD INC en-US International Journal of Applied Science 2576-7240 <p>Copyright for this article is retained by the author(s), with first publication rights granted to the journal.<br>This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).</p> https://j.ideasspread.org/index.php/ijas/article/view/1405 <p>In the context of recommendation scenarios, the utilization of data purification and data augmentation methodologies has demonstrated their efficiency in enhancing the quality of representations. Nevertheless, within an open environment, the constrained nature of interaction data and the diverse range of interaction intentions present formidable challenges, giving rise to insufficient generalization ability and generalization bias in these methodologies. To address this issue, in this paper we introduce Exploitation &amp; Exploration: A Unified Framework for Data Purification &amp; Augmentation in Recommendation Systems, which not only ensures the precise purification of current data but also delves into potential noisy data for further exploration. To be specific, based on the traditional collaborative filtering method calculating user-item correlation, we first implement an efficient multi-head SENet block to remove potential noise from the interaction data. After this, we deploy a diffusion module to remove the added adversarial noise based on its ability to denoise all kinds of noise. And finally we use mutual-learning method to coordinate two parts' learning. We conducted experiments on three publicly available datasets, evaluating our model against current state-of-the-art algorithms in recommendation robustness tasks. The experimental results validate the effectiveness of our model.</p> Cheng Junwei ##submission.copyrightStatement## http://creativecommons.org/licenses/by/4.0 2025-01-14 2025-01-14 8 1 p1 p1 10.30560/ijas.v8n1p1