Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource TD?DNN: A Time Decay?Based Deep Neural Network for Recommendation System Subject The topic of the resource Collaborative Filtering; Deep Neural Network; Matrix Factorization; noisy ratings; recommendation system; time decay functions Description An account of the resource In recent years, commercial platforms have embraced recommendation algorithms to provide customers with personalized recommendations. Collaborative Filtering is the most widely used technique of recommendation systems, whose accuracy is primarily reliant on the computed similarity by a similarity measure. Data sparsity is one problem that affects the performance of the similarity measures. In addition, most recommendation algorithms do not remove noisy data from datasets while recommending the items, reducing the accuracy of the recommendation. Further-more, existing recommendation algorithms only consider historical ratings when recommending the items to users, but users tastes may change over time. To address these issues, this research presents a Deep Neural Network based on Time Decay (TD?DNN). In the data preprocessing phase of the model, noisy ratings are detected from the dataset and corrected using the Matrix Factorization approach. A power decay function is applied to the preprocessed input to provide more weight-age to the recent ratings. This non?noisy weighted matrix is fed into the Deep Learning model, con-sisting of an input layer, a Multi?Layer Perceptron, and an output layer to generate predicted rat-ings. The models performance is tested on three benchmark datasets, and experimental results con-firm that TD?DNN outperforms other existing approaches. 2022 by the authors. Li-censee MDPI, Basel, Switzerland. Creator An entity primarily responsible for making the resource Jain G.; Mahara T.; Sharma S.C.; Agarwal S.; Kim H. Source A related resource from which the described resource is derived Applied Sciences (Switzerland), Vol-12, No. 13 Publisher An entity responsible for making the resource available MDPI Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.3390/app12136398" target="_blank" rel="noreferrer noopener">https://doi.org/10.3390/app12136398</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132983140&amp;doi=10.3390%2Fapp12136398&amp;partnerID=40&amp;md5=db330081d3f470cf8ade841ecf8e6576" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132983140&amp;doi=10.3390%2fapp12136398&amp;partnerID=40&amp;md5=db330081d3f470cf8ade841ecf8e6576</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 20763417 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jain G., Electronics and Computer Discipline, Indian Institute of Technology, Roorkee, 247667, India; Mahara T., Institute of Management, Christ University, Bengaluru, 560029, India; Sharma S.C., Electronics and Computer Discipline, Indian Institute of Technology, Roorkee, 247667, India; Agarwal S., Amity School of Engineering &amp; Technology, Amity University Uttar Pradesh, Noida, 201313, India; Kim H., School of Computer Science, Kyungil University, Kyungbuk, Gyeongsan, 38428, South Korea