{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T10:56:10Z","timestamp":1761648970468,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,4]],"date-time":"2022-01-04T00:00:00Z","timestamp":1641254400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hanoi University of Science and Technology","award":["T2020-PC-024"],"award-info":[{"award-number":["T2020-PC-024"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Recommendation systems based on convolutional neural network (CNN) have attracted great attention due to their effectiveness in processing unstructured data such as images or audio. However, a huge amount of raw data produced by data crawling and digital transformation is structured, which makes it difficult to utilize the advantages of CNN. This paper introduces a novel autoencoder, named Half Convolutional Autoencoder, which adopts convolutional layers to discover the high-order correlation between structured features in the form of Tag Genome, the side information associated with each movie in the MovieLens 20 M dataset, in order to generate a robust feature vector. Subsequently, these new movie representations, along with the introduction of users\u2019 characteristics generated via Tag Genome and their past transactions, are applied into well-known matrix factorization models to resolve the initialization problem and enhance the predicting results. This method not only outperforms traditional matrix factorization techniques by at least 5.35% in terms of accuracy but also stabilizes the training process and guarantees faster convergence.<\/jats:p>","DOI":"10.3390\/fi14010020","type":"journal-article","created":{"date-parts":[[2022,1,6]],"date-time":"2022-01-06T03:41:32Z","timestamp":1641440492000},"page":"20","update-policy":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Utilizing Half Convolutional Autoencoder to Generate User and Item Vectors for Initialization in Matrix Factorization"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-2442-6263","authenticated-orcid":false,"given":"Tan Nghia","family":"Duong","sequence":"first","affiliation":[{"name":"School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi 100000, Vietnam"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0002-6166-4826","authenticated-orcid":false,"given":"Nguyen Nam","family":"Doan","sequence":"additional","affiliation":[{"name":"School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi 100000, Vietnam"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0001-6509-1482","authenticated-orcid":false,"given":"Truong Giang","family":"Do","sequence":"additional","affiliation":[{"name":"School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi 100000, Vietnam"}]},{"given":"Manh Hoang","family":"Tran","sequence":"additional","affiliation":[{"name":"School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi 100000, Vietnam"}]},{"given":"Duc Minh","family":"Nguyen","sequence":"additional","affiliation":[{"name":"School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi 100000, Vietnam"}]},{"given":"Quang Hieu","family":"Dang","sequence":"additional","affiliation":[{"name":"School of Electronics and Telecommunications, Hanoi University of Science and Technology, Hanoi 100000, Vietnam"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1109\/TKDE.2005.99","article-title":"Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions","volume":"17","author":"Adomavicius","year":"2005","journal-title":"IEEE Trans. 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