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Existing session\u2010based recommendation systems usually model a session into a sequence and extract sequence features through recurrent neural network. Although the performance is greatly improved, these procedures ignore the relationships between items that contain rich information. In order to obtain rich items embeddings, we propose a novel Recommendation Model based on Multi\u2010channel Convolutional Neural Network for session\u2010based recommendation, RMMCNN for brevity. Specifically, we capture items\u2032 internal features from three dimensions through multi\u2010channel convolutional neural network firstly. Next, we merge the internal features with external features obtained by a GRU unit. Then, both internal features and external features are merged by an attention mechanism together as the input of the transformation function. Finally, the probability distribution is taken as the output after the softmax function. Experiments on various datasets show that our method\u2032s precision and recommendation performance are better than those of other state\u2010of\u2010the\u2010art approaches.<\/jats:p>","DOI":"10.1155\/2021\/6661901","type":"journal-article","created":{"date-parts":[[2021,4,5]],"date-time":"2021-04-05T18:20:06Z","timestamp":1617646806000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi\u2010channel Convolutional Neural Network Feature Extraction for Session Based Recommendation"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6566-9464","authenticated-orcid":false,"given":"Zhenyan","family":"Ji","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3849-4283","authenticated-orcid":false,"given":"Mengdan","family":"Wu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0912-454X","authenticated-orcid":false,"given":"Yumin","family":"Feng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2666-7191","authenticated-orcid":false,"given":"Jos\u00e9 Enrique","family":"Armend\u00e1riz \u00cd\u00f1igo","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,4,5]]},"reference":[{"key":"e_1_2_9_1_2","first-page":"126","article-title":"A hybrid recommendation model based on fusion of multi-source heterogeneous data","volume":"42","author":"Ji Z. 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