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Collaborative filtering is a type of recommender system model that uses customers\u2019 activities in the past, such as ratings. Unfortunately, the number of ratings collected from customers is sparse, amounting to less than 4%. The latent factor model is a kind of collaborative filtering that involves matrix factorization to generate rating predictions. However, using only matrix factorization would result in an inaccurate recommendation. Several models include product review documents to increase the effectiveness of their rating prediction. Most of them use methods such as TF\u2010IDF and LDA to interpret product review documents. However, traditional models such as LDA and TF\u2010IDF face some shortcomings, in that they show a less contextual understanding of the document. This research integrated matrix factorization and novel models to interpret and understand product review documents using LSTM and word embedding. According to the experiment report, this model significantly outperformed the traditional latent factor model by more than 16% on an average and achieved 1% on an average based on RMSE evaluation metrics, compared to the previous best performance. Contextual insight of the product review document is an important aspect to improve performance in a sparse rating matrix. In the future work, generating contextual insight using bidirectional word sequential is required to increase the performance of e\u2010commerce recommender systems with sparse data issues.<\/jats:p>","DOI":"10.1155\/2021\/8751173","type":"journal-article","created":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T06:11:29Z","timestamp":1638943889000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Word Sequential Using Deep LSTM and Matrix Factorization to Handle Rating Sparse Data for E\u2010Commerce Recommender System"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8592-7678","authenticated-orcid":false,"family":"Hanafi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8976-7416","authenticated-orcid":false,"given":"Burhanuddin","family":"Mohd Aboobaider","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,12,7]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-29659-3"},{"key":"e_1_2_8_2_2","article-title":"The netflix recommender system: algorithms, business value, and innovation","volume":"6","author":"Uribe C. 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