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Fulltext Word Sequential Using Deep LSTM and Matrix Factorization to Handle Rating Sparse Data for E-Commerce Recommender System

Hanafi, Mohd Aboobaider B

Comput Intell Neurosci, 2021;2021:8751173.
PMID: 34917141 DOI: 10.1155/2021/8751173

Recommender systems are essential engines to deliver product recommendations for e-commerce businesses. Successful adoption of recommender systems could significantly influence the growth of marketing targets. Collaborative filtering is a type of recommender system model that uses customers' 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-IDF and LDA to interpret product review documents. However, traditional models such as LDA and TF-IDF 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-commerce recommender systems with sparse data issues.
Exploring the contributing factors to workarounds to the hospital information system in Malaysian hospitals

Salahuddin L, Ismail Z, Abd Ghani MK, Mohd Aboobaider B, Hasan Basari AS

J Eval Clin Pract, 2020 Oct;26(5):1416-1424.
PMID: 31863517 DOI: 10.1111/jep.13326

OBJECTIVES: The objective of this study was to identify the factors influencing workarounds to the Hospital Information System (HIS) in Malaysian government hospitals.
METHODS: Semi-structured interviews were conducted among 31 medical doctors in three Malaysian government hospitals on the implementation of the Total Hospital Information System (THIS) between March and May 2015. A thematic qualitative analysis was performed on the resultant data to deduce the relevant themes.
RESULTS: Five themes emerged as the factors influencing workarounds to the HIS: (a) typing skills, (b) system usability, (c) computer resources, (d) workload, and (e) time.
CONCLUSIONS: This study provided the key factors as to why doctors were involved in workarounds during the implementation of the HIS. It is important to understand these factors in order to help mitigate work practices that can pose a threat to patient safety.

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