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

Abstract

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.

Enhance Rating Prediction for E-commerce Recommender System Using Hybridization of SDAE, Attention Mechanism and Probabilistic Matrix Factorization

“…This experiment scenario aims to observe the effectiveness of the model with attention mechanism to capture product document context from review, where document context with W expect to increase share weigh of product document representation. Finally, according to experiment report on Table 4, the propose model outperform over previous work that involve CNN [15] and LSTM model in capturing of document context [18]. While, according to user information representation in PHD [19] DDL-PMF [20], they used similar algorithm based on SDAE.…”

“…Similar with previous work, Hanafi proposed a hybrid model using PMF and deep learning approach based on LSTM [16][17][18]. The objective of this study to advance product document understanding using LSTM.…”

Enhance Intrusion Detection (IDS) System Using Deep SDAE to Increase Effectiveness of Dimensional Reduction in Machine Learning and Deep Learning

“…At the time, deep learning achieved phenomenal performance in the image processing classification task. Additionally, deep learning has established itself as the industry standard for a variety of computer science-related problems, including image processing [15], speech recognition, recommender system using contextual document based on CNN [16][17][18], LSTM [19,20], SDAE [21], Word embedding and LSTM [22].…”

An Intrusion Detection System Using SDAE to Enhance Dimensional Reduction in Machine Learning

“…Deep learning had achieved tremendous performance in the image processing classification task. Furthermore, deep learning has become the industry standard for dealing with a variety of computer science-related problems such as image processing, voice recognition, text mining, recommender system [14], [15], [16], [17], [18], [19], matrix factorization enhancement for recommender system [20]. Recommender system based on location for transportation service [21], product document representation to enhance collaborative filtering based on matrix factorization [22], [23], [24], CNN for document context for recommender system [25].…”