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

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.

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Development of Fuel Calculation Applications Android Based for Ship Operations

This study aims to develop an android-based application to calculate the amount of fuel adjusted to the distance and speed of the ship based on the trajectory (way point). The calculation of distance in this application uses the concept of a spherical triangle which is used in voyage navigation. This development adopts the Waterfall model with the stages of Requirement Engineering, Design and implementation, Testing, Release and Maintenance. At the product review stage, it involves software experts (to provide criticism and suggestions for improvement), then testing is carried out by comparing the results of calculations using applications with manual calculations and ECDIS (electronic Chart Display Information System). The test results show that there is no significant difference between the results of calculations using the application with manual calculations and ECDIS. Thus, this application can be used to calculate the amount of fuel needed according to the distance traveled and the speed of the ship based on the selected trajectory.