Open AccessDeep Contextual of Document Using Deep LSTM Meet Matrix Factorization to Handle Sparse Data: Proposed Model
Author(s) -
Hanafi Hanafi,
Nanna Suryana,
Ash Basari
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1577/1/012002
Subject(s) - collaborative filtering , computer science , recommender system , exploit , matrix decomposition , scalability , sparse matrix , big data , artificial intelligence , product (mathematics) , process (computing) , information retrieval , machine learning , data mining , database , eigenvalues and eigenvectors , physics , geometry , computer security , mathematics , quantum mechanics , gaussian , operating system
Recommender system is important tool in big data era. It has responsible to make suggestion about product or service automatically for web application or mobile. In everyday utility, we cannot escape for information about food, travelling, social network, ticketing, news and etc. What the best choice for customer necessary is recommender system task to provide relevant information. Collaborative filtering is most useful recommender system technique in which considering user behaviour in the past to calculate recommendation. The first generation of collaborative filtering exploit statistical approach to calculate product recommendation. However, traditional collaborative filtering facing serious problem in scalability, accuracy and shortcoming in large data. Model based in the second generation of collaborative filtering to produce product recommendation where this model rely on matrix factorization to produce recommendation. Model based proven better performance over memory based. However, model-based performance degrades significantly when met with sparse data due the number of rating are very small. This problem popular called sparse data problem. Several methods proposed by researchers to handle sparse data problem. Mostly of them exploit text document to increase recommendation performance. However, majority of model fail to gain text document understanding. This study proceeds ongoing process with several stage. First, develop model to interpreted text document using LSTM aims to capture contextual understanding of document. Second, integrated LSTM with matrix factorization. This step aims to produce rating prediction considering text document of the product. The first step completely finished. According to experiment report, this model success to capture contextual of the document then transform into 2D space text document representation. For the further research, we are going to integrated with matrix factorization and evaluation result of rating prediction using RMSE metric evaluation.