How to cite (IEEE): -. Hanafi, A. Widowati, -. Jaeni, & J. Rusdi "Enhance Document Contextual Using Attention-LSTM to Eliminate Sparse Data Matrix for E-Commerce Recommender System," JOIV : International Journal on Informatics Visualization, vol. 6, no. 3, , pp. 688-696, Sep. 2022. https://doi.org/10.30630/joiv.6.3.1233

How to cite (APA): Hanafi, -., Widowati, A., Jaeni, -., & Rusdi, J. (2022). Enhance Document Contextual Using Attention-LSTM to Eliminate Sparse Data Matrix for E-Commerce Recommender System. JOIV : International Journal on Informatics Visualization, 6(3), 688-696. https://doi.org/10.30630/joiv.6.3.1233

How to cite (Chicago): Hanafi, -, Widowati, Anik Sri, Jaeni, -, AND Rusdi, Jack Febrian. "Enhance Document Contextual Using Attention-LSTM to Eliminate Sparse Data Matrix for E-Commerce Recommender System" JOIV : International Journal on Informatics Visualization [Online], Volume 6 Number 3 (30 September 2022) https://doi.org/10.30630/joiv.6.3.1233

How to cite (Vancouver): Hanafi -, Widowati A, Jaeni -, & Rusdi J . Enhance Document Contextual Using Attention-LSTM to Eliminate Sparse Data Matrix for E-Commerce Recommender System. JOIV : International Journal on Informatics Visualization [Online]. 2022 Sep; 6(3):688-696. https://doi.org/10.30630/joiv.6.3.1233

How to cite (Harvard): Hanafi, -., Widowati, A., Jaeni, -., & Rusdi, J. ,2022. Enhance Document Contextual Using Attention-LSTM to Eliminate Sparse Data Matrix for E-Commerce Recommender System. JOIV : International Journal on Informatics Visualization, [Online] 6(3), pp. 688-696. https://doi.org/10.30630/joiv.6.3.1233

How to cite (MLA8): Hanafi, -, Anik Sri Widowati, - Jaeni, & Jack Febrian Rusdi. "Enhance Document Contextual Using Attention-LSTM to Eliminate Sparse Data Matrix for E-Commerce Recommender System." JOIV : International Journal on Informatics Visualization [Online], 6.3 (2022): 688-696. Web. 26 Mar. 2023 , https://doi.org/10.30630/joiv.6.3.1233

BibTex Citation Data :

@article{JOIV1233,
    	author = {- Hanafi and Anik Sri Widowati and - Jaeni and Jack Febrian Rusdi},
    	title = {Enhance Document Contextual Using Attention-LSTM to Eliminate Sparse Data Matrix for E-Commerce Recommender System},
    	journal = {JOIV : International Journal on Informatics Visualization},
    	volume = {6},
    	number = {3},
    	year = {2022},
    	keywords = {Sparsity data; recommender system; matrix factorization; e-commerce; attention mechanism; PMF.},
    	abstract = {E-commerce has been the most important service in the last two decades. E-commerce services influence the growth of the economic impact worldwide. A recommender system is an essential mechanism for calculating product information for e-commerce users. The successfulness of recommender system adoption influences the target revenue of an e-commerce company. Collaborative filtering (CF) is the most popular algorithm for creating a recommender system. CF applied a matrix factorization mechanism to calculate the relationship between user and product using rating variable as intersection value between user and product. However, the number of ratings is very sparse, where the number of ratings is less than 4%. Product Document is the product side information representation. The document aims to advance the effectiveness of matrix factorization performance. This research considers to the enhancement of document context using LSTM with an attention mechanism to capture a contextual understanding of product review and incorporate matrix factorization based on probabilistic matrix factorization (PMF) to produce rating prediction. This study employs a real dataset using MovieLens dataset ML.1M and Amazon information video (AIV) to observe our ATT-PMF model. Movielens dataset represents of number sparse rating that only contains below 4% (ML.1M). Our experiment report shows that ATT-PMF outperforms more than 2% on average than previous work. Moreover, our model is also suitable to implement on huge datasets. For further research, enhancement of product document context will be a good factor in eliminating sparse data problems in big data problems.},
        	issn = {2549-9904},    	pages = {688--696},    	doi = {10.30630/joiv.6.3.1233},
        	url = {https://joiv.org/index.php/joiv/article/view/1233}
    }
    

Refworks Citation Data :

@article{{JOIV}{1233}, author = {Hanafi, -., Widowati, A., Jaeni, -., Rusdi, J.}, title = {Enhance Document Contextual Using Attention-LSTM to Eliminate Sparse Data Matrix for E-Commerce Recommender System}, journal = {JOIV : International Journal on Informatics Visualization}, volume = {6}, number = {3}, year = {2022}, doi = {10.30630/joiv.6.3.1233}, url = {} }