DOI : https://doi.org/10.18517/ijaseit.9.4.7402

A Comparative Study between Collaborative Filtering Techniques and Generate Personalized Story Recommendations for the Vixio Application

Albert Darmawan (1), Ida Bagus Kerthyayana Manuaba (2)
(1) Bina Nusantara University
(2) Bina Nusantara University
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How to cite (IJASEIT) :
Darmawan, Albert, and Ida Bagus Kerthyayana Manuaba. “A Comparative Study Between Collaborative Filtering Techniques and Generate Personalized Story Recommendations for the Vixio Application”. International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 4, Aug. 2019, pp. 1223-30, doi:10.18517/ijaseit.9.4.7402.
Citation Format :
Interactive fiction (or text-based game) is a game that consists of texts which are used to bring interactivity to a story. Interactive fiction shows the potential to improve reading behaviour and engage the player with reading materials. In continuing to explore more benefits in reading, creating, and sharing interactive fiction, a web application called Vixio is developed as a platform, where users can develop and distribute interactive fiction. To engage and to feed the users with more interactive stories, a recommender system is applied to provide recommendations of stories that would be suitable to the reader’s interest. This paper is focused on developing a recommender system which can generate personalized story recommendations for the Vixio web application. This paper also discusses determining which techniques are better to be implemented inside the recommender system by conducting a comparative study between five collaborative filtering techniques, which are: Three Matrix Factorizations (SVD, SVD++, and NMF), Slope One, and Co-clustering. To compare each technique with one another, 5-fold cross-validation and response time were measured. Based on these two evaluations, it is shown that there is no technique which has a superior accuracy over the others. However, Slope One algorithm is eminent in terms of fit time and mean response time.

I. B. K. Manuaba, “Text-Based Games as Potential Media for Improving Reading Behaviour in Indonesia,” in Procedia Computer Science, 2017, vol. 116, pp. 214-221.

A. Darmawan, S. Valdo, I. Ignatius, and I. B. K. Manuaba, “A Comparative Study Between Narrative Fiction and Interactive Fiction to Enhance Youth Literacy in Indonesia,” in 11th Annual International Conference on Computer Games Multimedia and Allied Technologies (CGAT 2018), 2018, pp. 15-21.

Textadventures, “textadventures.co.uk - Create and play text adventure games,” textadventures, 2018.

textadventures.co.uk, “Quest - Write text adventure games and interactive stories.” .

Inform, “About Interactive Fiction: Inform,” Inform, 2018. .

P. Melville and V. Sindhwani, “Recommender Systems,” Encycl. Mach. Learn., no. 338, pp. 829-838, 2011.

Y. Yun, D. Hooshyar, J. Jo, and H. Lim, “Developing a hybrid collaborative filtering recommendation system with opinion mining on purchase review,” J. Inf. Sci., vol. 44, no. 3, pp. 331-344, Jun. 2018.

U. Kuzelewska, “Clustering Algorithms in Hybrid Recommender System on MovieLens Data,” Stud. Logic, Gramm. Rhetor., vol. 37, no. 1, pp. 125-139, Aug. 2014.

K. Haruna, M. Akmar Ismail, D. Damiasih, J. Sutopo, and T. Herawan, “A collaborative approach for research paper recommender system,” PLoS One, vol. 12, no. 10, p. e0184516, Oct. 2017.

C. A. Gomez-Uribe and N. Hunt, “The Netflix Recommender System,” ACM Trans. Manag. Inf. Syst., vol. 6, no. 4, pp. 1-19, Dec. 2015.

J. Brownlee, “A Gentle Introduction to Matrix Factorization for Machine Learning,” Linear Algebra, 2018. .

G. Li and Q. Chen, “Exploiting Explicit and Implicit Feedback for Personalized Ranking,” Math. Probl. Eng., vol. 2016, pp. 1-11, Jan. 2016.

D. Bokde, S. Girase, and D. Mukhopadhyay, “Matrix Factorization Model in Collaborative Filtering Algorithms: A Survey,” Procedia Comput. Sci., vol. 49, pp. 136-146, Jan. 2015.

D. Lemire and A. Maclachlan, “Slope One Predictors for Online Rating-Based Collaborative Filtering,” in Proceedings of the 2005 SIAM International Conference on Data Mining, Philadelphia, PA: Society for Industrial and Applied Mathematics, 2005, pp. 471-475.

N. Hug, “Slope One,” Surprise, 2015. .

T. George and S. Merugu, “A Scalable Collaborative Filtering Framework Based on Co-Clustering,” in Fifth IEEE International Conference on Data Mining (ICDM’05), 2005, pp. 625-628.

N. Hug, “Co-clustering,” Surprise, 2015. .

J. Brownlee, “A Gentle Introduction to k-fold Cross-Validation,” Statistical Methods, 2018. .

Scikit-learn developers, “Cross-validation: evaluating estimator performance,” scikit learn, 2017. .

D. Becker, “Cross-Validation | Kaggle,” Kaggle, 2018. .

J. Wesner, “MAE and RMSE — Which Metric is Better?,” Medium - Human in a Machine World, 2016. .

Techopedia, “Response Time,” Techopedia, 2018. .

A. Shellhammer, “The need for mobile speed: How mobile latency impacts publisher revenue,” DoubleClck by Google, 2016.

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