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

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@article{IJASEIT7402,
   author = {Albert Darmawan and Ida Bagus Kerthyayana Manuaba},
   title = {A Comparative Study between Collaborative Filtering Techniques and Generate Personalized Story Recommendations for the Vixio Application},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {9},
   number = {4},
   year = {2019},
   pages = {1223--1230},
   keywords = {collaborative filtering; matrix factorization; slope one; co-clustering; vixio – interactive fiction platform.},
   abstract = {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.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7402},
   doi = {10.18517/ijaseit.9.4.7402}
}

EndNote

%A Darmawan, Albert
%A Manuaba, Ida Bagus Kerthyayana
%D 2019
%T A Comparative Study between Collaborative Filtering Techniques and Generate Personalized Story Recommendations for the Vixio Application
%B 2019
%9 collaborative filtering; matrix factorization; slope one; co-clustering; vixio – interactive fiction platform.
%! A Comparative Study between Collaborative Filtering Techniques and Generate Personalized Story Recommendations for the Vixio Application
%K collaborative filtering; matrix factorization; slope one; co-clustering; vixio – interactive fiction platform.
%X 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.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7402
%R doi:10.18517/ijaseit.9.4.7402
%J International Journal on Advanced Science, Engineering and Information Technology
%V 9
%N 4
%@ 2088-5334

IEEE

Albert Darmawan 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, pp. 1223-1230, 2019. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.9.4.7402.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Darmawan, Albert
AU  - Manuaba, Ida Bagus Kerthyayana
PY  - 2019
TI  - A Comparative Study between Collaborative Filtering Techniques and Generate Personalized Story Recommendations for the Vixio Application
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 9 (2019) No. 4
Y2  - 2019
SP  - 1223
EP  - 1230
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - collaborative filtering; matrix factorization; slope one; co-clustering; vixio – interactive fiction platform.
N2  - 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.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7402
DO  - 10.18517/ijaseit.9.4.7402

RefWorks

RT Journal Article
ID 7402
A1 Darmawan, Albert
A1 Manuaba, Ida Bagus Kerthyayana
T1 A Comparative Study between Collaborative Filtering Techniques and Generate Personalized Story Recommendations for the Vixio Application
JF International Journal on Advanced Science, Engineering and Information Technology
VO 9
IS 4
YR 2019
SP 1223
OP 1230
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 collaborative filtering; matrix factorization; slope one; co-clustering; vixio – interactive fiction platform.
AB 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.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=7402
DO  - 10.18517/ijaseit.9.4.7402