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Original scientific paper
https://doi.org/10.17559/TV-20160708140839

Collaborative Filtering with Temporal Dynamics with Using Singular Value Decomposition

Cigdem Bakir ; Yildiz Technical University, Davutpasa Street Esenler, 34220 İstanbul, Turkey

Fulltext: english, pdf (496 KB) pages 130-135 downloads: 792* cite
APA 6th Edition
Bakir, C. (2018). Collaborative Filtering with Temporal Dynamics with Using Singular Value Decomposition. Tehnički vjesnik, 25 (1), 130-135. https://doi.org/10.17559/TV-20160708140839
MLA 8th Edition
Bakir, Cigdem. "Collaborative Filtering with Temporal Dynamics with Using Singular Value Decomposition." Tehnički vjesnik, vol. 25, no. 1, 2018, pp. 130-135. https://doi.org/10.17559/TV-20160708140839. Accessed 13 Apr. 2021.
Chicago 17th Edition
Bakir, Cigdem. "Collaborative Filtering with Temporal Dynamics with Using Singular Value Decomposition." Tehnički vjesnik 25, no. 1 (2018): 130-135. https://doi.org/10.17559/TV-20160708140839
Harvard
Bakir, C. (2018). 'Collaborative Filtering with Temporal Dynamics with Using Singular Value Decomposition', Tehnički vjesnik, 25(1), pp. 130-135. https://doi.org/10.17559/TV-20160708140839
Vancouver
Bakir C. Collaborative Filtering with Temporal Dynamics with Using Singular Value Decomposition. Tehnički vjesnik [Internet]. 2018 [cited 2021 April 13];25(1):130-135. https://doi.org/10.17559/TV-20160708140839
IEEE
C. Bakir, "Collaborative Filtering with Temporal Dynamics with Using Singular Value Decomposition", Tehnički vjesnik, vol.25, no. 1, pp. 130-135, 2018. [Online]. https://doi.org/10.17559/TV-20160708140839

Abstracts
Nowadays, Collaborative Filtering (CF) is a widely used recommendation system. However, traditional CF techniques are harder to make fast and accurate suggestions due to changes in user preferences over time, the emergence of new products and the availability of too many users and too many products in the system. Therefore, it becomes more important to make suggestions that are both fast and take the changes in time into consideration. In the presented study, a new method for providing suggestions customized according to the users' preference and taste as they change over time was developed. By combining the time-dependent changes through the SVD (Singular Value Decomposition), a faster suggestion system was developed. Thus, an attempt was made to enhance product prediction success. In the present study all techniques on Netflix data and the results were compared. The results obtained on the accuracy of the predicted ratings were found out to be promising.

Keywords
Collaborative Filtering; recommendation system; temporal dynamics

Hrčak ID: 193606

URI
https://hrcak.srce.hr/193606

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