Browsing by Author "Kaveendri, D."

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Embargo
Hybrid Recommender for Condensed Sinhala News with Grey Sheep User Identification
(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Tennakoon, A.; Gamlath, N.; Ranatunga, J.; Haddela, P.; Kaveendri, D.
With the present explosion of news circulating the digital space, which consists mostly of unstructured textual data, there is a need to absorb the content of news easily and effectively. While there are many Sinhala news sites out there, no site facilitates recommendation despite the popularity of recommender systems in the current age and day. Therefore, it is effective if the news were presented in a summarized version which tallies with the user preferences as well. Our research aims to fill these gaps by providing a centralized news platform that recommends news to its users clearly and concisely. The news articles were collected using web scraping and after performing categorization it will be presented in a summarized context. Also, we expect to detect the grey sheep users and to provide separate recommendations to them in order to minimize errors in the recommendation. By implementing the proposed system, we provide a user-friendly Sinhala news platform.
Embargo
A Story of Two Surveys: for the Advancement of Sinhalese Mobile Text Entry Research
(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Reyal, S.; Piyawardana, V.; Kaveendri, D.
This paper presents two surveys: a literature survey on the current progress on Sinhalese mobile text entry research and a user survey on how Sri Lankans experience Sinhalese mobile text entry. The first survey concludes that Sinhalese mobile text entry is limited in scope and size compared to western text entry research. The second survey attempts to bridge this gap by providing deep insight into aspects in Sinhalese mobile text entry such as language switching, using English within Sinhalese e.g. mixed-mode and Singlish, and the popularity of various input modalities, keyboard vendors, and keyboard layouts. This is also the first research publication that unveils the current state-of-the-art in Sinhalese mobile text entry, along with user-preferences such as using autocorrect, glide-typing, and speech. Results from this survey deepens our understanding of the Sinhalese mobile text entry domain resulting in a stronger empirical footing and more innovative Sinhalese mobile text entry solutions.