A streaming experience is more than playback. Viewers need discovery without repetitive suggestions and social presence without leaving the content experience.
The project is important because it does not treat the algorithm as an isolated demonstration. It asks how data, representation, computation, interface, and evaluation must work together before a technical idea becomes a useful research contribution.
Recommendation finds the next story; real-time interaction makes watching feel shared. The contribution is the complete research argument connecting a real problem to a computational design and then testing whether that design produces meaningful evidence. Familiar techniques become research when their selection, combination, implementation, and evaluation reveal something that was not previously clear.
Use the three most recently watched titles as preference context
Here the central computational idea is implemented. The value lies in how the method is adapted to the research question, not merely in naming an algorithm.
Overlay real-time floating comments during viewing
Evaluation reconnects the technical artifact to the original question. Metrics, user evidence, and error patterns determine how far the conclusion can travel.
01Using the three latest watched titles produced the study’s most effective recommendation setting.ผลลัพธ์ลำดับที่ 1 แสดงความเชื่อมโยงระหว่างการออกแบบระบบกับหลักฐานที่สังเกตได้ โดยต้องตีความภายในขอบเขตของชุดข้อมูล ผู้เข้าร่วม และเงื่อนไขการทดลอง02The approach broadened genre variety and reduced repeated suggestions.ผลลัพธ์ลำดับที่ 2 แสดงความเชื่อมโยงระหว่างการออกแบบระบบกับหลักฐานที่สังเกตได้ โดยต้องตีความภายในขอบเขตของชุดข้อมูล ผู้เข้าร่วม และเงื่อนไขการทดลอง03Floating comments created an immediate channel for community participation.ผลลัพธ์ลำดับที่ 3 แสดงความเชื่อมโยงระหว่างการออกแบบระบบกับหลักฐานที่สังเกตได้ โดยต้องตีความภายในขอบเขตของชุดข้อมูล ผู้เข้าร่วม และเงื่อนไขการทดลอง
Research interpretationการตีความงานวิจัย
The metric is the beginning of the explanation
ตัวเลขคือจุดเริ่มต้นของคำอธิบาย
The reported results matter because they show that the proposed system can work under the study conditions. The deeper value is explanatory: the study identifies which representation and workflow made the outcome possible, where the approach is likely to transfer, and what uncertainty remains. That makes the work useful to researchers, developers, educators, and students designing the next experiment.
The findings depend on the available anime database and the project’s evaluation setting. Moderation, licensing, accessibility, and large-scale latency are outside the prototype’s evidence.
Each direction changes one meaningful assumption and can become a prototype, an experiment, and a defensible contribution. Students can begin with reproduction, document the baseline, then introduce one carefully justified change.
APASuvil Chomchaiya, Kunakron Tana, Thanachot Wongyai, Thanapong Simmanee, Pasin Laopooti, and Warin Wattanapornprom (2024). Development of a Web-Based Anime Streaming and Recommending Application. CreTech 2024.IEEES. Chomchaiya, Kunakron Tana, Thanachot Wongyai, Thanapong Simmanee, Pasin Laopooti, Warin Wattanapornprom, “Development of a Web-Based Anime Streaming and Recommending Application,” CreTech 2024, 2024.BibTeX@inproceedings{chomchaiya2024anime,
title={Development of a Web-Based Anime Streaming and Recommending Application},
author={Suvil Chomchaiya and Kunakron Tana and Thanachot Wongyai and Thanapong Simmanee and Pasin Laopooti and Warin Wattanapornprom},
booktitle={CreTech 2024},
year={2024}
}