@inproceedings{78472a9b4c2a4eb39b942a325450db86,
title = "Crowdsourced time-sync video recommendation via semantic-aware neural collaborative filtering",
abstract = "As an emerging type of video comments, time-sync comments (TSCs) enable viewers to make comments on video shots in a real-time manner. Such comments well reflect user interests in the frame level, which can be utilized to further improve the accuracy of video recommendation. In this paper, we make the first attempt in this direction and propose a new video recommendation algorithm called SACF by exploiting temporal relationship between time-sync comments and video frames. Our algorithm can extract a rich set of semantic features from crowdsourced time-sync comments, and combine latent semantic representations of users and videos by neural collaborative filtering. We conduct extensive experiments using real TSC datasets, and our results show that our proposed algorithm can improve the recommendation performance by 9.73% in HR@10 and 5.72% in NDCG@10 compared with other baseline solutions.",
keywords = "Collaborative filtering, Recommender system, Time-synchronized comment",
author = "Zhanpeng Wu and Yan Zhou and Di Wu and Yipeng Zhou and Jing Qin",
year = "2019",
month = jan,
day = "1",
doi = "10.1007/978-3-030-19274-7_13",
language = "English",
isbn = "9783030192730",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-VDI-Verlag GmbH & Co. KG",
pages = "171--186",
editor = "Maxim Bakaev and Flavius Frasincar and In-Young Ko",
booktitle = "Web Engineering",
address = "Germany",
note = "19th International Conference on Web Engineering, ICWE 2019 ; Conference date: 11-06-2019 Through 14-06-2019",
}