@conference{Elahi2017b,
title = {Visually-Aware Video Recommendation in the Cold Start},
author = {Mehdi Elahi and Reza Hosseini and Mohammad Hossein Rimaz and Farshad B. Moghaddam and Christoph Trattner},
url = {https://christophtrattner.com/pubs/ht2020.pdf},
year = {2017},
date = {2017-07-01},
urldate = {2017-07-01},
pages = {1-5},
organization = {Proccedings of theACM Hypertext 2020},
abstract = {Recommender Systems (RSs) have become essential tools in any
video-sharing platforms (such as YouTube) by generating video
suggestions for users. Although, RSs have been e!ective, however,
they su!er from the so-called New Item problem. New item problem,
as part of Cold Start problem, happens when a new item is added to
the system catalogue and the RS has no or little data available for
that new item. In such a case, the system may fail to meaningfully
recommend the new item to any user.
In this paper, we propose a novel recommendation technique
based on visual tags, i.e., tags that are automatically annotated
to videos based on visual description of videos. Such visual tags
can be used in an extreme cold start situation, where neither any
rating, nor any tag is available for the new video item. The visual
tags could also be used in the moderate cold start situation when
the new video item has been annotated with few tags. This type
of content features can be extracted automatically without any
human involvement and have been shown to be very e!ective in
representing the video content.
We have used a large dataset of videos and shown that automatically
extracted visual tags can be incorporated into the cold start
recommendation process and achieve superior results compared to
the recommendation based on human-annotated tags.},
note = {Pre SFI},
keywords = {Video Recommendation},
pubstate = {published},
tppubtype = {conference}
}