7,115 research outputs found
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
Learning to Hash-tag Videos with Tag2Vec
User-given tags or labels are valuable resources for semantic understanding
of visual media such as images and videos. Recently, a new type of labeling
mechanism known as hash-tags have become increasingly popular on social media
sites. In this paper, we study the problem of generating relevant and useful
hash-tags for short video clips. Traditional data-driven approaches for tag
enrichment and recommendation use direct visual similarity for label transfer
and propagation. We attempt to learn a direct low-cost mapping from video to
hash-tags using a two step training process. We first employ a natural language
processing (NLP) technique, skip-gram models with neural network training to
learn a low-dimensional vector representation of hash-tags (Tag2Vec) using a
corpus of 10 million hash-tags. We then train an embedding function to map
video features to the low-dimensional Tag2vec space. We learn this embedding
for 29 categories of short video clips with hash-tags. A query video without
any tag-information can then be directly mapped to the vector space of tags
using the learned embedding and relevant tags can be found by performing a
simple nearest-neighbor retrieval in the Tag2Vec space. We validate the
relevance of the tags suggested by our system qualitatively and quantitatively
with a user study
Event Organization 101: Understanding Latent Factors of Event Popularity
The problem of understanding people's participation in real-world events has
been a subject of active research and can offer valuable insights for human
behavior analysis and event-related recommendation/advertisement. In this work,
we study the latent factors for determining event popularity using large-scale
datasets collected from the popular Meetup.com EBSN in three major cities
around the world. We have conducted modeling analysis of four contextual
factors (spatial, group, temporal, and semantic), and also developed a
group-based social influence propagation network to model group-specific
influences on events. By combining the Contextual features And Social Influence
NetwOrk, our integrated prediction framework CASINO can capture the diverse
influential factors of event participation and can be used by event organizers
to predict/improve the popularity of their events. Evaluations demonstrate that
our CASINO framework achieves high prediction accuracy with contributions from
all the latent features we capture.Comment: International AAAI Conference on Web and Social Media (ICWSM) 2017
https://www.aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/view/1557
#Bieber + #Blast = #BieberBlast: Early Prediction of Popular Hashtag Compounds
Compounding of natural language units is a very common phenomena. In this
paper, we show, for the first time, that Twitter hashtags which, could be
considered as correlates of such linguistic units, undergo compounding. We
identify reasons for this compounding and propose a prediction model that can
identify with 77.07% accuracy if a pair of hashtags compounding in the near
future (i.e., 2 months after compounding) shall become popular. At longer times
T = 6, 10 months the accuracies are 77.52% and 79.13% respectively. This
technique has strong implications to trending hashtag recommendation since
newly formed hashtag compounds can be recommended early, even before the
compounding has taken place. Further, humans can predict compounds with an
overall accuracy of only 48.7% (treated as baseline). Notably, while humans can
discriminate the relatively easier cases, the automatic framework is successful
in classifying the relatively harder cases.Comment: 14 pages, 4 figures, 9 tables, published in CSCW (Computer-Supported
Cooperative Work and Social Computing) 2016. in Proceedings of 19th ACM
conference on Computer-Supported Cooperative Work and Social Computing (CSCW
2016
A Data-Driven Approach for Tag Refinement and Localization in Web Videos
Tagging of visual content is becoming more and more widespread as web-based
services and social networks have popularized tagging functionalities among
their users. These user-generated tags are used to ease browsing and
exploration of media collections, e.g. using tag clouds, or to retrieve
multimedia content. However, not all media are equally tagged by users. Using
the current systems is easy to tag a single photo, and even tagging a part of a
photo, like a face, has become common in sites like Flickr and Facebook. On the
other hand, tagging a video sequence is more complicated and time consuming, so
that users just tag the overall content of a video. In this paper we present a
method for automatic video annotation that increases the number of tags
originally provided by users, and localizes them temporally, associating tags
to keyframes. Our approach exploits collective knowledge embedded in
user-generated tags and web sources, and visual similarity of keyframes and
images uploaded to social sites like YouTube and Flickr, as well as web sources
like Google and Bing. Given a keyframe, our method is able to select on the fly
from these visual sources the training exemplars that should be the most
relevant for this test sample, and proceeds to transfer labels across similar
images. Compared to existing video tagging approaches that require training
classifiers for each tag, our system has few parameters, is easy to implement
and can deal with an open vocabulary scenario. We demonstrate the approach on
tag refinement and localization on DUT-WEBV, a large dataset of web videos, and
show state-of-the-art results.Comment: Preprint submitted to Computer Vision and Image Understanding (CVIU
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