102 research outputs found
Improving the Navigability of Tagging Systems with Hierarchically Constructed Resource Lists and Tag Trails
Recent research has shown that the navigability of tagging systems leaves much to be desired. In general, it was observed that tagging systems are not navigable if the resource lists of the tagging system are limited to a certain factor k. Hence, in this paper a novel resource list generation approach is introduced that addresses this issue. The proposed approach is based on a hierarchical network model. The paper shows through a number of experiments based on a tagging dataset from a large online encyclopedia system called Austria-Forum, that the new algorithm is able to create tag network structures that are navigable in an efficient manner. Contrary to previous work, the method featured in this paper is completely generic, i.e. the introduced resource list generation approach could be used to improve the navigability of any tagging system. This work is relevant for researchers interested in navigability of emergent hypertext structures and for engineers seeking to improve the navigability of tagging systems
Twitter in Academic Conferences: Usage, Networking and Participation over Time
Twitter is often referred to as a backchannel for conferences. While the main
conference takes place in a physical setting, attendees and virtual attendees
socialize, introduce new ideas or broadcast information by microblogging on
Twitter. In this paper we analyze the scholars' Twitter use in 16 Computer
Science conferences over a timespan of five years. Our primary finding is that
over the years there are increasing differences with respect to conversation
use and information use in Twitter. We studied the interaction network between
users to understand whether assumptions about the structure of the
conversations hold over time and between different types of interactions, such
as retweets, replies, and mentions. While `people come and people go', we want
to understand what keeps people stay with the conference on Twitter. By casting
the problem to a classification task, we find different factors that contribute
to the continuing participation of users to the online Twitter conference
activity. These results have implications for research communities to implement
strategies for continuous and active participation among members
An evaluation of recommendation algorithms for online recipe portals
Better models of food preferences are required to realise the oft touted potential of food recommenders to aid with the obesity crisis. Many of the food recommender evaluations in the literature have been performed with small convenience samples, which limits our conidence in the generalisability of the results. In this work we test a range of collaborative iltering (CF) and content-based (CB) recommenders on a large dataset crawled from the web consisting of naturalistic user interaction data over a 15 year period. The results reveal strengths and limitations of diferent approaches. While CF approaches consistently outperform CB approaches when testing on the complete dataset, our experiments show that to improve on CF methods require a large number of users (> 637 when sampling randomly). Moreover the results show diferent facets of recipe content to ofer utility. In particular one of the strongest content related features was a measure of health derived from guidelines from the UK Food Safety Agency. This inding underlines the challenges we face as a community to develop recommender algorithms, which improve the healthfulness of the food people choose to eat.publishedVersio
Recommending Items in Social Tagging Systems Using Tag and Time Information
In this work we present a novel item recommendation approach that aims at
improving Collaborative Filtering (CF) in social tagging systems using the
information about tags and time. Our algorithm follows a two-step approach,
where in the first step a potentially interesting candidate item-set is found
using user-based CF and in the second step this candidate item-set is ranked
using item-based CF. Within this ranking step we integrate the information of
tag usage and time using the Base-Level Learning (BLL) equation coming from
human memory theory that is used to determine the reuse-probability of words
and tags using a power-law forgetting function.
As the results of our extensive evaluation conducted on data-sets gathered
from three social tagging systems (BibSonomy, CiteULike and MovieLens) show,
the usage of tag-based and time information via the BLL equation also helps to
improve the ranking and recommendation process of items and thus, can be used
to realize an effective item recommender that outperforms two alternative
algorithms which also exploit time and tag-based information.Comment: 6 pages, 2 tables, 9 figure
Comparing Neural and Attractiveness-based Visual Features for Artwork Recommendation
Advances in image processing and computer vision in the latest years have
brought about the use of visual features in artwork recommendation. Recent
works have shown that visual features obtained from pre-trained deep neural
networks (DNNs) perform very well for recommending digital art. Other recent
works have shown that explicit visual features (EVF) based on attractiveness
can perform well in preference prediction tasks, but no previous work has
compared DNN features versus specific attractiveness-based visual features
(e.g. brightness, texture) in terms of recommendation performance. In this
work, we study and compare the performance of DNN and EVF features for the
purpose of physical artwork recommendation using transactional data from
UGallery, an online store of physical paintings. In addition, we perform an
exploratory analysis to understand if DNN embedded features have some relation
with certain EVF. Our results show that DNN features outperform EVF, that
certain EVF features are more suited for physical artwork recommendation and,
finally, we show evidence that certain neurons in the DNN might be partially
encoding visual features such as brightness, providing an opportunity for
explaining recommendations based on visual neural models.Comment: DLRS 2017 workshop, co-located at RecSys 201
Language, Twitter and Academic Conferences
Using Twitter during academic conferences is a way of engaging and connecting
an audience inherently multicultural by the nature of scientific collaboration.
English is expected to be the lingua franca bridging the communication and
integration between native speakers of different mother tongues. However,
little research has been done to support this assumption. In this paper we
analyzed how integrated language communities are by analyzing the scholars'
tweets used in 26 Computer Science conferences over a time span of five years.
We found that although English is the most popular language used to tweet
during conferences, a significant proportion of people also tweet in other
languages. In addition, people who tweet solely in English interact mostly
within the same group (English monolinguals), while people who speak other
languages tend to show a more diverse interaction with other lingua groups.
Finally, we also found that the people who interact with other Twitter users
show a more diverse language distribution, while people who do not interact
mostly post tweets in a single language. These results suggest a relation
between the number of languages a user speaks, which can affect the interaction
dynamics of online communities.Comment: 4 pages, 3 figures, 4 tables, submitted to ACM Hypertext and Social
Media 201
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