10,159 research outputs found
Finding co-solvers on Twitter, with a little help from Linked Data
In this paper we propose a method for suggesting potential collaborators for solving innovation challenges online, based on their competence, similarity of interests and social proximity with the user. We rely on Linked Data to derive a measure of semantic relatedness that we use to enrich both user profiles and innovation problems with additional relevant topics, thereby improving the performance of co-solver recommendation. We evaluate this approach against state of the art methods for query enrichment based on the distribution of topics in user profiles, and demonstrate its usefulness in recommending collaborators that are both complementary in competence and compatible with the user. Our experiments are grounded using data from the social networking service Twitter.com
A Topic Recommender for Journalists
The way in which people acquire information on events and form their own
opinion on them has changed dramatically with the advent of social media. For many
readers, the news gathered from online sources become an opportunity to share points
of view and information within micro-blogging platforms such as Twitter, mainly
aimed at satisfying their communication needs. Furthermore, the need to deepen the
aspects related to news stimulates a demand for additional information which is often
met through online encyclopedias, such as Wikipedia. This behaviour has also
influenced the way in which journalists write their articles, requiring a careful assessment
of what actually interests the readers. The goal of this paper is to present
a recommender system, What to Write and Why, capable of suggesting to a journalist,
for a given event, the aspects still uncovered in news articles on which the
readers focus their interest. The basic idea is to characterize an event according to
the echo it receives in online news sources and associate it with the corresponding
readers’ communicative and informative patterns, detected through the analysis of
Twitter and Wikipedia, respectively. Our methodology temporally aligns the results
of this analysis and recommends the concepts that emerge as topics of interest from
Twitter and Wikipedia, either not covered or poorly covered in the published news
articles
A Trio Neural Model for Dynamic Entity Relatedness Ranking
Measuring entity relatedness is a fundamental task for many natural language
processing and information retrieval applications. Prior work often studies
entity relatedness in static settings and an unsupervised manner. However,
entities in real-world are often involved in many different relationships,
consequently entity-relations are very dynamic over time. In this work, we
propose a neural networkbased approach for dynamic entity relatedness,
leveraging the collective attention as supervision. Our model is capable of
learning rich and different entity representations in a joint framework.
Through extensive experiments on large-scale datasets, we demonstrate that our
method achieves better results than competitive baselines.Comment: In Proceedings of CoNLL 201
Multiple Models for Recommending Temporal Aspects of Entities
Entity aspect recommendation is an emerging task in semantic search that
helps users discover serendipitous and prominent information with respect to an
entity, of which salience (e.g., popularity) is the most important factor in
previous work. However, entity aspects are temporally dynamic and often driven
by events happening over time. For such cases, aspect suggestion based solely
on salience features can give unsatisfactory results, for two reasons. First,
salience is often accumulated over a long time period and does not account for
recency. Second, many aspects related to an event entity are strongly
time-dependent. In this paper, we study the task of temporal aspect
recommendation for a given entity, which aims at recommending the most relevant
aspects and takes into account time in order to improve search experience. We
propose a novel event-centric ensemble ranking method that learns from multiple
time and type-dependent models and dynamically trades off salience and recency
characteristics. Through extensive experiments on real-world query logs, we
demonstrate that our method is robust and achieves better effectiveness than
competitive baselines.Comment: In proceedings of the 15th Extended Semantic Web Conference (ESWC
2018
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