2,823 research outputs found
Exploring dynamics and semantics of user interests for user modeling on Twitter for link recommendations
User modeling for individual users on the Social Web plays
an important role and is a fundamental step for personalization as well as recommendations. Recent studies have
proposed different user modeling strategies considering various dimensions such as temporal dynamics and semantics
of user interests. Although previous work proposed different
user modeling strategies considering the temporal dynamics
of user interests, there is a lack of comparative studies on
those methods and therefore the comparative performance
over each other is unknown. In terms of semantics of user
interests, background knowledge from DBpedia has been
explored to enrich user interest profiles so as to reveal more
information about users. However, it is still unclear to what
extent different types of information from DBpedia contribute
to the enrichment of user interest profiles.
In this paper, we propose user modeling strategies which
use Concept Frequency - Inverse Document Frequency (CF-IDF) as a weighting scheme and incorporate either or both
of the dynamics and semantics of user interests. To this end,
we first provide a comparative study on different user modeling strategies considering the dynamics of user interests in
previous literature to present their comparative performance.
In addition, we investigate different types of information (i.e.,
categories, classes and connected entities via various properties) for entities from DBpedia and the combination of them
for extending user interest profiles. Finally, we build our user
modeling strategies incorporating either or both of the best performing methods in each dimension. Results show that
our strategies outperform two baseline strategies significantly
in the context of link recommendations on Twitter
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
Modelling Requirements for Content Recommendation Systems
This paper addresses the modelling of requirements for a content
Recommendation System (RS) for Online Social Networks (OSNs). On OSNs, a user
switches roles constantly between content generator and content receiver. The
goals and softgoals are different when the user is generating a post, as
opposed as replying to a post. In other words, the user is generating instances
of different entities, depending on the role she has: a generator generates
instances of a "post", while the receiver generates instances of a "reply".
Therefore, we believe that when addressing Requirements Engineering (RE) for
RS, it is necessary to distinguish these roles clearly.
We aim to model an essential dynamic on OSN, namely that when a user creates
(posts) content, other users can ignore that content, or themselves start
generating new content in reply, or react to the initial posting. This dynamic
is key to designing OSNs, because it influences how active users are, and how
attractive the OSN is for existing, and to new users. We apply a well-known
Goal Oriented RE (GORE) technique, namely i-star, and show that this language
fails to capture this dynamic, and thus cannot be used alone to model the
problem domain. Hence, in order to represent this dynamic, its relationships to
other OSNs' requirements, and to capture all relevant information, we suggest
using another modelling language, namely Petri Nets, on top of i-star for the
modelling of the problem domain. We use Petri Nets because it is a tool that is
used to simulate the dynamic and concurrent activities of a system and can be
used by both practitioners and theoreticians.Comment: 28 pages, 7 figure
Inferring user interests in microblogging social networks: a survey
With the growing popularity of microblogging services such as Twitter in recent years,
an increasing number of users are using these services in their daily lives. The huge volume of information generated by users raises new opportunities in various applications
and areas. Inferring user interests plays a significant role in providing personalized
recommendations on microblogging services, and also on third-party applications
providing social logins via these services, especially in cold-start situations. In this
survey, we review user modeling strategies with respect to inferring user interests
from previous studies. To this end, we focus on four dimensions of inferring user
interest profiles: (1) data collection, (2) representation of user interest profiles, (3)
construction and enhancement of user interest profiles, and (4) the evaluation of the
constructed profiles. Through this survey, we aim to provide an overview of state-of-the-art user modeling strategies for inferring user interest profiles on microblogging
social networks with respect to the four dimensions. For each dimension, we review
and summarize previous studies based on specified criteria. Finally, we discuss some
challenges and opportunities for future work in this research domain
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