176,800 research outputs found
Personalized Degrees: Effects on Link Formation in Dynamic Networks from an Egocentric Perspective
Understanding mechanisms driving link formation in dynamic social networks is
a long-standing problem that has implications to understanding social structure
as well as link prediction and recommendation. Social networks exhibit a high
degree of transitivity, which explains the successes of common neighbor-based
methods for link prediction. In this paper, we examine mechanisms behind link
formation from the perspective of an ego node. We introduce the notion of
personalized degree for each neighbor node of the ego, which is the number of
other neighbors a particular neighbor is connected to. From empirical analyses
on four on-line social network datasets, we find that neighbors with higher
personalized degree are more likely to lead to new link formations when they
serve as common neighbors with other nodes, both in undirected and directed
settings. This is complementary to the finding of Adamic and Adar that neighbor
nodes with higher (global) degree are less likely to lead to new link
formations. Furthermore, on directed networks, we find that personalized
out-degree has a stronger effect on link formation than personalized in-degree,
whereas global in-degree has a stronger effect than global out-degree. We
validate our empirical findings through several link recommendation experiments
and observe that incorporating both personalized and global degree into link
recommendation greatly improves accuracy.Comment: To appear at the 10th International Workshop on Modeling Social Media
co-located with the Web Conference 201
Inferring latent network from cascade data for dynamic social recommendation
© 2016 IEEE. Social recommendation explores social information to improve the quality of a recommender system. It can be further divided into explicit and implicit social network recommendation. The former assumes the existence of explicit social connections between users in addition to the rating data. The latter one assumes the availability of only the ratings but not the social connections between users since the explicit social information data may not necessarily be available and usually are binary decision values (e.g., whether two people are friends), while the strength of their relationships is missing. Most of the works in this field use only rating data to infer the latent social networks. They ignore the dynamic nature of users that the preferences of users drift over time distinctly. To this end, we propose a new Implicit Dynamic Social Recommendation (IDSR) model, which infers latent social network from cascade data. It can sufficiently mine the information contained in time by mining the cascade data and identify the dynamic changes in the users in time by using the latest updated social network to make recommendations. Experiments and comparisons on three real-world datasets show that the proposed model outperforms the state-of-The-Art solutions in both explicit and implicit scenarios
Social networking and dental care: State of the art and analysis of the impact on dentists, dental practices and their patients
Health Social networking sites offering search, reviews and recommendation are gaining popularity. This paper reviews the most popular social networking sites related to dental care. Social networks such as DrOogle and Yelp enable their users to review and rate their dentists and dental practices. Such information is then used to rank and recommend dentists or dental practices to new users/patients. This paper compares the dental care social networking sites in terms of their features and criteria supported for search, reviews and recommendations of dentists or dental practices. Mismatches between features and criteria among different dental care reviews sites are identified, which may cause inconsistency in the recommendations in the dental care. Therefore, this paper proposes a new framework for dynamic dental care recommendation system which takes both local (personalised) and global (crowdsourced) trust into account. It analyses the impact of current social networks on dentists, dental practices and their patients. Finally, it identifies the open issues and challenges that need to be addressed to design a trustworthy recommendation system for both the dental professionals and their patients
Understanding and Predicting Delay in Reciprocal Relations
Reciprocity in directed networks points to user's willingness to return
favors in building mutual interactions. High reciprocity has been widely
observed in many directed social media networks such as following relations in
Twitter and Tumblr. Therefore, reciprocal relations between users are often
regarded as a basic mechanism to create stable social ties and play a crucial
role in the formation and evolution of networks. Each reciprocity relation is
formed by two parasocial links in a back-and-forth manner with a time delay.
Hence, understanding the delay can help us gain better insights into the
underlying mechanisms of network dynamics. Meanwhile, the accurate prediction
of delay has practical implications in advancing a variety of real-world
applications such as friend recommendation and marketing campaign. For example,
by knowing when will users follow back, service providers can focus on the
users with a potential long reciprocal delay for effective targeted marketing.
This paper presents the initial investigation of the time delay in reciprocal
relations. Our study is based on a large-scale directed network from Tumblr
that consists of 62.8 million users and 3.1 billion user following relations
with a timespan of multiple years (from 31 Oct 2007 to 24 Jul 2013). We reveal
a number of interesting patterns about the delay that motivate the development
of a principled learning model to predict the delay in reciprocal relations.
Experimental results on the above mentioned dynamic networks corroborate the
effectiveness of the proposed delay prediction model.Comment: 10 page
Recommendations on the Internet of Things: Requirements, Challenges, and Directions
© 1997-2012 IEEE. The Internet of Things (IoT) is accelerating the growth of data available on the Internet, which makes the traditional search paradigms incapable of digging the information that people need from massive and deep resources. Furthermore, given the dynamic nature of organizations, social structures, and devices involved in IoT environments, intelligent and automated approaches become critical to support decision makers with the knowledge derived from the vast amount of information available through IoT networks. Indeed, IoT is more desirable of an effective and efficient paradigm of proactive discovering rather than postactive searching. This paper discusses some of the important requirements and key challenges to enable effective and efficient thing-of-interest recommendation and provides an array of new perspectives on IoT recommendation
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
XEL Group Learning – A Socio-technical Framework for Self-regulated Learning
We describe XEL-Group Learning, a socio-technical framework for socially oriented e-learning. The aim of the presented framework is to address the lack of holistic pedagogical solutions that take into account motivational theories, socio–technical factors, and cultural elements in social learning networks. The presented framework provides initiatives for collaboration by providing a dynamic psycho-pedagogical recommendation mechanism with validation properties. In this paper, we begin by highlighting the socio-technical concept associated with socially-oriented e-learning. Next, we describe XEL-GL’s main mechanisms such as group formation and the semantic matching framework. Moreover, through semantic similarity measurements, we show how cultural elements, such as the learning subject, can enhance the quality of recommendations by allowing for more accurate predictions of friends networks
Time-aware metric embedding with asymmetric projection for successive POI recommendation
© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Successive Point-of-Interest (POI) recommendation aims to recommend next POIs for a given user based on this user’s current location. Indeed, with the rapid growth of Location-based Social Networks (LBSNs), successive POI recommendation has become an important and challenging task, since it can help to meet users’ dynamic interests based on their recent check-in behaviors. While some efforts have been made for this task, most of them do not capture the following properties: 1) The transition between consecutive POIs in user check-in sequences presents asymmetric property, however existing approaches usually assume the forward and backward transition probabilities between a POI pair are symmetric. 2) Users usually prefer different successive POIs at different time, but most existing studies do not consider this dynamic factor. To this end, in this paper, we propose a time-aware metric embedding approach with asymmetric projection (referred to as MEAP-T) for successive POI recommendation, which takes the above two properties into consideration. In addition, we exploit three latent Euclidean spaces to project the POI-POI, POI-user, and POI-time relationships. Finally, the experimental results on two real-world datasets show MEAP-T outperforms the state-of-the-art methods in terms of both precision and recall
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