696 research outputs found
Contextual Social Networking
The thesis centers around the multi-faceted research question of how contexts may
be detected and derived that can be used for new context aware Social Networking
services and for improving the usefulness of existing Social Networking services, giving
rise to the notion of Contextual Social Networking. In a first foundational part,
we characterize the closely related fields of Contextual-, Mobile-, and Decentralized
Social Networking using different methods and focusing on different detailed
aspects. A second part focuses on the question of how short-term and long-term
social contexts as especially interesting forms of context for Social Networking may
be derived. We focus on NLP based methods for the characterization of social relations
as a typical form of long-term social contexts and on Mobile Social Signal
Processing methods for deriving short-term social contexts on the basis of geometry
of interaction and audio. We furthermore investigate, how personal social agents
may combine such social context elements on various levels of abstraction. The third
part discusses new and improved context aware Social Networking service concepts.
We investigate special forms of awareness services, new forms of social information
retrieval, social recommender systems, context aware privacy concepts and services
and platforms supporting Open Innovation and creative processes.
This version of the thesis does not contain the included publications because of
copyrights of the journals etc. Contact in terms of the version with all included
publications: Georg Groh, [email protected] zentrale Gegenstand der vorliegenden Arbeit ist die vielschichtige Frage, wie Kontexte detektiert und abgeleitet werden können, die dazu dienen können, neuartige kontextbewusste Social Networking Dienste zu schaffen und bestehende Dienste in ihrem Nutzwert zu verbessern. Die (noch nicht abgeschlossene) erfolgreiche Umsetzung dieses Programmes führt auf ein Konzept, das man als Contextual Social Networking bezeichnen kann. In einem grundlegenden ersten Teil werden die eng zusammenhängenden Gebiete Contextual Social Networking, Mobile Social Networking und Decentralized Social Networking mit verschiedenen Methoden und unter Fokussierung auf verschiedene Detail-Aspekte näher beleuchtet und in Zusammenhang gesetzt. Ein zweiter Teil behandelt die Frage, wie soziale Kurzzeit- und Langzeit-Kontexte als für das Social Networking besonders interessante Formen von Kontext gemessen und abgeleitet werden können. Ein Fokus liegt hierbei auf NLP Methoden zur Charakterisierung sozialer Beziehungen als einer typischen Form von sozialem Langzeit-Kontext. Ein weiterer Schwerpunkt liegt auf Methoden aus dem Mobile Social Signal Processing zur Ableitung sinnvoller sozialer Kurzzeit-Kontexte auf der Basis von Interaktionsgeometrien und Audio-Daten. Es wird ferner untersucht, wie persönliche soziale Agenten Kontext-Elemente verschiedener Abstraktionsgrade miteinander kombinieren können. Der dritte Teil behandelt neuartige und verbesserte Konzepte für kontextbewusste Social Networking Dienste. Es werden spezielle Formen von Awareness Diensten, neue Formen von sozialem Information Retrieval, Konzepte für kontextbewusstes Privacy Management und Dienste und Plattformen zur Unterstützung von Open Innovation und Kreativität untersucht und vorgestellt. Diese Version der Habilitationsschrift enthält die inkludierten Publikationen zurVermeidung von Copyright-Verletzungen auf Seiten der Journals u.a. nicht. Kontakt in Bezug auf die Version mit allen inkludierten Publikationen: Georg Groh, [email protected]
Joint Geographical and Temporal Modeling based on Matrix Factorization for Point-of-Interest Recommendation
With the popularity of Location-based Social Networks, Point-of-Interest
(POI) recommendation has become an important task, which learns the users'
preferences and mobility patterns to recommend POIs. Previous studies show that
incorporating contextual information such as geographical and temporal
influences is necessary to improve POI recommendation by addressing the data
sparsity problem. However, existing methods model the geographical influence
based on the physical distance between POIs and users, while ignoring the
temporal characteristics of such geographical influences. In this paper, we
perform a study on the user mobility patterns where we find out that users'
check-ins happen around several centers depending on their current temporal
state. Next, we propose a spatio-temporal activity-centers algorithm to model
users' behavior more accurately. Finally, we demonstrate the effectiveness of
our proposed contextual model by incorporating it into the matrix factorization
model under two different settings: i) static and ii) temporal. To show the
effectiveness of our proposed method, which we refer to as STACP, we conduct
experiments on two well-known real-world datasets acquired from Gowalla and
Foursquare LBSNs. Experimental results show that the STACP model achieves a
statistically significant performance improvement, compared to the
state-of-the-art techniques. Also, we demonstrate the effectiveness of
capturing geographical and temporal information for modeling users' activity
centers and the importance of modeling them jointly.Comment: To be appear in ECIR 202
Tensor Learning for Recovering Missing Information: Algorithms and Applications on Social Media
Real-time social systems like Facebook, Twitter, and Snapchat have been growing
rapidly, producing exabytes of data in different views or aspects. Coupled with more
and more GPS-enabled sharing of videos, images, blogs, and tweets that provide valuable
information regarding “who”, “where”, “when” and “what”, these real-time human
sensor data promise new research opportunities to uncover models of user behavior, mobility,
and information sharing. These real-time dynamics in social systems usually come
in multiple aspects, which are able to help better understand the social interactions of the
underlying network. However, these multi-aspect datasets are often raw and incomplete
owing to various unpredictable or unavoidable reasons; for instance, API limitations and
data sampling policies can lead to an incomplete (and often biased) perspective on these
multi-aspect datasets. This missing data could raise serious concerns such as biased estimations
on structural properties of the network and properties of information cascades in
social networks. In order to recover missing values or information in social systems, we
identify “4S” challenges: extreme sparsity of the observed multi-aspect datasets, adoption
of rich side information that is able to describe the similarities of entities, generation of
robust models rather than limiting them on specific applications, and scalability of models
to handle real large-scale datasets (billions of observed entries). With these challenges
in mind, this dissertation aims to develop scalable and interpretable tensor-based frameworks,
algorithms and methods for recovering missing information on social media. In
particular, this dissertation research makes four unique contributions:
_ The first research contribution of this dissertation research is to propose a scalable
framework based on low-rank tensor learning in the presence of incomplete information.
Concretely, we formally define the problem of recovering the spatio-temporal dynamics of online memes and tackle this problem by proposing a novel tensor-based
factorization approach based on the alternative direction method of multipliers
(ADMM) with the integration of the latent relationships derived from contextual
information among locations, memes, and times.
_ The second research contribution of this dissertation research is to evaluate the generalization
of the proposed tensor learning framework and extend it to the recommendation
problem. In particular, we develop a novel tensor-based approach to
solve the personalized expert recommendation by integrating both the latent relationships
between homogeneous entities (e.g., users and users, experts and experts)
and the relationships between heterogeneous entities (e.g., users and experts, topics
and experts) from the geo-spatial, topical, and social contexts.
_ The third research contribution of this dissertation research is to extend the proposed
tensor learning framework to the user topical profiling problem. Specifically,
we propose a tensor-based contextual regularization model embedded into a matrix
factorization framework, which leverages the social, textual, and behavioral contexts
across users, in order to overcome identified challenges.
_ The fourth research contribution of this dissertation research is to scale up the proposed
tensor learning framework to be capable of handling real large-scale datasets
that are too big to fit in the main memory of a single machine. Particularly, we
propose a novel distributed tensor completion algorithm with the trace-based regularization
of the auxiliary information based on ADMM under the proposed tensor
learning framework, which is designed to scale up to real large-scale tensors (e.g.,
billions of entries) by efficiently computing auxiliary variables, minimizing intermediate
data, and reducing the workload of updating new tensors
A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users
Spatial item recommendation has become an important means to help people
discover interesting locations, especially when people pay a visit to
unfamiliar regions. Some current researches are focusing on modelling
individual and collective geographical preferences for spatial item
recommendation based on users' check-in records, but they fail to explore the
phenomenon of user interest drift across geographical regions, i.e., users
would show different interests when they travel to different regions. Besides,
they ignore the influence of public comments for subsequent users' check-in
behaviors. Specifically, it is intuitive that users would refuse to check in to
a spatial item whose historical reviews seem negative overall, even though it
might fit their interests. Therefore, it is necessary to recommend the right
item to the right user at the right location. In this paper, we propose a
latent probabilistic generative model called LSARS to mimic the decision-making
process of users' check-in activities both in home-town and out-of-town
scenarios by adapting to user interest drift and crowd sentiments, which can
learn location-aware and sentiment-aware individual interests from the contents
of spatial items and user reviews. Due to the sparsity of user activities in
out-of-town regions, LSARS is further designed to incorporate the public
preferences learned from local users' check-in behaviors. Finally, we deploy
LSARS into two practical application scenes: spatial item recommendation and
target user discovery. Extensive experiments on two large-scale location-based
social networks (LBSNs) datasets show that LSARS achieves better performance
than existing state-of-the-art methods.Comment: Accepted by KDD 201
Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches
In recent years, location-based social networks (LBSNs) that allow members to share their location and provide related services, and point-of-interest (POIs) recommendations which suggest attractive places to visit, have become noteworthy and useful for users, research areas, industries, and advertising companies. The POI recommendation system combines different information sources and creates numerous research challenges and questions. New research in this field utilizes deep-learning techniques as a solution to the issues because it has the ability to represent the nonlinear relationship between users and items more effectively than other methods. Despite all the obvious improvements that have been made recently, this field still does not have an updated and integrated view of the types of methods, their limitations, features, and future prospects. This paper provides a systematic review focusing on recent research on this topic. First, this approach prepares an overall view of the types of recommendation methods, their challenges, and the various influencing factors that can improve model performance in POI recommendations, then it reviews the traditional machine-learning methods and deep-learning techniques employed in the POI recommendation and analyzes their strengths and weaknesses. The recently proposed models are categorized according to the method used, the dataset, and the evaluation metrics. It found that these articles give priority to accuracy in comparison with other dimensions of quality. Finally, this approach introduces the research trends and future orientations, and it realizes that POI recommender systems based on deep learning are a promising future work
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