114 research outputs found
Harvesting and Structuring Social Data in Music Information Retrieval
Abstract. An exponentially growing amount of music and sound resources are being shared by communities of users on the Internet. Social media content can be found with different levels of structuring, and the contributing users might be experts or non-experts of the domain. Harvesting and structuring this information semantically would be very useful in context-aware Music Information Retrieval (MIR). Until now, scant research in this field has taken advantage of the use of formal knowledge representations in the process of structuring information. We propose a methodology that combines Social Media Mining, Knowledge Extraction and Natural Language Processing techniques, to extract meaningful context information from social data. By using the extracted information we aim to improve retrieval, discovery and annotation of music and sound resources. We define three different scenarios to test and develop our methodology
Social software for music
Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 200
Profiling user interactions on online social networks.
Over the last couple of years, there has been signi_cant research e_ort in mining user behavior on online social networks for applications ranging from sentiment analysis to marketing. In most of those applications, usually a snapshot of user attributes or user relationships are analyzed to build the data mining models, without considering how user attributes and user relationships can be utilized together.
In this thesis, we will describe how user relationships within a social network can be further augmented by information gathered from user generated texts to analyze large scale dynamics of social networks. Speci_cally, we aim at explaining social network interactions by using information gleaned from friendships, pro_les, and status posts of users. Our approach pro_les user interactions in terms of shared similarities among users, and applies the gained knowledge to help users in understanding the inherent reasons, consequences and bene_ts of interacting with other social network users
state of the art analysis ; working packages in project phase II
In this report, we introduce our goals and present our requirement analysis
for the second phase of the Corporate Semantic Web project. Corporate ontology
engineering will improve the facilitation of agile ontology engineering to
lessen the costs of ontology development and, especially, maintenance.
Corporate semantic collaboration focuses the human-centered aspects of
knowledge management in corporate contexts. Corporate semantic search is
settled on the highest application level of the three research areas and at
that point it is a representative for applications working on and with the
appropriately represented and delivered background knowledge
Toward Geo-social Information Systems: Methods and Algorithms
The widespread adoption of GPS-enabled tagging of social media content via
smartphones and social media services (e.g., Facebook, Twitter, Foursquare) uncovers
a new window into the spatio-temporal activities of hundreds of millions of people.
These \footprints" open new possibilities for understanding how people can organize
for societal impact and lay the foundation for new crowd-powered geo-social systems.
However, there are key challenges to delivering on this promise: the slow adoption
of location sharing, the inherent bias in the users that do share location, imbalanced
location granularity, respecting location privacy, among many others. With these
challenges in mind, this dissertation aims to develop the framework, algorithms, and
methods for a new class of geo-social information systems. The dissertation is structured
in two main parts: the rst focuses on understanding the capacity of existing
footprints; the second demonstrates the potential of new geo-social information systems
through two concrete prototypes.
First, we investigate the capacity of using these geo-social footprints to build new
geo-social information systems. (i): we propose and evaluate a probabilistic framework
for estimating a microblog user's location based purely on the content of the
user's posts. With the help of a classi cation component for automatically identifying
words in tweets with a strong local geo-scope, the location estimator places 51%
of Twitter users within 100 miles of their actual location. (ii): we investigate a set of
22 million check-ins across 220,000 users and report a quantitative assessment of human
mobility patterns by analyzing the spatial, temporal, social, and textual aspects
associated with these footprints. Concretely, we observe that users follow simple reproducible
mobility patterns. (iii): we compare a set of 35 million publicly shared check-ins with a set of over 400 million private query logs recorded by a commercial
hotel search engine. Although generated by users with fundamentally di erent intentions,
we nd common conclusions may be drawn from both data sources, indicating
the viability of publicly shared location information to complement (and replace, in
some cases), privately held location information.
Second, we introduce a couple of prototypes of new geo-social information systems
that utilize the collective intelligence from the emerging geo-social footprints.
Concretely, we propose an activity-driven search system, and a local expert nding
system that both take advantage of the collective intelligence. Speci cally, we study
location-based activity patterns revealed through location sharing services and nd
that these activity patterns can identify semantically related locations, and help with
both unsupervised location clustering, and supervised location categorization with a
high con dence. Based on these results, we show how activity-driven semantic organization
of locations may be naturally incorporated into location-based web search.
In addition, we propose a local expert nding system that identi es top local experts
for a topic in a location. Concretely, the system utilizes semantic labels that people
label each other, people's locations in current location-based social networks, and can
identify top local experts with a high precision. We also observe that the proposed
local authority metrics that utilize collective intelligence from expert candidates' core
audience (list labelers), signi cantly improve the performance of local experts nding
than the more intuitive way that only considers candidates' locations.
ii
Personalized Expert Recommendation: Models and Algorithms
Many large-scale information sharing systems including social media systems, questionanswering
sites and rating and reviewing applications have been growing rapidly, allowing
millions of human participants to generate and consume information on an unprecedented
scale. To manage the sheer growth of information generation, there comes the need to enable
personalization of information resources for users — to surface high-quality content
and feeds, to provide personally relevant suggestions, and so on. A fundamental task in
creating and supporting user-centered personalization systems is to build rich user profile
to aid recommendation for better user experience.
Therefore, in this dissertation research, we propose models and algorithms to facilitate
the creation of new crowd-powered personalized information sharing systems. Specifically,
we first give a principled framework to enable personalization of resources so that
information seekers can be matched with customized knowledgeable users based on their
previous historical actions and contextual information; We then focus on creating rich
user models that allows accurate and comprehensive modeling of user profiles for long
tail users, including discovering user’s known-for profile, user’s opinion bias and user’s
geo-topic profile. In particular, this dissertation research makes two unique contributions:
First, we introduce the problem of personalized expert recommendation and propose
the first principled framework for addressing this problem. To overcome the sparsity issue,
we investigate the use of user’s contextual information that can be exploited to build robust
models of personal expertise, study how spatial preference for personally-valuable expertise
varies across regions, across topics and based on different underlying social communities,
and integrate these different forms of preferences into a matrix factorization-based
personalized expert recommender.
Second, to support the personalized recommendation on experts, we focus on modeling
and inferring user profiles in online information sharing systems. In order to tap
the knowledge of most majority of users, we provide frameworks and algorithms to accurately
and comprehensively create user models by discovering user’s known-for profile,
user’s opinion bias and user’s geo-topic profile, with each described shortly as follows:
—We develop a probabilistic model called Bayesian Contextual Poisson Factorization
to discover what users are known for by others. Our model considers as input a small fraction
of users whose known-for profiles are already known and the vast majority of users for
whom we have little (or no) information, learns the implicit relationships between user?s
known-for profiles and their contextual signals, and finally predict known-for profiles for
those majority of users.
—We explore user’s topic-sensitive opinion bias, propose a lightweight semi-supervised
system called “BiasWatch” to semi-automatically infer the opinion bias of long-tail users,
and demonstrate how user’s opinion bias can be exploited to recommend other users with
similar opinion in social networks.
— We study how a user’s topical profile varies geo-spatially and how we can model
a user’s geo-spatial known-for profile as the last step in our dissertation for creation of
rich user profile. We propose a multi-layered Bayesian hierarchical user factorization to
overcome user heterogeneity and an enhanced model to alleviate the sparsity issue by integrating
user contexts into the two-layered hierarchical user model for better representation
of user’s geo-topic preference by others
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