73 research outputs found
Tag-Aware Recommender Systems: A State-of-the-art Survey
In the past decade, Social Tagging Systems have attracted increasing
attention from both physical and computer science communities. Besides the
underlying structure and dynamics of tagging systems, many efforts have been
addressed to unify tagging information to reveal user behaviors and
preferences, extract the latent semantic relations among items, make
recommendations, and so on. Specifically, this article summarizes recent
progress about tag-aware recommender systems, emphasizing on the contributions
from three mainstream perspectives and approaches: network-based methods,
tensor-based methods, and the topic-based methods. Finally, we outline some
other tag-related works and future challenges of tag-aware recommendation
algorithms.Comment: 19 pages, 3 figure
Collective dynamics of social annotation
The enormous increase of popularity and use of the WWW has led in the recent
years to important changes in the ways people communicate. An interesting
example of this fact is provided by the now very popular social annotation
systems, through which users annotate resources (such as web pages or digital
photographs) with text keywords dubbed tags. Understanding the rich emerging
structures resulting from the uncoordinated actions of users calls for an
interdisciplinary effort. In particular concepts borrowed from statistical
physics, such as random walks, and the complex networks framework, can
effectively contribute to the mathematical modeling of social annotation
systems. Here we show that the process of social annotation can be seen as a
collective but uncoordinated exploration of an underlying semantic space,
pictured as a graph, through a series of random walks. This modeling framework
reproduces several aspects, so far unexplained, of social annotation, among
which the peculiar growth of the size of the vocabulary used by the community
and its complex network structure that represents an externalization of
semantic structures grounded in cognition and typically hard to access
Addressing the cold start problem in tag-based recommender systems
Folksonomies have become a powerful tool to describe, discover, search, and navigate
online resources (e.g., pictures, videos, blogs) on the Social Web. Unlike taxonomies and
ontologies, which impose a hierarchical categorisation on content, folksonomies directly
allow end users to freely create and choose the categories (in this case, tags) that best
describe a piece of information. However, the freedom aafforded to users comes at a cost:
as tags are defined informally, the retrieval of information becomes more challenging.
Different solutions have been proposed to help users discover content in this highly dynamic
setting. However, they have proved to be effective only for users who have already heavily
used the system (active users) and who are interested in popular items (i.e., items tagged
by many other users).
In this thesis we explore principles to help both active users and more importantly new or
inactive users (cold starters) to find content they are interested in even when this content
falls into the long tail of medium-to-low popularity items (cold start items). We investigate
the tagging behaviour of users on content and show how the similarities between users and
tags can be used to produce better recommendations. We then analyse how users create
new content on social tagging websites and show how preferences of only a small portion
of active users (leaders), responsible for the vast majority of the tagged content, can be
used to improve the recommender system's scalability. We also investigate the growth of
the number of users, items and tags in the system over time. We then show how this
information can be used to decide whether the benefits of an update of the data structures
modelling the system outweigh the corresponding cost.
In this work we formalize the ideas introduced above and we describe their implementation.
To demonstrate the improvements of our proposal in recommendation efficacy and
efficiency, we report the results of an extensive evaluation conducted on three different
social tagging websites: CiteULike, Bibsonomy and MovieLens. Our results demonstrate
that our approach achieves higher accuracy than state-of-the-art systems for cold start
users and for users searching for cold start items. Moreover, while accuracy of our technique
is comparable to other techniques for active users, the computational cost that it
requires is much smaller. In other words our approach is more scalable and thus more
suitable for large and quickly growing settings
Using Data Mining for Facilitating User Contributions in the Social Semantic Web
This thesis utilizes recommender systems to aid the user in contributing to the Social Semantic Web. In this work, we propose a framework that maps domain properties to recommendation technologies. Next, we develop novel recommendation algorithms for improving personalized tag recommendation and for recommendation of semantic relations. Finally, we introduce a framework to analyze different types of potential attacks against social tagging systems and evaluate their impact on those systems
Content-awareness and graph-based ranking for tag recommendation in folksonomies
Tag recommendation algorithms aid the social tagging process in many userdriven
document indexing applications, such as social bookmarking and publication
sharing websites. This thesis gives an overview of existing tag recommendation
methods and proposes novel approaches that address the new document problem
and the task of ranking tags. The focus is on graph-based methods such as Folk-
Rank that apply weight spreading algorithms to a graph representation of the folksonomy.
In order to suggest tags for previously untagged documents, extensions are
presented that introduce content into the recommendation process as an additional
information source. To address the problem of ranking tags, an in-depth analysis
of graph models as well as ranking algorithms is conducted. Implicit assumptions
made by the widely-used graph model of the folksonomy are highlighted and an
improved model is proposed that captures the characteristics of the social tagging
data more accurately. Additionally, issues in the tag rank computation of FolkRank
are analysed and an adapted weight spreading approach for social tagging data is
presented. Moreover, the applicability of conventional weight spreading methods to
data from the social tagging domain is examined in detail. Finally, indications of
implicit negative feedback in the data structure of folksonomies are analysed and
novel approaches of identifying negative relationships are presented. By exploiting
the three-dimensional characteristics of social tagging data the proposed metrics are
based on stronger evidence and provide reliable measures of negative feedback.
Including content into the tag recommendation process leads to a significant
increase in recommendation accuracy on real-world datasets. The proposed adaptations
to graph models and ranking algorithms result in more accurate and computationally
less expensive recommenders. Moreover, new insights into the fundamental
characteristics of social tagging data are revealed and a novel data interpretation
that takes negative feedback into account is proposed
Predictive Modeling for Navigating Social Media
Social media changes the way people use the Web. It has transformed ordinary Web users from information consumers to content contributors. One popular form of content contribution is social tagging, in which users assign tags to Web resources. By the collective efforts of the social tagging community, a new information space has been created for information navigation. Navigation allows serendipitous discovery of information by examining the information objects linked to one another in the social tagging space. In this dissertation, we study prediction tasks that facilitate navigation in social tagging systems. For social tagging systems to meet complex navigation needs of users, two issues are fundamental, namely link sparseness and object selection. Link sparseness is observed for many resources that are untagged or inadequately tagged, hindering navigation to the resources. Object selection is concerned when there are a large number of information objects that are linked to the current object, requiring to select the more interesting or relevant ones for guiding navigation effectively. This dissertation focuses on three dimensions, namely the semantic, social and temporal dimensions, to address link sparseness and object selection. To address link sparseness, we study the task of tag prediction. This task aims to enrich tags for the untagged or inadequately tagged resources, such that the predicted tags can serve as navigable links to these resources. For this task, we take a topic modeling approach to exploit the latent semantic relationships between resource content and tags. To address object selection, we study the task of personalized tag recommendation and trend discovery using social annotations. Personalized tag recommendation leverages the collective wisdom from the social tagging community to recommend tags that are semantically relevant to the target resource, while being tailored to the tagging preferences of individual users. For this task, we propose a probabilistic framework which leverages the implicit social links between like-minded users, i.e. who show similar tagging preferences, to recommend suitable tags. Social tags capture the interest of the users in the annotated resources at different times. These social annotations allow us to construct temporal profiles for the annotated resources. By analyzing these temporal profiles, we unveil the non-trivial temporal trends of the annotated resources, which provide novel metrics for selecting relevant and interesting resources for guiding navigation. For trend discovery using social annotations, we propose a trend discovery process which enables us to analyze trends for a multitude of semantics encapsulated in the temporal profiles of the annotated resources
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