31,832 research outputs found
User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy
Recommender systems have become an integral part of many social networks and
extract knowledge from a user's personal and sensitive data both explicitly,
with the user's knowledge, and implicitly. This trend has created major privacy
concerns as users are mostly unaware of what data and how much data is being
used and how securely it is used. In this context, several works have been done
to address privacy concerns for usage in online social network data and by
recommender systems. This paper surveys the main privacy concerns, measurements
and privacy-preserving techniques used in large-scale online social networks
and recommender systems. It is based on historical works on security,
privacy-preserving, statistical modeling, and datasets to provide an overview
of the technical difficulties and problems associated with privacy preserving
in online social networks.Comment: 26 pages, IET book chapter on big data recommender system
Assessing and improving recommender systems to deal with user cold-start problem
Recommender systems are in our everyday life. The recommendation methods have as
main purpose to predict preferences for new items based on userŠs past preferences. The
research related to this topic seeks among other things to discuss user cold-start problem,
which is the challenge of recommending to users with few or no preferences records.
One way to address cold-start issues is to infer the missing data relying on side information.
Side information of different types has been explored in researches. Some
studies use social information combined with usersŠ preferences, others user click behavior,
location-based information, userŠs visual perception, contextual information, etc. The
typical approach is to use side information to build one prediction model for each cold
user. Due to the inherent complexity of this prediction process, for full cold-start user in
particular, the performance of most recommender systems falls a great deal. We, rather,
propose that cold users are best served by models already built in system.
In this thesis we propose 4 approaches to deal with user cold-start problem using
existing models available for analysis in the recommender systems. We cover the follow
aspects:
o Embedding social information into traditional recommender systems: We investigate
the role of several social metrics on pairwise preference recommendations and
provide the Ąrst steps towards a general framework to incorporate social information
in traditional approaches.
o Improving recommendation with visual perception similarities: We extract networks
connecting users with similar visual perception and use them to come up with
prediction models that maximize the information gained from cold users.
o Analyzing the beneĄts of general framework to incorporate networked information
into recommender systems: Representing different types of side information as a
user network, we investigated how to incorporate networked information into recommender
systems to understand the beneĄts of it in the context of cold user
recommendation.
o Analyzing the impact of prediction model selection for cold users: The last proposal
consider that without side information the system will recommend to cold users
based on the switch of models already built in system.
We evaluated the proposed approaches in terms of prediction quality and ranking
quality in real-world datasets under different recommendation domains. The experiments
showed that our approaches achieve better results than the comparison methods.Tese (Doutorado)Sistemas de recomendação fazem parte do nosso dia-a-dia. Os métodos usados nesses
sistemas tem como objetivo principal predizer as preferências por novos itens baseado no
perĄl do usuário. As pesquisas relacionadas a esse tópico procuram entre outras coisas
tratar o problema do cold-start do usuário, que é o desaĄo de recomendar itens para
usuários que possuem poucos ou nenhum registro de preferências no sistema.
Uma forma de tratar o cold-start do usuário é buscar inferir as preferências dos usuários
a partir de informações adicionais. Dessa forma, informações adicionais de diferentes tipos
podem ser exploradas nas pesquisas. Alguns estudos usam informação social combinada
com preferências dos usuários, outros se baseiam nos clicks ao navegar por sites Web,
informação de localização geográĄca, percepção visual, informação de contexto, etc. A
abordagem típica desses sistemas é usar informação adicional para construir um modelo
de predição para cada usuário. Além desse processo ser mais complexo, para usuários
full cold-start (sem preferências identiĄcadas pelo sistema) em particular, a maioria dos
sistemas de recomendação apresentam um baixo desempenho. O trabalho aqui apresentado,
por outro lado, propõe que novos usuários receberão recomendações mais acuradas
de modelos de predição que já existem no sistema.
Nesta tese foram propostas 4 abordagens para lidar com o problema de cold-start
do usuário usando modelos existentes nos sistemas de recomendação. As abordagens
apresentadas trataram os seguintes aspectos:
o Inclusão de informação social em sistemas de recomendação tradicional: foram investigados
os papéis de várias métricas sociais em um sistema de recomendação de
preferências pairwise fornecendo subsidíos para a deĄnição de um framework geral
para incluir informação social em abordagens tradicionais.
o Uso de similaridade por percepção visual: usando a similaridade por percepção
visual foram inferidas redes, conectando usuários similares, para serem usadas na
seleção de modelos de predição para novos usuários.
o Análise dos benefícios de um framework geral para incluir informação de redes
de usuários em sistemas de recomendação: representando diferentes tipos de informação
adicional como uma rede de usuários, foi investigado como as redes de
usuários podem ser incluídas nos sistemas de recomendação de maneira a beneĄciar
a recomendação para usuários cold-start.
o Análise do impacto da seleção de modelos de predição para usuários cold-start:
a última abordagem proposta considerou que sem a informação adicional o sistema
poderia recomendar para novos usuários fazendo a troca entre os modelos já
existentes no sistema e procurando aprender qual seria o mais adequado para a
recomendação.
As abordagens propostas foram avaliadas em termos da qualidade da predição e da
qualidade do ranking em banco de dados reais e de diferentes domínios. Os resultados
obtidos demonstraram que as abordagens propostas atingiram melhores resultados que os
métodos do estado da arte
Wearing Many (Social) Hats: How Different are Your Different Social Network Personae?
This paper investigates when users create profiles in different social
networks, whether they are redundant expressions of the same persona, or they
are adapted to each platform. Using the personal webpages of 116,998 users on
About.me, we identify and extract matched user profiles on several major social
networks including Facebook, Twitter, LinkedIn, and Instagram. We find evidence
for distinct site-specific norms, such as differences in the language used in
the text of the profile self-description, and the kind of picture used as
profile image. By learning a model that robustly identifies the platform given
a user's profile image (0.657--0.829 AUC) or self-description (0.608--0.847
AUC), we confirm that users do adapt their behaviour to individual platforms in
an identifiable and learnable manner. However, different genders and age groups
adapt their behaviour differently from each other, and these differences are,
in general, consistent across different platforms. We show that differences in
social profile construction correspond to differences in how formal or informal
the platform is.Comment: Accepted at the 11th International AAAI Conference on Web and Social
Media (ICWSM17
Mobility is the Message: Experiments with Mobile Media Sharing
This thesis explores new mobile media sharing applications by building, deploying, and studying their use. While we share media in many different ways both on the web and on mobile phones, there are few ways of sharing media with people physically near us. Studied were three designed and built systems: Push!Music, Columbus, and Portrait Catalog, as well as a fourth commercially available system – Foursquare. This thesis offers four contributions: First, it explores the design space of co-present media sharing of four test systems. Second, through user studies of these systems it reports on how these come to be used. Third, it explores new ways of conducting trials as the technical mobile landscape has changed. Last, we look at how the technical solutions demonstrate different lines of thinking from how similar solutions might look today.
Through a Human-Computer Interaction methodology of design, build, and study, we look at systems through the eyes of embodied interaction and examine how the systems come to be in use. Using Goffman’s understanding of social order, we see how these mobile media sharing systems allow people to actively present themselves through these media. In turn, using McLuhan’s way of understanding media, we reflect on how these new systems enable a new type of medium distinct from the web centric media, and how this relates directly to mobility.
While media sharing is something that takes place everywhere in western society, it is still tied to the way media is shared through computers. Although often mobile, they do not consider the mobile settings. The systems in this thesis treat mobility as an opportunity for design. It is still left to see how this mobile media sharing will come to present itself in people’s everyday life, and when it does, how we will come to understand it and how it will transform society as a medium distinct from those before. This thesis gives a glimpse at what this future will look like
IMPROVING COLLABORATIVE FILTERING RECOMMENDER BY USING MULTI-CRITERIA RATING AND IMPLICIT SOCIAL NETWORKS TO RECOMMEND RESEARCH PAPERS
Research paper recommender systems (RSs) aim to alleviate the information overload of researchers by suggesting relevant and useful papers. The collaborative filtering in the area of recommending research papers can benefit by using richer user feedback data through multi-criteria rating, and by integrating richer social network data into the recommender algorithm.
Existing approaches using collaborative filtering or hybrid approaches typically allow only one rating criterion (overall liking) for users to evaluate papers. We conducted a qualitative study using focus group to explore the most important criteria for rating research papers that can be used to control the paper recommendation by enabling users to set the weight for each criterion. We investigated also the effect of using different rating criteria on the user interface design and how the user can control the weight of the criteria. We followed that by a quantitative study using a questionnaire to validate our findings from the focus group and to find if the chosen criteria are domain independent.
Combining social network information with collaborative filtering recommendation algorithms has successfully reduced some of the drawbacks of collaborative filtering and increased the accuracy of recommendations. All existing recommendation approaches that combine social network information with collaborative filtering in this domain have used explicit social relations that are initiated by users (e.g. “friendship”, “following”). The results have shown that the recommendations produced using explicit social relations cannot compete with traditional collaborative filtering and suffer from the low user coverage. We argue that the available data in social bookmarking Web sites can be exploited to connect similar users using implicit social connections based on their bookmarking behavior. We explore the implicit social relations between users in social bookmarking Web sites (such as CiteULike and Mendeley), and propose three different implicit social networks to recommend relevant papers to users: readership, co-readership and tag-based implicit social networks. First, for each network, we tested the interest similarities of users who are connected using the proposed implicit social networks and compare them with the interest similarities using two explicit social networks: co-authorship and friendship. We found that the readership implicit social network connects users with more similarities than users who are connected using co-authorship and friendship explicit social networks. Then, we compare the recommendation using three different recommendation approaches and implicit social network alone with the recommendation using implicit and explicit social network. We found that fusing recommendation from implicit and explicit social networks can increase the prediction accuracy, and user coverage. The trade-off between the prediction accuracy and diversity was also studied with different social distances between users. The results showed that the diversity of the recommended list increases with the increase of social distance.
To summarize, the main contributions of this dissertation to the area of research paper recommendation are two-fold. It is the first to explore the use of multi-criteria rating for research papers. Secondly, it proposes and evaluates a novel approach to improve collaborative filtering in both prediction accuracy (performance) and user coverage and diversity (nonperformance measures) in social bookmarking systems for sharing research papers, by defining and exploiting several implicit social networks from usage data that is widely available
Recommendations based on social links
The goal of this chapter is to give an overview of recent works on the development of social link-based recommender systems and to offer insights on related issues, as well as future directions for research. Among several kinds of social recommendations, this chapter focuses on recommendations, which are based on users’ self-defined (i.e., explicit) social links and suggest items, rather than people of interest. The chapter starts by reviewing the needs for social link-based recommendations and studies that explain the viability of social networks as useful information sources. Following that, the core part of the chapter dissects and examines modern research on social link-based recommendations along several dimensions. It concludes with a discussion of several important issues and future directions for social link-based recommendation research
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