@inproceedings{Barbieri:2014:FWL:2623330.2623733,
author = {Barbieri, Nicola and Bonchi, Francesco and Manco,
Giuseppe},
title = {Who to Follow and Why: Link Prediction with
Explanations},
booktitle = {Proceedings of the 20th ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining},
series = {KDD '14},
year = {2014},
isbn = {978-1-4503-2956-9},
location = {New York, New York, USA},
pages = {1266--1275},
numpages = {10},
url = {http://www.francescobonchi.com/frp1266-barbieri.pdf},
doi = {10.1145/2623330.2623733},
keywords = {link prediction, social networks},
abstract = {User recommender systems are a key component in any
on-line social networking platform: they help the
users growing their network faster, thus driving
engagement and loyalty. In this paper we study link
prediction with explanations for user recommendation
in social networks. For this problem we propose WTFW
("Who to Follow and Why"), a stochastic topic model
for link prediction over directed and
nodes-attributed graphs. Our model not only predicts
links, but for each predicted link it decides
whether it is a "topical" or a "social" link, and
depending on this decision it produces a different
type of explanation. A topical link is recommended
between a user interested in a topic and a user
authoritative in that topic: the explanation in this
case is a set of binary features describing the
topic responsible of the link creation. A social
link is recommended between users which share a
large social neighborhood: in this case the
explanation is the set of neighbors which are more
likely to be responsible for the link creation. Our
experimental assessment on real-world data confirms
the accuracy of WTFW in the link prediction and the
quality of the associated explanations.}
}
@inproceedings{DBLP:conf/pkdd/CostaMO14,
author = {Gianni Costa and Giuseppe Manco and Riccardo Ortale},
title = {A Generative Bayesian Model for Item and User
Recommendation in Social Rating Networks with Trust
Relationships},
booktitle = {Machine Learning and Knowledge Discovery in
Databases - European Conference, {ECML} {PKDD} 2014,
Nancy, France, September 15-19, 2014. Proceedings,
Part {I}},
year = {2014},
pages = {258--273},
url = {https://www.researchgate.net/publication/265856499_A_Generative_Bayesian_Model_for_Item_and_User_Recommendation_in_Social_Rating_Networks_with_Trust_Relationships},
doi = {10.1007/978-3-662-44848-9_17},
abstract = {A Bayesian generative model is presented for
recommending interesting items and trustworthy users
to the targeted users in social rating networks with
asymmetric and directed trust relationships. The
proposed model is the first unified approach to the
combination of the two recommendation tasks. Within
the devised model, each user is associated with two
latent-factor vectors, i.e., her susceptibility and
expertise. Items are also associated with
corresponding latent-factor vector
representations. The probabilistic factorization of
the rating data and trust relationships is exploited
to infer user susceptibility and expertise.
Statistical social-network modeling is instead used
to constrain the trust relationships from a user to
another to be governed by their respective
susceptibility and expertise. The inherently
ambiguous meaning of unobserved trust relationships
between users is suitably disambiguated. An
intensive comparative experimentation on real-world
social rating networks with trust relationships
demonstrates the superior predictive performance of
the presented model in terms of RMSE and AUC.}
}
@inproceedings{DBLP:conf/icdm/BarbieriBM13,
author = {Nicola Barbieri and Francesco Bonchi and Giuseppe
Manco},
title = {Influence-based Network-oblivious Community Detection},
booktitle = {13th IEEE International Conference on Data Mining,
ICDM 2013, Dallas TX, December 7-10, 2013},
year = 2013,
pages = {955-960},
url = {https://www.dropbox.com/s/2mzraqexh3b1kb9/icdm2013_cr.pdf?dl=0},
abstract = {How can we detect communities when the social graphs
is not available? We tackle this problem by modeling
social contagion from a log of user activity, that
is a dataset of tuples (u, i, t) recording the fact
that user u "adopted" item i at time t. This is the
only input to our problem. We propose a stochastic
framework which assumes that item adoptions are
governed by un underlying diffusion process over the
unobserved social network, and that such diffusion
model is based on community-level influence. By
fitting the model parameters to the user activity
log, we learn the community membership and the level
of influence of each user in each community. This
allows to identify for each community the "key"
users, i.e., the leaders which are most likely to
influence the rest of the community to adopt a
certain item. The general framework can be
instantiated with different diffusion models. In
this paper we define two models: the extension to
the community level of the classic (discrete time)
Independent Cascade model, and a model that focuses
on the time delay between adoptions. To the best of
our knowledge, this is the first work studying
community detection without the network. }
}
@inproceedings{DBLP:conf/wsdm/BarbieriBM13,
author = {Nicola Barbieri and Francesco Bonchi and Giuseppe
Manco},
title = {Cascade-based community detection},
booktitle = {Sixth ACM International Conference on Web Search and
Data Mining, WSDM 2013, Rome, Italy, February 4-8,
2013},
year = {2013},
publisher = {ACM},
pages = {33-42},
url = {https://www.dropbox.com/s/bwj4i5rncb6hpjd/wsdm13_final.pdf?dl=0},
abstract = {Given a directed social graph and a set of past
informa- tion cascades observed over the graph, we
study the novel problem of detecting modules of the
graph (communities of nodes), that also explain the
cascades. Our key observation is that both
information propagation and social ties forma- tion
in a social network can be explained according to
the same latent factor, which ultimately guide a
user behavior within the network. Based on this
observation, we propose the Community-Cascade
Network (CCN) model, a stochas- tic mixture
membership generative model that can fit, at the
same time, the social graph and the observed set of
cas- cades. Our model produces overlapping
communities and for each node, its level of
authority and passive interest in each community it
belongs. For learning the parameters of the CCN
model, we devise a Generalized Expectation
Maximization procedure. We then apply our model to
real-world social networks and in- formation
cascades: the results witness the validity of the
proposed CCN model, providing useful insights on its
signif- icance for analyzing social behavior. }
}
@inproceedings{DBLP:conf/ic3k/BarbieriBCMR12,
author = {Nicola Barbieri and Antonio Bevacqua and Marco
Carnuccio and Giuseppe Manco and Ettore Ritacco},
title = {Probabilistic Sequence Modeling for Recommender
Systems},
booktitle = {KDIR 2012 - Proceedings of the International
Conference on Knowledge Discovery and Information
Retrieval, Barcelona, Spain, 4 - 7 October, 2012},
year = {2012},
pages = {75-84},
url = {https://www.dropbox.com/s/qgljal7gezvt27q/kdir2012_final.pdf?dl=0},
abstract = {Probabilistic topic models are widely used in
different contexts to uncover the hidden structure
in large text corpora. One of the main features of
these models is that generative process follows a
bag-of-words assump- tion, i.e each token is
independent from the previous one. We extend the
popular Latent Dirichlet Allocation model by
exploiting a conditional Markovian assumptions,
where the token generation depends on the cur- rent
topic and on the previous token. The resulting model
is capable of accommodating temporal correlations
among tokens, which better model user behavior. This
is particularly significant in a collaborative
filtering context, where the choice of a user can be
exploited for recommendation purposes, and hence a
more re- alistic and accurate modeling enables
better recommendations. For the mentioned model we
present a fast Gibbs Sampling procedure for the
parameters estimation. A thorough experimental
evaluation over real-word data shows the performance
advantages, in terms of recall and precision, of the
proposed sequence-modeling approach. }
}
@inproceedings{DBLP:conf/icdm/BarbieriBM12,
author = {Nicola Barbieri and Francesco Bonchi and Giuseppe
Manco},
title = {Topic-Aware Social Influence Propagation Models},
booktitle = {12th IEEE International Conference on Data Mining,
ICDM 2012, Brussels, Belgium, December 10-13, 2012},
year = 2012,
pages = {81-90},
url = {https://www.dropbox.com/s/rnns0yiyy24tair/icdm2012.pdf?dl=0},
abstract = {We study social influence from a topic modeling
perspective. We introduce novel topic-aware
influence-driven propagation models that
experimentally result to be more ac- curate in
describing real-world cascades than the standard
propagation models studied in the literature. In
particular, we first propose simple topic-aware
extensions of the well-known Independent Cascade and
Linear Threshold models. Next, we propose a
different approach explicitly modeling
authoritative- ness, influence and relevance under a
topic-aware perspective. We devise methods to learn
the parameters of the models from a dataset of past
propagations. Our experimentation confirms the high
accuracy of the proposed models and learning
schemes.}
}
@inproceedings{DBLP:conf/sdm/BarbieriMOR12,
author = {Nicola Barbieri and Giuseppe Manco and Riccardo
Ortale and Ettore Ritacco},
title = {Balancing Prediction and Recommendation Accuracy:
Hierarchical Latent Factors for Preference Data},
booktitle = {Proceedings of the Twelfth SIAM International
Conference on Data Mining, Anaheim, California, USA,
April 26-28, 2012},
publisher = {SIAM / Omnipress},
year = {2012},
pages = {1035-1046},
url = {http://siam.omnibooksonline.com/2012datamining/data/papers/049.pdf},
abstract = {Recent works in Recommender Systems (RS) have investigated the relationships between the prediction
accuracy, i.e. the ability of a RS to minimize a
cost function (for instance the RMSE measure) in
estimating users' preferences, and the accuracy of
the recommendation list provided to
users. State-of-the-art recommendation algorithms,
which focus on the minimization of RMSE, have shown
to achieve weak results from the recommendation
accuracy perspective, and vice versa. In this work
we present a novel Bayesian probabilistic hierarchical approach for users' preference data,
which is designed to overcome the limitation of
current method- ologies and thus to meet both
prediction and recommendation accuracy. According
to the generative semantics of this technique, each
user is modeled as a random mixture over latent
factors, which identify users community
interests. Each individual user community is then
modeled as a mixture of topics, which capture the
preferences of the members on a set of items. We
provide two different formalization of the basic
hierarchical model: BH-Forced focuses on rating
prediction, while BH-Free models both the popularity
of items and the distribution over item
ratings. The combined modeling of item popularity
and rating provides a powerful framework for the
generation of highly accurate recommendations. An
extensive evaluation over two popular benchmark
datasets reveals the effectiveness and the quality
of the proposed algorithms, showing that BH-Free
realizes the most satisfactory compromise between
prediction and recommendation accuracy with respect
to several state- of-the-art competitors.}
}
@inproceedings{DBLP:conf/pkdd/BarbieriM11,
author = {Nicola Barbieri and Giuseppe Manco},
title = {An Analysis of Probabilistic Methods for Top-N
Recommendation in Collaborative Filtering},
booktitle = {Machine Learning and Knowledge Discovery in
Databases - European Conference, ECML PKDD 2011,
Athens, Greece, September 5-9, 2011},
year = {2011},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = {6911},
pages = {172-187},
url = {https://www.dropbox.com/s/4t31pgus91ryjb7/pkdd2011.pdf?dl=0},
abstract = {In this work we perform an analysis of probabilistic
approaches to recommendation upon a different
validation perspective, which focuses on accuracy
metrics such as recall and precision of the
recommendation list. Traditionally, state-of-art
approches to recommen- dations consider the
recommendation process from a “missing value pre-
diction” perspective. This approach simplifies the
model validation phase that is based on the
minimization of standard error metrics such as
RMSE. However, recent studies have pointed several
limitations of this approach, showing that a lower
RMSE does not necessarily imply im- provements in
terms of specific recommendations. We demonstrate
that the underlying probabilistic framework offers
several advantages over tra- ditional methods, in
terms of flexibility in the generation of the recom-
mendation list and consequently in the accuracy of
recommendation.}
}
@inproceedings{DBLP:conf/ic3k/BarbieriCMR11,
author = {Nicola Barbieri and Gianni Costa and Giuseppe Manco
and Ettore Ritacco},
title = {Characterizing Relationships through Co-clustering -
A Probabilistic Approach},
booktitle = {KDIR 2011 - Proceedings of the International
Conference on Knowledge Discovery and Information
Retrieval, Paris, France, 26-29 October, 2011},
year = {2011},
publisher = {SciTePress},
pages = {64-73},
url = {https://www.dropbox.com/s/ch2s4nf1dxz6ogp/kdir2011_final.pdf?dl=0},
abstract = {In this paper we propose a probabilistic
co-clustering approach for pattern discovery in
collaborative filtering data. We extend the Block
Mixture Model in order to learn about the structures
and relationships within pref- erence data. The
resulting model can simultaneously cluster users
into communities and items into categories. Besides
its predictive capabilities, the model enables the
discovery of significant knowledge patterns, such as
the analysis of common trends and relationships
between items and users within
communities/categories. We reformulate the
mathematical model and implement a parameter
estimation technique. Next, we show how the model
parameters enable pattern discovery tasks, namely:
(i) to infer topics for each items category and
characteristic items for each user community; (ii)
to model community interests and transitions among
topics. Experiments on MovieLens data provide
evidence about the effectiveness of the proposed
approach.}
}
@inproceedings{DBLP:conf/recsys/BarbieriCMO11,
author = {Nicola Barbieri and Gianni Costa and Giuseppe Manco
and Riccardo Ortale},
title = {Modeling item selection and relevance for accurate
recommendations: a bayesian approach},
booktitle = {Proceedings of the 2011 ACM Conference on
Recommender Systems, RecSys 2011, Chicago, IL, USA,
October 23-27, 2011},
year = {2011},
pages = {21-28},
url = {https://www.dropbox.com/s/c31iw8b1esrii1z/RecSys2011_final.pdf?dl=0},
abstract = {We propose a bayesian probabilistic model for
explicit preference data. The model introduces a
generative process, which takes into account both
item selection and rating emission to gather into
communities those users who ex- perience the same
items and tend to adopt the same rating
pattern. Each user is modeled as a random mixture of
topics, where each topic is characterized by a
distribu- tion modeling the popularity of items
within the respective user-community and by a
distribution over preference val- ues for those
items. The proposed model can be associated with a
novel item-relevance ranking criterion, which is
based both on item popularity and user’s
preferences. We show that the proposed model,
equipped with the new ranking criterion, outperforms
state-of-art approaches in terms of accuracy of the
recommendation list provided to users on standard
benchmark datasets.}
}
@inproceedings{DBLP:conf/sdm/BarbieriMR11,
author = {Nicola Barbieri and Giuseppe Manco and Ettore
Ritacco},
title = {A Probabilistic Hierarchical Approach for Pattern
Discovery in Collaborative Filtering Data},
booktitle = {Proceedings of the Eleventh SIAM International
Conference on Data Mining, SDM 2011, April 28-30,
2011, Mesa, Arizona, USA},
year = {2011},
pages = {630-621},
publisher = {SIAM / Omnipress},
url = {http://siam.omnibooksonline.com/2011datamining/data/papers/163.pdf},
abstract = {This paper presents a hierarchical probabilistic approach to collaborative filtering which allows the
discovery and analysis of both global patterns
(i.e., tendency of some products of being
`universally appreciated') and local patterns (tendency of users within a community to express a
common preference on the same group of items). We
reformulate the collaborative filtering approach
as a clustering problem in a high-dimensional
setting, and propose a probabilistic approach to
model the data. The core of our approach is a
co-clustering strategy, arranged in a hierarchical
fashion: first, user communities are discovered, and
then the information provided by each user community
is used to discover topics, grouping items into
categories. The resulting probabilistic framework
can be used for detecting interesting
relationships between users and items within user
communities. The experimental evaluation shows that
the proposed model achieves a competitive prediction
accuracy with respect to the state-of-art
collaborative filtering approaches.}
}
This file was generated by bibtex2html 1.96.