author = {Barbieri, Nicola and Bonchi, Francesco and Manco,
  title = {Who to Follow and Why: Link Prediction with
  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 = {},
  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.}
  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
  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 = {},
  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.}
  author = {Nicola Barbieri and Francesco Bonchi and Giuseppe
  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 = {},
  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. }
  author = {Nicola Barbieri and Francesco Bonchi and Giuseppe
  title = {Cascade-based community detection},
  booktitle = {Sixth ACM International Conference on Web Search and
                  Data Mining, WSDM 2013, Rome, Italy, February 4-8,
  year = {2013},
  publisher = {ACM},
  pages = {33-42},
  url = {},
  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. }
  author = {Nicola Barbieri and Antonio Bevacqua and Marco
                  Carnuccio and Giuseppe Manco and Ettore Ritacco},
  title = {Probabilistic Sequence Modeling for Recommender
  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 = {},
  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. }
  author = {Nicola Barbieri and Francesco Bonchi and Giuseppe
  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 = {},
  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
  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 = {},
  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.}
  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 = {},
  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
  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 = {},
  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
  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 = {},
  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.}
  author = {Nicola Barbieri and Giuseppe Manco and Ettore
  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 = {},
  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.}

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