48,180 research outputs found
Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation
Existing item-based collaborative filtering (ICF) methods leverage only the
relation of collaborative similarity. Nevertheless, there exist multiple
relations between items in real-world scenarios. Distinct from the
collaborative similarity that implies co-interact patterns from the user
perspective, these relations reveal fine-grained knowledge on items from
different perspectives of meta-data, functionality, etc. However, how to
incorporate multiple item relations is less explored in recommendation
research. In this work, we propose Relational Collaborative Filtering (RCF), a
general framework to exploit multiple relations between items in recommender
system. We find that both the relation type and the relation value are crucial
in inferring user preference. To this end, we develop a two-level hierarchical
attention mechanism to model user preference. The first-level attention
discriminates which types of relations are more important, and the second-level
attention considers the specific relation values to estimate the contribution
of a historical item in recommending the target item. To make the item
embeddings be reflective of the relational structure between items, we further
formulate a task to preserve the item relations, and jointly train it with the
recommendation task of preference modeling. Empirical results on two real
datasets demonstrate the strong performance of RCF. Furthermore, we also
conduct qualitative analyses to show the benefits of explanations brought by
the modeling of multiple item relations
A kernel-based framework for learning graded relations from data
Driven by a large number of potential applications in areas like
bioinformatics, information retrieval and social network analysis, the problem
setting of inferring relations between pairs of data objects has recently been
investigated quite intensively in the machine learning community. To this end,
current approaches typically consider datasets containing crisp relations, so
that standard classification methods can be adopted. However, relations between
objects like similarities and preferences are often expressed in a graded
manner in real-world applications. A general kernel-based framework for
learning relations from data is introduced here. It extends existing approaches
because both crisp and graded relations are considered, and it unifies existing
approaches because different types of graded relations can be modeled,
including symmetric and reciprocal relations. This framework establishes
important links between recent developments in fuzzy set theory and machine
learning. Its usefulness is demonstrated through various experiments on
synthetic and real-world data.Comment: This work has been submitted to the IEEE for possible publication.
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LATTE: Application Oriented Social Network Embedding
In recent years, many research works propose to embed the network structured
data into a low-dimensional feature space, where each node is represented as a
feature vector. However, due to the detachment of embedding process with
external tasks, the learned embedding results by most existing embedding models
can be ineffective for application tasks with specific objectives, e.g.,
community detection or information diffusion. In this paper, we propose study
the application oriented heterogeneous social network embedding problem.
Significantly different from the existing works, besides the network structure
preservation, the problem should also incorporate the objectives of external
applications in the objective function. To resolve the problem, in this paper,
we propose a novel network embedding framework, namely the "appLicAtion
orienTed neTwork Embedding" (Latte) model. In Latte, the heterogeneous network
structure can be applied to compute the node "diffusive proximity" scores,
which capture both local and global network structures. Based on these computed
scores, Latte learns the network representation feature vectors by extending
the autoencoder model model to the heterogeneous network scenario, which can
also effectively unite the objectives of network embedding and external
application tasks. Extensive experiments have been done on real-world
heterogeneous social network datasets, and the experimental results have
demonstrated the outstanding performance of Latte in learning the
representation vectors for specific application tasks.Comment: 11 Pages, 12 Figures, 1 Tabl
Network Model Selection for Task-Focused Attributed Network Inference
Networks are models representing relationships between entities. Often these
relationships are explicitly given, or we must learn a representation which
generalizes and predicts observed behavior in underlying individual data (e.g.
attributes or labels). Whether given or inferred, choosing the best
representation affects subsequent tasks and questions on the network. This work
focuses on model selection to evaluate network representations from data,
focusing on fundamental predictive tasks on networks. We present a modular
methodology using general, interpretable network models, task neighborhood
functions found across domains, and several criteria for robust model
selection. We demonstrate our methodology on three online user activity
datasets and show that network model selection for the appropriate network task
vs. an alternate task increases performance by an order of magnitude in our
experiments
Link Prediction via Generalized Coupled Tensor Factorisation
This study deals with the missing link prediction problem: the problem of
predicting the existence of missing connections between entities of interest.
We address link prediction using coupled analysis of relational datasets
represented as heterogeneous data, i.e., datasets in the form of matrices and
higher-order tensors. We propose to use an approach based on probabilistic
interpretation of tensor factorisation models, i.e., Generalised Coupled Tensor
Factorisation, which can simultaneously fit a large class of tensor models to
higher-order tensors/matrices with com- mon latent factors using different loss
functions. Numerical experiments demonstrate that joint analysis of data from
multiple sources via coupled factorisation improves the link prediction
performance and the selection of right loss function and tensor model is
crucial for accurately predicting missing links
A Survey on Bayesian Deep Learning
A comprehensive artificial intelligence system needs to not only perceive the
environment with different `senses' (e.g., seeing and hearing) but also infer
the world's conditional (or even causal) relations and corresponding
uncertainty. The past decade has seen major advances in many perception tasks
such as visual object recognition and speech recognition using deep learning
models. For higher-level inference, however, probabilistic graphical models
with their Bayesian nature are still more powerful and flexible. In recent
years, Bayesian deep learning has emerged as a unified probabilistic framework
to tightly integrate deep learning and Bayesian models. In this general
framework, the perception of text or images using deep learning can boost the
performance of higher-level inference and in turn, the feedback from the
inference process is able to enhance the perception of text or images. This
survey provides a comprehensive introduction to Bayesian deep learning and
reviews its recent applications on recommender systems, topic models, control,
etc. Besides, we also discuss the relationship and differences between Bayesian
deep learning and other related topics such as Bayesian treatment of neural
networks.Comment: To appear in ACM Computing Surveys (CSUR) 202
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