4,624 research outputs found
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Agent Street: An Environment for Exploring Agent-Based Models in Second Life
Urban models can be seen on a continuum between iconic and symbolic. Generally speaking, iconic models are physical versions of the real world at some scaled down representation, while symbolic models represent the system in terms of the way they function replacing the physical or material system by some logical and/or mathematical formulae. Traditionally iconic and symbolic models were distinct classes of model but due to the rise of digital computing the distinction between the two is becoming blurred, with symbolic models being embedded into iconic models. However, such models tend to be single user. This paper demonstrates how 3D symbolic models in the form of agent-based simulations can be embedded into iconic models using the multi-user virtual world of Second Life. Furthermore, the paper demonstrates Second Life\'s potential for social science simulation. To demonstrate this, we first introduce Second Life and provide two exemplar models; Conway\'s Game of Life, and Schelling\'s Segregation Model which highlight how symbolic models can be viewed in an iconic environment. We then present a simple pedestrian evacuation model which merges the iconic and symbolic together and extends the model to directly incorporate avatars and agents in the same environment illustrating how \'real\' participants can influence simulation outcomes. Such examples demonstrate the potential for creating highly visual, immersive, interactive agent-based models for social scientists in multi-user real time virtual worlds. The paper concludes with some final comments on problems with representing models in current virtual worlds and future avenues of research.Agent-Based Modelling, Pedestrian Evacuation, Segregation, Virtual Worlds, Second Life
Item Recommendation with Evolving User Preferences and Experience
Current recommender systems exploit user and item similarities by
collaborative filtering. Some advanced methods also consider the temporal
evolution of item ratings as a global background process. However, all prior
methods disregard the individual evolution of a user's experience level and how
this is expressed in the user's writing in a review community. In this paper,
we model the joint evolution of user experience, interest in specific item
facets, writing style, and rating behavior. This way we can generate individual
recommendations that take into account the user's maturity level (e.g.,
recommending art movies rather than blockbusters for a cinematography expert).
As only item ratings and review texts are observables, we capture the user's
experience and interests in a latent model learned from her reviews, vocabulary
and writing style. We develop a generative HMM-LDA model to trace user
evolution, where the Hidden Markov Model (HMM) traces her latent experience
progressing over time -- with solely user reviews and ratings as observables
over time. The facets of a user's interest are drawn from a Latent Dirichlet
Allocation (LDA) model derived from her reviews, as a function of her (again
latent) experience level. In experiments with five real-world datasets, we show
that our model improves the rating prediction over state-of-the-art baselines,
by a substantial margin. We also show, in a use-case study, that our model
performs well in the assessment of user experience levels
False News On Social Media: A Data-Driven Survey
In the past few years, the research community has dedicated growing interest
to the issue of false news circulating on social networks. The widespread
attention on detecting and characterizing false news has been motivated by
considerable backlashes of this threat against the real world. As a matter of
fact, social media platforms exhibit peculiar characteristics, with respect to
traditional news outlets, which have been particularly favorable to the
proliferation of deceptive information. They also present unique challenges for
all kind of potential interventions on the subject. As this issue becomes of
global concern, it is also gaining more attention in academia. The aim of this
survey is to offer a comprehensive study on the recent advances in terms of
detection, characterization and mitigation of false news that propagate on
social media, as well as the challenges and the open questions that await
future research on the field. We use a data-driven approach, focusing on a
classification of the features that are used in each study to characterize
false information and on the datasets used for instructing classification
methods. At the end of the survey, we highlight emerging approaches that look
most promising for addressing false news
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