948 research outputs found
Taste or Addiction?: Using Play Logs to Infer Song Selection Motivation
Online music services are increasing in popularity. They enable us to analyze
people's music listening behavior based on play logs. Although it is known that
people listen to music based on topic (e.g., rock or jazz), we assume that when
a user is addicted to an artist, s/he chooses the artist's songs regardless of
topic. Based on this assumption, in this paper, we propose a probabilistic
model to analyze people's music listening behavior. Our main contributions are
three-fold. First, to the best of our knowledge, this is the first study
modeling music listening behavior by taking into account the influence of
addiction to artists. Second, by using real-world datasets of play logs, we
showed the effectiveness of our proposed model. Third, we carried out
qualitative experiments and showed that taking addiction into account enables
us to analyze music listening behavior from a new viewpoint in terms of how
people listen to music according to the time of day, how an artist's songs are
listened to by people, etc. We also discuss the possibility of applying the
analysis results to applications such as artist similarity computation and song
recommendation.Comment: Accepted by The 21st Pacific-Asia Conference on Knowledge Discovery
and Data Mining (PAKDD 2017
Compressing Word Embeddings
Recent methods for learning vector space representations of words have
succeeded in capturing fine-grained semantic and syntactic regularities using
vector arithmetic. However, these vector space representations (created through
large-scale text analysis) are typically stored verbatim, since their internal
structure is opaque. Using word-analogy tests to monitor the level of detail
stored in compressed re-representations of the same vector space, the
trade-offs between the reduction in memory usage and expressiveness are
investigated. A simple scheme is outlined that can reduce the memory footprint
of a state-of-the-art embedding by a factor of 10, with only minimal impact on
performance. Then, using the same `bit budget', a binary (approximate)
factorisation of the same space is also explored, with the aim of creating an
equivalent representation with better interpretability.Comment: 10 pages, 0 figures, submitted to ICONIP-2016. Previous experimental
results were submitted to ICLR-2016, but the paper has been significantly
updated, since a new experimental set-up worked much bette
Gaussian Process Surrogate Models for Neural Networks
Not being able to understand and predict the behavior of deep learning systems makes it hard to decide what architecture and algorithm to use for a given problem. In science and engineering, modeling is a methodology used to understand complex systems whose internal processes are opaque. Modeling replaces a complex system with a simpler, more interpretable surrogate. Drawing inspiration from this, we construct a class of surrogate models for neural networks using Gaussian processes. Rather than deriving kernels for infinite neural networks, we learn kernels empirically from the naturalistic behavior of finite neural networks. We demonstrate our approach captures existing phenomena related to the spectral bias of neural networks, and then show that our surrogate models can be used to solve practical problems such as identifying which points most influence the behavior of specific neural networks and predicting which architectures and algorithms will generalize well for specific datasets
Exploring Time-Sensitive Variational Bayesian Inference LDA for Social Media Data
There is considerable interest among both researchers and the mass public in understanding the topics of discussion on social media as they occur over time. Scholars have thoroughly analysed sampling-based topic modelling approaches for various text corpora including social media; however, another LDA topic modelling implementation—Variational Bayesian (VB)—has not been well studied, despite its known efficiency and its adaptability to the volume and dynamics of social media data. In this paper, we examine the performance of the VB-based topic modelling approach for producing coherent topics, and further, we extend the VB approach by proposing a novel time-sensitive Variational Bayesian implementation, denoted as TVB. Our newly proposed TVB approach incorporates time so as to increase the quality of the generated topics. Using a Twitter dataset covering 8 events, our empirical results show that the coherence of the topics in our TVB model is improved by the integration of time. In particular, through a user study, we find that our TVB approach generates less mixed topics than state-of-the-art topic modelling approaches. Moreover, our proposed TVB approach can more accurately estimate topical trends, making it particularly suitable to assist end-users in tracking emerging topics on social media
Discovering conversational topics and emotions associated with Demonetization tweets in India
Social media platforms contain great wealth of information which provides us
opportunities explore hidden patterns or unknown correlations, and understand
people's satisfaction with what they are discussing. As one showcase, in this
paper, we summarize the data set of Twitter messages related to recent
demonetization of all Rs. 500 and Rs. 1000 notes in India and explore insights
from Twitter's data. Our proposed system automatically extracts the popular
latent topics in conversations regarding demonetization discussed in Twitter
via the Latent Dirichlet Allocation (LDA) based topic model and also identifies
the correlated topics across different categories. Additionally, it also
discovers people's opinions expressed through their tweets related to the event
under consideration via the emotion analyzer. The system also employs an
intuitive and informative visualization to show the uncovered insight.
Furthermore, we use an evaluation measure, Normalized Mutual Information (NMI),
to select the best LDA models. The obtained LDA results show that the tool can
be effectively used to extract discussion topics and summarize them for further
manual analysis.Comment: 6 pages, 11 figures. arXiv admin note: substantial text overlap with
arXiv:1608.02519 by other authors; text overlap with arXiv:1705.08094 by
other author
Internet Gaming Disorder: the interplay between physical activity and user–avatar relationship
Understanding both the risk and protective factors associated with Internet Gaming Disorder (IGD) has been viewed by many in the gaming studies field as an area of research priority. The present study focused on the potential risk and protective effects of user-avatar (game figure) relationship and physical activity (PA) respectively. To address these aims, a cross-sectional and a longitudinal mixed-methods design were combined (comprising both psychological and physiological assessments). A sample of 121 emerging adult gamers (18%29 years) residing in Australia, who played massively multiplayer online games, were assessed in relation to their IGD behaviors using the nine-item Internet Gaming Disorder Scale-Short Form. Additionally, the Proto-Self-Presence scale was used to evaluate the extent to which gamers identified with the body of their avatar. Finally, a PA monitor (Fit Bit Flex ) measured levels of energy consumed during real world daily activities (active minutes). A number of linear regressions and moderation analyses were conducted. Findings confirmed that Proto-Self-Presence functioned as an IGD risk factor and that PA acted protectively, weakening the association between Proto-Self-Presence and IGD behaviors. Implications of these findings are discussed in relation to IGD treatment and gaming development aspects
Internet pornography viewing preference as a risk factor for adolescent Internet addiction: the moderating role of classroom personality factors
Background and aims: Adolescent Internet pornography viewing has been significantly increased in the last decade with research highlighting its association with Internet addiction (IA). However, there is little longitudinal data on this topic, particularly in relation to peer context effects. This study aimed to examine age- and context-related variations in the Internet pornography–IA association.
Methods: A total of 648 adolescents, from 34 classrooms, were assessed at 16 years and then at 18 years to examine the effect of Internet pornography preference on IA in relation to the classroom context. IA was assessed using the Internet Addiction Test (Young, 1998), Internet pornography preference (over other Internet applications) was assessed with a binary (yes/no) question, and classroom introversion and openness to experience (OTE) with the synonymous subscales within the Five Factor Questionnaire (Asendorpf & Van Aken, 2003).
Results: Three-level hierarchical linear models were calculated. Findings showed that viewing Internet pornography exacerbates the risk of IA over time, while classroom factors, such as the average level of OTE and introversion, differentially moderate this relationship.
Discussion and conclusion: The study demonstrated that the contribution of Internet pornography preference (as an IA risk factor) might be increased in more extroverted classrooms and decreased in OTE classrooms
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Depression, Internet Gaming Disorder, and the moderating effect of the gamer-avatar relationship: an exploratory longitudinal study
Research into Internet Gaming Disorder (IGD) literature largely uses cross-sectional designs and seldom examines gaming context-related factors. Therefore, the present study combined a cross-sectional and longitudinal design to examine depression and the gamer-avatar relationship (GAR) as risk factors in the development of IGD among emerging adults. IGD behaviors of 125 gamers (64 online gamers, Mage = 23.3 years, SD= 3.4; 61 offline gamers, Mage = 23.0 years, SD= 3.4) were assessed using the nine-item Internet Gaming Disorder Scale Short Form (IGDS-SF9; Pontes & Griffiths, 2015). The Self-Presence Scale (Ratan & Dawson, 2015) and the Beck Depression Inventory (Beck, Steer, & Brown, 1996) were also used to assess gamers’ levels of GAR and depressive symptoms respectively. Regression and moderation analyses revealed that depression and the GAR act as individual risk factors in the development of IGD over time. Furthermore, the GAR exacerbates the IGD risk effect of depression
Detecting Large Concept Extensions for Conceptual Analysis
When performing a conceptual analysis of a concept, philosophers are
interested in all forms of expression of a concept in a text---be it direct or
indirect, explicit or implicit. In this paper, we experiment with topic-based
methods of automating the detection of concept expressions in order to
facilitate philosophical conceptual analysis. We propose six methods based on
LDA, and evaluate them on a new corpus of court decision that we had annotated
by experts and non-experts. Our results indicate that these methods can yield
important improvements over the keyword heuristic, which is often used as a
concept detection heuristic in many contexts. While more work remains to be
done, this indicates that detecting concepts through topics can serve as a
general-purpose method for at least some forms of concept expression that are
not captured using naive keyword approaches
A Probabilistic model of meetings that combines words and discourse features
(c) 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.This is the author's accepted version of this article. The final published version can be found here: http://dx.doi.org/10.1109/TASL.2008.92586
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