3,936 research outputs found
A Hierarchical Attention-based Contrastive Learning Method for Micro Video Popularity Prediction
Micro videos popularity prediction (MVPP) has recently attracted widespread research interests given the increasing prevalence of video-based social platforms. However, previous studies have overlooked the unique patterns between popular and unpopular videos and the interactions between asynchronous features different data dimensions. To address this, we propose a novel hierarchical attention contrastive learning method named HACL, which extracts explainable representation features, learns their asynchronous interactions from both temporal and spatial levels, and separates the positive and negative embeddings identities. This reveals video popularity in a contrastive and interrelated view, and thus can be responsible for a better MVPP. Dual neural networks account for separate positive and negative patterns via contrastive learning. To obtain the temporal-wise interaction coefficients, we propose a Hadamard-product based attention approach to optimize the trainable attention-map matrices. Results from our experiments on a TikTok micro video dataset show that HACL outperforms benchmarks and provides insightful managerial implications
Alleviating Video-Length Effect for Micro-video Recommendation
Micro-videos platforms such as TikTok are extremely popular nowadays. One
important feature is that users no longer select interested videos from a set,
instead they either watch the recommended video or skip to the next one. As a
result, the time length of users' watching behavior becomes the most important
signal for identifying preferences. However, our empirical data analysis has
shown a video-length effect that long videos are easier to receive a higher
value of average view time, thus adopting such view-time labels for measuring
user preferences can easily induce a biased model that favors the longer
videos. In this paper, we propose a Video Length Debiasing Recommendation
(VLDRec) method to alleviate such an effect for micro-video recommendation.
VLDRec designs the data labeling approach and the sample generation module that
better capture user preferences in a view-time oriented manner. It further
leverages the multi-task learning technique to jointly optimize the above
samples with original biased ones. Extensive experiments show that VLDRec can
improve the users' view time by 1.81% and 11.32% on two real-world datasets,
given a recommendation list of a fixed overall video length, compared with the
best baseline method. Moreover, VLDRec is also more effective in matching
users' interests in terms of the video content.Comment: Accept by TOI
Fine-grained Video Attractiveness Prediction Using Multimodal Deep Learning on a Large Real-world Dataset
Nowadays, billions of videos are online ready to be viewed and shared. Among
an enormous volume of videos, some popular ones are widely viewed by online
users while the majority attract little attention. Furthermore, within each
video, different segments may attract significantly different numbers of views.
This phenomenon leads to a challenging yet important problem, namely
fine-grained video attractiveness prediction. However, one major obstacle for
such a challenging problem is that no suitable benchmark dataset currently
exists. To this end, we construct the first fine-grained video attractiveness
dataset, which is collected from one of the most popular video websites in the
world. In total, the constructed FVAD consists of 1,019 drama episodes with
780.6 hours covering different categories and a wide variety of video contents.
Apart from the large amount of videos, hundreds of millions of user behaviors
during watching videos are also included, such as "view counts",
"fast-forward", "fast-rewind", and so on, where "view counts" reflects the
video attractiveness while other engagements capture the interactions between
the viewers and videos. First, we demonstrate that video attractiveness and
different engagements present different relationships. Second, FVAD provides us
an opportunity to study the fine-grained video attractiveness prediction
problem. We design different sequential models to perform video attractiveness
prediction by relying solely on video contents. The sequential models exploit
the multimodal relationships between visual and audio components of the video
contents at different levels. Experimental results demonstrate the
effectiveness of our proposed sequential models with different visual and audio
representations, the necessity of incorporating the two modalities, and the
complementary behaviors of the sequential prediction models at different
levels.Comment: Accepted by WWW 2018 The Big Web Trac
Knowledge-aware Complementary Product Representation Learning
Learning product representations that reflect complementary relationship
plays a central role in e-commerce recommender system. In the absence of the
product relationships graph, which existing methods rely on, there is a need to
detect the complementary relationships directly from noisy and sparse customer
purchase activities. Furthermore, unlike simple relationships such as
similarity, complementariness is asymmetric and non-transitive. Standard usage
of representation learning emphasizes on only one set of embedding, which is
problematic for modelling such properties of complementariness. We propose
using knowledge-aware learning with dual product embedding to solve the above
challenges. We encode contextual knowledge into product representation by
multi-task learning, to alleviate the sparsity issue. By explicitly modelling
with user bias terms, we separate the noise of customer-specific preferences
from the complementariness. Furthermore, we adopt the dual embedding framework
to capture the intrinsic properties of complementariness and provide geometric
interpretation motivated by the classic separating hyperplane theory. Finally,
we propose a Bayesian network structure that unifies all the components, which
also concludes several popular models as special cases. The proposed method
compares favourably to state-of-art methods, in downstream classification and
recommendation tasks. We also develop an implementation that scales efficiently
to a dataset with millions of items and customers
Content Modelling for unbiased Information Analysis
Content is the form through which the information is conveyed as per the requirement of user. A volume of content is huge and expected to grow exponentially hence classification of useful data and not useful data is a very tedious task. Interface between content and user is Search engine. Therefore, the contents are designed considering search engine\u27s perspective. Content designed by the organization, utilizes user’s data for promoting their products and services. This is done mostly using inorganic ways utilized to influence the quality measures of a content, this may mislead the information. There is no correct mechanism available to analyse and disseminate the data. The gap between Actual results displayed to the user and results expected by the user can be minimized by introducing the quality check for the parameter to assess the quality of content. This may help to ensure the quality of content and popularity will not be allowed to precede quality of content. Social networking sites will help in doing the user modelling so that the qualitative dissemination of content can be validated
Machine Learning Models for Educational Platforms
Scaling up education online and onlife is presenting numerous key challenges, such as hardly manageable classes, overwhelming content alternatives, and academic dishonesty while interacting remotely. However, thanks to the wider availability of learning-related data and increasingly higher performance computing, Artificial Intelligence has the potential to turn such challenges into an unparalleled opportunity. One of its sub-fields, namely Machine Learning, is enabling machines to receive data and learn for themselves, without being programmed with rules. Bringing this intelligent support to education at large scale has a number of advantages, such as avoiding manual error-prone tasks and reducing the chance that learners do any misconduct. Planning, collecting, developing, and predicting become essential steps to make it concrete into real-world education.
This thesis deals with the design, implementation, and evaluation of Machine Learning models in the context of online educational platforms deployed at large scale. Constructing and assessing the performance of intelligent models is a crucial step towards increasing reliability and convenience of such an educational medium. The contributions result in large data sets and high-performing models that capitalize on Natural Language Processing, Human Behavior Mining, and Machine Perception. The model decisions aim to support stakeholders over the instructional pipeline, specifically on content categorization, content recommendation, learners’ identity verification, and learners’ sentiment analysis. Past research in this field often relied on statistical processes hardly applicable at large scale. Through our studies, we explore opportunities and challenges introduced by Machine Learning for the above goals, a relevant and timely topic in literature.
Supported by extensive experiments, our work reveals a clear opportunity in combining human and machine sensing for researchers interested in online education. Our findings illustrate the feasibility of designing and assessing Machine Learning models for categorization, recommendation, authentication, and sentiment prediction in this research area. Our results provide guidelines on model motivation, data collection, model design, and analysis techniques concerning the above applicative scenarios. Researchers can use our findings to improve data collection on educational platforms, to reduce bias in data and models, to increase model effectiveness, and to increase the reliability of their models, among others. We expect that this thesis can support the adoption of Machine Learning models in educational platforms even more, strengthening the role of data as a precious asset. The thesis outputs are publicly available at https://www.mirkomarras.com
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