330,024 research outputs found
Graph Neural Network for Customer Engagement Prediction on Social Media Platforms
Social media platforms such as Twitter and Facebook play a pivotal role in companiesâ strategy of engaging customers. How to target potential customers on social media effectively and efficiently is an important yet unsolved question. Predicting customer engagement on social media platforms is facing several challenges that cannot be solved by traditional methods. In this work, we design a framework that leverages individual behavior on Facebook together with network contextual information to predict customer engagement (like/comment/share) of a brandâs posts. We first build a meta-path based Heterogeneous Information Network (HIN) to exploit large-scale content consumption information. We then design a Graph Neural Network (GNN) model combined with attention mechanism to learn structural feature representations of users to make the customer-brand engagement prediction. The proposed model is examined using a large-scale Facebook dataset and the result shows significant performance improvement compared with state-of-the-art baselines. Besides, the effectiveness of attention mechanism reveals the potential interpretability of the proposed model for the prediction results
Recommended from our members
Distilling Mobile Privacy Requirements from Qualitative Data
As mobile computing applications have become commonplace, it is increasingly important for them to address end-users' privacy requirements. Mobile privacy requirements depend on a number of contextual socio-cultural factors to which mobility adds another level of contextual variation. However, traditional requirements elicitation methods do not sufficiently account for contextual factors and therefore cannot be used effectively to represent and analyse the privacy requirements of mobile end users. On the other hand, methods that investigate contextual factors tend to produce data which can be difficult to use for requirements modelling. To address this problem, we have developed a Distillation approach that employs a problem analysis model to extract and refine privacy requirements for mobile applications from raw data gathered through empirical studies involving real users. Our aim was to enable the extraction of mobile privacy requirements that account for relevant contextual factors while contributing to the software design and implementation process. A key feature of the distillation approach is a problem structuring framework called privacy facets (PriF). The facets in the PriF framework support the identification of privacy requirements from different contextual perspectives namely - actors, information, information-flows and places. The PriF framework also aids in uncovering privacy determinants and threats that a system must take into account in order to support the end-user's privacy. In this work, we first show the working of distillation using qualitative data taken from an empirical study which involved social-networking practices of mobile users. As a means of validating distillation, another distinctly separate qualitative dataset from a location-tracking study is used, in both cases, the empirical studies relate to privacy issues faced by real users observed in their mobile environment
Vision Transformers with Natural Language Semantics
Tokens or patches within Vision Transformers (ViT) lack essential semantic
information, unlike their counterparts in natural language processing (NLP).
Typically, ViT tokens are associated with rectangular image patches that lack
specific semantic context, making interpretation difficult and failing to
effectively encapsulate information. We introduce a novel transformer model,
Semantic Vision Transformers (sViT), which leverages recent progress on
segmentation models to design novel tokenizer strategies. sViT effectively
harnesses semantic information, creating an inductive bias reminiscent of
convolutional neural networks while capturing global dependencies and
contextual information within images that are characteristic of transformers.
Through validation using real datasets, sViT demonstrates superiority over ViT,
requiring less training data while maintaining similar or superior performance.
Furthermore, sViT demonstrates significant superiority in out-of-distribution
generalization and robustness to natural distribution shifts, attributed to its
scale invariance semantic characteristic. Notably, the use of semantic tokens
significantly enhances the model's interpretability. Lastly, the proposed
paradigm facilitates the introduction of new and powerful augmentation
techniques at the token (or segment) level, increasing training data diversity
and generalization capabilities. Just as sentences are made of words, images
are formed by semantic objects; our proposed methodology leverages recent
progress in object segmentation and takes an important and natural step toward
interpretable and robust vision transformers.Comment: 22 pages, 9 figure
A Study of Legal Information Seeking Behaviour to Inform the Design of Electronic Legal Research Tools
Our work is motivated by the desire to support digital library users in ?getting to grips? with electronic resources. More specifically we are motivated by the desire to support users in understanding how to use, and in which situations it is appropriate to use, particular digital library or electronic resources. This work focuses on lawyers as a specific category of user; Callister [5] highlights that lawyers been traditionally regarded as having poor research skills. Electronic research skills are no exception: Howland and Lewis [8] surveyed U.S. law firm librarians to examine the quality and extent of the electronic legal research skills of summer clerks and first-year associates. They found that these graduates were unable to efficiently or effectively research issues that appear routinely in actual legal cases and concluded that they were not efficient or cost-effective users of LexisNexis and Westlaw (the two biggest digital law libraries in terms of case, legislation and journal coverage). This was despite all of the students having received some training on how to use the libraries while in law school. Digital libraries have traditionally been regarded as difficult to use [4] and based on our contextual observations with academic lawyers, digital law libraries such as LexisNexis Professional and Westlaw are no exception. We believe that this difficulty of use contributes to the problems that lawyers face with electronic legal research. Furthermore, we argue that developing better research skills goes hand-inhand with developing an understanding of the electronic environments in which these skills must be practiced. Our current work is focused on gaining a better understanding of legal academics? and professionals? information seeking behaviour when using existing electronic resources. This understanding will then be used to inform the design of user-centred support tools for digital law libraries (and potentially the design of the libraries themselves)
Activity River: Visualizing Planned and Logged Personal Activities for Reflection
We present Activity River, a personal visualization tool which enables
individuals to plan, log, and reflect on their self-defined activities. We are
interested in supporting this type of reflective practice as prior work has
shown that reflection can help people plan and manage their time effectively.
Hence, we designed Activity River based on five design goals (visualize
historical and contextual data, facilitate comparison of goals and
achievements, engage viewers with delightful visuals, support authorship, and
enable flexible planning and logging) which we distilled from the Information
Visualization and Human-Computer Interaction literature. To explore our
approach's strengths and limitations, we conducted a qualitative study of
Activity River using a role-playing method. Through this qualitative
exploration, we illustrate how our participants envisioned using our
visualization to perform dynamic and continuous reflection on their activities.
We observed that they were able to assess their progress towards their plans
and adapt to unforeseen circumstances using our tool.Comment: 9 pages, 6 figures, AVI '20, September 28-October 2, 2020, Salerno,
Italy 2020 Association for Computing Machiner
Adding generic contextual capabilities to wearable computers
Context-awareness has an increasingly important role to play in the development of wearable computing systems. In order to better define this role we have identified four generic contextual capabilities: sensing, adaptation, resource discovery, and augmentation. A prototype application has been constructed to explore how some of these capabilities could be deployed in a wearable system designed to aid an ecologist's observations of giraffe in a Kenyan game reserve. However, despite the benefits of context-awareness demonstrated in this prototype, widespread innovation of these capabilities is currently stifled by the difficulty in obtaining the contextual data. To remedy this situation the Contextual Information Service (CIS) is introduced. Installed on the user's wearable computer, the CIS provides a common point of access for clients to obtain, manipulate and model contextual information independently of the underlying plethora of data formats and sensor interface mechanisms
- âŠ