8,819 research outputs found
Exploiting Topic Modeling and Neural Word Embeddings for Interpretable Retail Item Recommendations
Digital platforms have used recommender systems to recommend relevant products to their users based on their historical interactions. Recently, neural network-based recommender systems that generate embedding vectors have gained popularity in both research and practice and show improved performance over traditional methods. However, it is often difficult to explain why and how the recommended items are provided to specific users by these black box systems. In this study, we propose a novel user-centric approach to recommending retail items by exploiting the latent intent of the users from transaction histories. The latent theme is learned using a Latent Dirichlet Allocation topic modeling method. The proposed method can explain the intent of the focal user and other similar users. A preliminary evaluation study shows our method outperform the baseline methods in both the accuracy and the interpretability of the recommended items
Fashion Conversation Data on Instagram
The fashion industry is establishing its presence on a number of
visual-centric social media like Instagram. This creates an interesting clash
as fashion brands that have traditionally practiced highly creative and
editorialized image marketing now have to engage with people on the platform
that epitomizes impromptu, realtime conversation. What kinds of fashion images
do brands and individuals share and what are the types of visual features that
attract likes and comments? In this research, we take both quantitative and
qualitative approaches to answer these questions. We analyze visual features of
fashion posts first via manual tagging and then via training on convolutional
neural networks. The classified images were examined across four types of
fashion brands: mega couture, small couture, designers, and high street. We
find that while product-only images make up the majority of fashion
conversation in terms of volume, body snaps and face images that portray
fashion items more naturally tend to receive a larger number of likes and
comments by the audience. Our findings bring insights into building an
automated tool for classifying or generating influential fashion information.
We make our novel dataset of {24,752} labeled images on fashion conversations,
containing visual and textual cues, available for the research community.Comment: 10 pages, 6 figures, This paper will be presented at ICWSM'1
IMPUTING OR SMOOTHING? MODELLING THE MISSING ONLINE CUSTOMER JOURNEY TRANSITIONS FOR PURCHASE PREDICTION
Online customer journeys are at the core of e-commerce systems and it is therefore important to model and understand this online customer behaviour. Clickstream data from online journeys can be modelled using Markov Chains. This study investigates two different approaches to handle missing transition probabilities in constructing Markov Chain models for purchase prediction. Imputing the transition probabilities by using Chapman-Kolmogorov (CK) equation addresses this issue and achieves high prediction accuracy by approximating them with one step ahead probability. However, it comes with the problem of a high computational burden and some probabilities remaining zero after imputation. An alternative approach is to smooth the transition probabilities using Bayesian techniques. This ensures non-zero probabilities but this approach has been criticized for not being as accurate as the CK method, though this has not been fully evaluated in the literature using realistic, commercial data. We compare the accuracy of the purchase prediction of the CK and Bayesian methods, and evaluate them based on commercial web server data from a major European airline
A Survey of Serious Games for Cybersecurity Education and Training
Serious games can challenge users in competitive and entertaining ways. Educators have used serious games to increase student engagement in cybersecurity education. Serious games have been developed to teach students various cybersecurity topics such as safe online behavior, threats and attacks, malware, and more. They have been used in cybersecurity training and education at different levels. Serious games have targeted different audiences such as K-12 students, undergraduate and graduate students in academic institutions, and professionals in the cybersecurity workforce. In this paper, we provide a survey of serious games used in cybersecurity education and training. We categorize these games into four types based on the topics they cover and the purposes of the games: security awareness, network and web security, cryptography, and secure software development. We provide a catalog of games available online. This survey informs educators of available resources for cybersecurity education and training using interactive games.
Keywords: Serious games; Game-based Learning; Cybersecurity
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