2,060 research outputs found
Multi-modal Machine Learning for Vehicle Rating Predictions Using Image, Text, and Parametric Data
Accurate vehicle rating prediction can facilitate designing and configuring
good vehicles. This prediction allows vehicle designers and manufacturers to
optimize and improve their designs in a timely manner, enhance their product
performance, and effectively attract consumers. However, most of the existing
data-driven methods rely on data from a single mode, e.g., text, image, or
parametric data, which results in a limited and incomplete exploration of the
available information. These methods lack comprehensive analyses and
exploration of data from multiple modes, which probably leads to inaccurate
conclusions and hinders progress in this field. To overcome this limitation, we
propose a multi-modal learning model for more comprehensive and accurate
vehicle rating predictions. Specifically, the model simultaneously learns
features from the parametric specifications, text descriptions, and images of
vehicles to predict five vehicle rating scores, including the total score,
critics score, performance score, safety score, and interior score. We compare
the multi-modal learning model to the corresponding unimodal models and find
that the multi-modal model's explanatory power is 4% - 12% higher than that of
the unimodal models. On this basis, we conduct sensitivity analyses using SHAP
to interpret our model and provide design and optimization directions to
designers and manufacturers. Our study underscores the importance of the
data-driven multi-modal learning approach for vehicle design, evaluation, and
optimization. We have made the code publicly available at
http://decode.mit.edu/projects/vehicleratings/.Comment: The paper submitted to IDETC/CIE2023, the International Design
Engineering Technical Conferences & Computers and Information in Engineering
Conference, has been accepte
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Modeling the Dynamics of Consumer Behavior from Massive Interaction Data
Recent technological innovations (e.g. e-commerce platforms, automated retail stores) have enabled dramatic changes in people's shopping experiences, as well as the accessibility to incredible volumes of consumer-product interaction data. As a result, machine learning (ML) systems can be widely developed to help people navigate relevant information and make decisions. Traditional ML systems have achieved great success on various well-defined problems such as speech recognition and facial recognition. Unlike these tasks where datasets and objectives are clearly benchmarked, modeling consumer behavior can be rather complicated; for example, consumer activities can be affected by real-time shopping contexts, collected interaction data can be noisy and biased, interests from multiple parties (both consumers and producers) can be involved in the predictive objectives.The primary goal of this dissertation is to address the obstacles in modeling consumer activities through computational approaches, but with careful considerations from economic and societal perspectives. Intellectually, such models help us to understand the forces that guide consumer behavior. Methodologically, I build algorithms capable of processing massive interaction datasets by connecting well-developed ML techniques and well-established economic theories. Practically, my work has applications ranging from recommender systems, e-commerce and business intelligence
Leveraging Deep Learning Techniques on Collaborative Filtering Recommender Systems
With the exponentially increasing volume of online data, searching and
finding required information have become an extensive and time-consuming task.
Recommender Systems as a subclass of information retrieval and decision support
systems by providing personalized suggestions helping users access what they
need more efficiently. Among the different techniques for building a
recommender system, Collaborative Filtering (CF) is the most popular and
widespread approach. However, cold start and data sparsity are the fundamental
challenges ahead of implementing an effective CF-based recommender. Recent
successful developments in enhancing and implementing deep learning
architectures motivated many studies to propose deep learning-based solutions
for solving the recommenders' weak points. In this research, unlike the past
similar works about using deep learning architectures in recommender systems
that covered different techniques generally, we specifically provide a
comprehensive review of deep learning-based collaborative filtering recommender
systems. This in-depth filtering gives a clear overview of the level of
popularity, gaps, and ignored areas on leveraging deep learning techniques to
build CF-based systems as the most influential recommenders.Comment: 24 pages, 14 figure
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