Content-based Fashion Recommender System Using Unsupervised Learning

Guillermo, Marielet; Española, Jason L.; Billones, Robert Kerwin C.; Vicerra, Ryan Rhay P.; Sybingco, Edwin; Dadios, Elmer P.; Fillone, Alexis M.

doi:10.1109/tencon54134.2021.9707459

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…Some of the latest developments in the field of deep neural networks include deep reinforcement learning (DRL), automated machine learning (AutoML), and graph neural networks (GNNs) [11]. Studies involving image processing have also been conducted, with researchers exploring ways to recommend relevant images based on clothing and footwear dataset quality queries [43]. Additionally, researchers have investigated the potential for extracting meaningful information from images and applying it to online recommendation systems to enhance the online shopping experience [44,45].…”

Section: Augmented Reality Deep Learning Virtual Assistantsmentioning

confidence: 99%

AI-Driven Recommendations: A Systematic Review of the State of the Art in E-Commerce

Necula

Păvăloaia

2023

Applied Sciences

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Electronic commerce has a strong connection with recommendation processes. There are various forms of recommendations, ranging from virtual assistants to online suggestions made in real time. Different algorithms and technologies are utilized for each form, and the choice of technique is dependent on the task at hand. For instance, artificial intelligence may utilize deep learning or machine learning techniques. The type of data also plays a role in determining the techniques used. Predictive modeling is applied to textual data, while image data requires image processing followed by AI algorithms for prediction. This study aimed to investigate the extent to which artificial intelligence is utilized in recommender systems for electronic commerce, as well as the current and future trends in the field. This was achieved through a systematic literature review of scientific articles from the past decade, using WosViewer for data collection and the Bibliometrix R package for analysis. The findings demonstrate that artificial intelligence works in conjunction with other technologies, such as blockchain, virtual reality, and augmented reality, to enhance the consumer experience throughout the e-commerce process.

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Section: Augmented Reality Deep Learning Virtual Assistantsmentioning

confidence: 99%

AI-Driven Recommendations: A Systematic Review of the State of the Art in E-Commerce

Necula

Păvăloaia

2023

Applied Sciences

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Graph Query Language (GQL)-structured Algorithms for Geospatial Intelligence on Public Transportation

Guillermo

Rivera

Concepcion

et al. 2022

2022 13th International Conference on Information and Communication Technology Convergence (ICTC)

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Strategic Transit Route Recommendation Considering Multi-Trip Feature Desirability Using Logit Model with Optimal Travel Time Analysis

Guillermo¹,

Rivera²,

Lucas³

et al. 2022

JACIII

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Route recommendation continues to manifest noteworthy contributions to the intelligent transportation system field of research as it evolves through time. Early related studies helped passengers and tourists experience a more convenient travel. At the same time, these helped transport planners analyze people’s trip preferences and its correlation with the region-specific economic status in a more time-relevant data. Majority, however, require historical data and heavy data collection methods. For user quantified metrics such as route cost in terms of travel time and distance, the complexity and sparsity of preferences between travelers are persistent challenges. The strategic transit route recommendation proposed in this study takes into account multiple trip features (both quantitative and qualitative) desirability using logit model and the optimal travel time with respect to a given road traffic condition, headway, and passenger demand. The chosen area of study is the Western Visayas region of the Philippines specific to the public utility bus (PUB) and jeepney (PUJ) transit routes. The results of the research exhibited the feasibility of an optimal and strategic recommendation of public transportation route for passengers considering present time relevant trip conditions rather than relying on the historical data which are difficult to obtain, or worse, non-existent.

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Content-based Fashion Recommender System Using Unsupervised Learning

Cited by 3 publications

References 33 publications

AI-Driven Recommendations: A Systematic Review of the State of the Art in E-Commerce

AI-Driven Recommendations: A Systematic Review of the State of the Art in E-Commerce

Graph Query Language (GQL)-structured Algorithms for Geospatial Intelligence on Public Transportation

Strategic Transit Route Recommendation Considering Multi-Trip Feature Desirability Using Logit Model with Optimal Travel Time Analysis

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