Fashion stylists have historically bridged the gap between consumers’ desires and perfect outfits, which involve intricate combinations of colors, patterns, and materials. Although recent advancements in fashion recommendation systems have made strides in outfit compatibility prediction and complementary item retrieval, these systems rely heavily on pre-selected customer choices. Therefore, we introduce a groundbreaking approach to fashion recommendations: text-to-outfit retrieval task that generates a complete outfit set based solely on textual descriptions given by users. Our model is devised at three semantic levels-item, style, and outfit-where each level progressively aggregates data to form a coherent outfit recommendation based on textual input. Here, we leverage strategies similar to those in the contrastive language-image pretraining model to address the intricate-style matrix within the outfit sets. Using the Maryland Polyvore and Polyvore Outfit datasets, our approach significantly outperformed state-of-the-art models in text-video retrieval tasks, solidifying its effectiveness in the fashion recommendation domain. This research not only pioneers a new facet of fashion recommendation systems, but also introduces a method that captures the essence of individual style preferences through textual descriptions.

The monitoring of arterial blood pressure (ABP) in anesthetized patients is crucial for preventing hypotension, which can lead to adverse clinical outcomes. Several efforts have been devoted to develop artificial intelligence-based hypotension prediction indices. However, the use of such indices is limited because they may not provide a compelling interpretation of the association between the predictors and hypotension. Herein, an interpretable deep learning model is developed that forecasts hypotension occurrence 10 min before a given 90-s ABP record. Internal and external validations of the model performance show the area under the receiver operating characteristic curves of 0.9145 and 0.9035, respectively. Furthermore, the hypotension prediction mechanism can be physiologically interpreted using the predictors automatically generated from the proposed model for representing ABP trends. Finally, the applicability of a deep learning model with high accuracy is demonstrated, thus providing an interpretation of the association between ABP trends and hypotension in clinical practice.

This study explains how the product diversity affects long-term performance of recommendation systems. We examine how the number of product categories offered to customers is related to customer churn incidence. We collect a large scale panel data consisting of product category, revenues and customer churn information from a large offline retailer. We find that as the number of product categories recommended increases, the likelihood that customers churn strikingly decreases after controlling for the number of individual products being recommended. Our results suggest that companies can achieve better outcomes in their recommendation systems by explicitly incorporating the diversity of products being offered to their customers. Further, simulation results show that our proposed diversity-based recommendation strategy can save the company approximately $26 million per year (7.5% of the company's annual revenue) by preventing customer churn.

User profile data (for example, age and sex) is usually self-reported by users, so it is prone to human errors or biases. For example, a user can be reluctant to provide a company with private information such as his/her actual age upon subscription, thus the user either does not fill in the age column or put in some random numbers to avoid unwanted privacy intrusion. However, inaccurate or uncertain user profile data undermines the integrity of a company's marketing or operational intelligence. Targeting customers based on uncertain user profile data will not be as effective as targeting customers based on accurate user profile data. Thus companies perform preprocessing on user profile data as part of effort to maintain the accuracy of their user profile data. This paper presents a study of preprocessing uncertain user profile data based on a proposed simple collaborative learning algorithm. We demonstrate that a user's accurate profile information can be inferred from profile information of the user's social network neighbors. Particularly, we address the issue of how a communication service company can verify whether a user's reported age is true or not. We implement a simple collaborative learning algorithm using mobile network data. The dataset contains anonymized user data from a large Korean mobile company, capturing 174,071 users' demographic profiles and their communication histories. To construct a mobile social network among users, we collect 3G voice call histories including 561,787 unique call receivers who belong to the same service carrier. Results reveal that the prediction accuracy of the proposed method based on voice network data is 97% which is very high compared to 53%, the best accuracy by among competing methods and indicates that our method effectively detects users with great discrepancy between self-reported age and actual age.