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The digital marketing landscape is rapidly evolving, but the integration of visual content still heavily depends on human expertise. Driven by the quest for innovative marketing strategies that resonate with family-oriented consumers, this study seeks to bridge this gap by applying machine learning to analyze visual content in the maternity and baby care product sector.

This study incorporates a range of machine learning techniques – including open science framework feature detection, panoptic segmentation, customized instance segmentation, and face detection calculation methods – to analyze and predict the appeal of images, thereby enhancing user engagement and parent-child intimacy.

The exploration of various ML models, such as DT, LightGBM, RIPPER algorithm, and CNNs, has offered a comparative analysis that addresses a methodological gap in the existing literature, which frequently depends on isolated model evaluations. According to our quadrant analysis with respect to engagement rate and parent-child intimacy, the selection of a model for real-world applications depends on balancing performance and interpretability.

The proposed system offers a series of actionable recommendations designed to enhance customer engagement and foster brand loyalty. This study contributes to image design in maternity and baby care marketing and provides analytical insights for recommendation systems.

Aggressive weeding in academic libraries is becoming more commonplace as colleges seek to create student-centered environments and space is at a premium. For one community college in the Southwest United States, several factors required the library to proactively weed its collection within three years. At the same time, the library sought to maintain the circulation of its physical books.

Updating the library’s collection development policy to include robust selection and weeding criteria allowed the library to embark on a revitalization project to remove thousands of outdated or unused items, resulting in a net loss of nearly 32,000 books.

The loss of more than half of the general collection had an unforeseen consequence – a 70% increase in circulation statistics during the three-year deselection project. The case study's results highlight the need for continual maintenance of academic library collections.

The case study is original and not published elsewhere.