We introduce PulseSearch, a GenAI-based approach for generating query suggestions in a music search platform. Designed to anticipate users’ intent during a search session, PulseSearch combines long- and short-term user signals by conditioning generation on both recent user queries and pre-generated listener profiles. To further enhance contextual relevance, it generates suggestions tailored to different times of the day. We conduct both online and offline evaluations, showing that PulseSearch consistently improves suggestion quality over dense retrieval baselines across dimensions such as personalization, diversity, and freshness. A demo of our results is available at the provided URL.

Hatebusters is a web application for actively reporting YouTube hate speech, aiming to establish an online community of volunteer citizens. Hatebusters searches YouTube for videos with potentially hateful comments, scores their comments with a classifier trained on human-annotated data and presents users those comments with the highest probability of being hate speech. It also employs gamification elements, such as achievements and leaderboards, to drive user engagement.

The ever growing data collections create the need for brief explorations of the available data to extract relevant information before decision making becomes necessary. In this context of data exploration, current data analysis solutions struggle to quickly pinpoint useful information in data collections. One major reason is that loading data in a DBMS without knowing which part of it will actually be useful is a major bottleneck. To remove this bottleneck, state-of-the art approaches perform queries in situ, thus avoiding the loading overhead. In situ query engines, however, are index-oblivious, and lack sophisticated techniques to reduce the amount of data to be accessed. Furthermore, applications constantly generate fresh data and update the existing raw data files whereas state-of-the art in situ approaches support only append-like workloads.