AI Research Analysis

Standard Practices for Data Processing and Multimodal Feature Extraction in Recommendation with DataRec and Ducho (D&D4Rec)

Recommendation pipelines involve several stages that can critically affect performance and reproducibility. However, early pipeline stages remain under-standardized, limiting comparability and inter-operability across studies. This tutorial addresses this gap by pro-viding both theoretical insights and hands-on experience with tools and practices for standardized data processing in recommender systems. In the first part, we introduce DATAREC, a Python library for reproducible and interoperable data management, and discuss data filtering, splitting, and topological analysis techniques. In the second part, we explore multimodal feature extraction in domains such as fashion, music, and movies, focusing on the challenges of meaningful multimodal integration. We introduce Ducho, a unified framework for extracting audio, visual, and textual features using modern backends, and demonstrate its integration with the evalua-tion framework ELLIOT. The tutorial targets researchers and practi-tioners with an interest in recommender systems, data preprocess-ing, and multimodal modeling. All materials, including slides, code, datasets, and recordings, will be openly available on a dedicated tutorial website: https://sites.google.com/view/dd4rec-tutorial/.