This document summarizes the key requirements and challenges for processing datasets to be used in recommender systems based on three case studies. The main points are: 1) Recommender systems need to handle diverse social data from multiple sources in different formats and languages to support various recommendation scenarios and boost performance by combining data. 2) A common challenge is defining a metadata schema to transform and aggregate social data from different sources for federated recommender systems. 3) Case studies on a learning portal, open science platform, and multi-criteria rating dataset revealed additional challenges of data harvesting, anonymization, URI resolution, and developing algorithms that perform well with limited personalized data.