This document summarizes several research papers on recommendation systems for web services. It discusses using rating data from users to recommend web services based on quality of service. Approaches include collaborative filtering to find similar users and services. Dynamic features are designed to describe user preferences and recommendations are made by weighting these features. The document also discusses using content-based filtering and social network data to provide recommendations. Improving recommendation diversity and combining collaborative and content-based filtering is addressed. Experimental results on real world datasets show hybrid approaches can improve performance on metrics like diversity, relevance and quality of service.