Group Recommender System for Restaurant Lunches
Erik Hallström · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2013
A group recommender system for lunch restaurants is developed. The user interface is an Android application which is run on a smartphone. The system features a novel approach for implicit rating collection when a user browses a list of item descriptions. The individual recommendation is based on extracting and comparing tf-idf feature vectors of menu texts as well as individual rankings of the restaurants. The group recommender system works by aggregating the individual estimated scores of the members in the group, and in addition to this machine learning methods are used to capture the group dynamics in the group decision.