Choice-based preference elicitation for collaborative filtering recommender systems
Benedikt Loepp, Tim Hussein, Jüergen Ziegler · 2014
We present an approach to interactive recommending that combines the advantages of algorithmic techniques with the benefits of user-controlled, interactive exploration in a novel manner. The method extracts latent factors from a matrix of user rating data as commonly used in Collaborative Filtering, and generates dialogs in which the user iteratively chooses between two sets of sample items. Samples are chosen by the system for low and high values of each latent factor considered. The method positions the user in the latent factor space with few interaction steps, and finally selects items near the user position as recommendations.