recommendations as a conversation with the user
Recommender systems provide users with products or content intended to satisfy their information needs. The primary evaluation measures for recommender systems emphasize either the perceived relevance of the recommendations or the actions driven by those recommendations (e.g., purchases on ecommerce sites or clicks on news and social networking sites). Unfortunately, this transactional emphasis neglects the inherently interactive nature of the user experience. This tutorial explores recommendations as part of a conversation between users and systems. A conversational approach should provide transparency, control, and guidance. Transparency means that users understand why systems offer particular recommendations. Control means that users can explicitly manipulate the behavior of recommender systems based on personal needs and preferences. Guidance means that systems offers plausible and predictable next steps rather than requiring users to guess the consequences of their interactions. Finally, there are psychological factors -- in particular, the faith that users place in the recommender system's effectiveness. Since users develop mental models of recommender systems, the system should become more predictable with repeated use. The tutorial does not require any special background in interfaces or usability. Rather, it summarizes the best lessons from research and industry, offering concrete examples and practical techniques to make recommender systems most effective for users.
recommender systems hcir information seeking