As an interactive intelligent system, recommender systems are developed to suggest items that match users’ preferences. Since the emergence of recommender systems, a large majority of research has focused on objective accuracy criteria and less attention has been paid to how users interact with the system and the efficacy of interface designs from users’ perspectives. The field has reached a point where it ready to look beyond algorithms, into users’ interactions, decision making processes and overall experience.

This workshop focuses on integrating different theories of human decision making into the construction of recommender systems. This year it will focus particularly on the impact of interfaces on decision support and overall satisfaction.

The aim is to bring together researchers and practitioners around the topics of designing and evaluating novel intelligent interfaces for recommender systems in order to: (1) share research and techniques, including new design technologies and evaluation methodologies (2) identify next key challenges in the area, and (3) identify emerging topics. This workshop aims at creating an interdisciplinary community with a focus on the interface design issues for recommender systems and promoting collaboration opportunities between researchers and practitioners. We particularly encourage demos and mock-ups of systems to be used as a basis of a lively and interactive discussion in the workshop.

Topics of interests include, but are not limited to:

1. User Interfaces

Visual interfaces for recommender systems
Explanation interfaces for recommender systems
Collaborative multi-user interfaces (e.g., group decision making in e‐tourism)
Spoken and natural language interfaces
Trust-aware interfaces
Social interfaces
Context-aware interfaces
Ubiquitous and mobile interfaces
Conversational interfaces
Example- and demonstration-based interfaces
New approaches to designing interfaces for recommender systems
User interfaces for decision making (e.g., decision strategies and user ratings)

2. Interaction, user modeling and decision-making

Cognitive Modeling for recommender systems
Human-recommender interaction
Controllability, transparency and scrutability
Decision theories in recommender systems (e.g., priming, framing, and decoy effects)
Preference detection (e.g., eye tracking for automated preference detection)
The role of emotions in recommender systems (e.g., emotion‐aware recommendation)
Trust inspiring recommendation (e.g., explanation‐aware recommendation)
Argumentation and Persuasive recommendation (e.g., argumentation‐aware recommendation)
Cultural differences (e.g., culture‐aware recommendation)
Mechanisms for effective group decision making (e.g., group recommendation heuristics)
Decision theories for effective group decision making (e.g., hidden profile management)
Detection and avoidance of decision biases (e.g., in item presentations)

3. Evaluation

Case studies
Empirical studies and evaluations of new interfaces
Empirical studies and evaluations of new interaction designs
Evaluation methods and metrics (e.g., evaluation questionnaire design)