The goal of the workshop is to bring together researchers and practitioners interested in addressing the challenges posed by information heterogeneity in recommender systems and studying information fusion in this context. We aim at identifying the main challenges, suggesting and discussing novel ideas for addressing these challenges, and proposing a research agenda for future research at the domain.
Topics of interest include, but are not limited to:
Heterogeneity and fusion of information in user profiles
- Fusion of user profiles from different representations
- Combination of short- and long-term user preferences
- Combination of different types of user preferences: tastes, interests, needs, goals, mood, etc.
- Cross domain recommendations, based on user preferences about different interest aspects (e.g., by merging movie and music tastes)
- Cross representation recommendations, considering diverse sources of user preferences: explicit and implicit feedback
Heterogeneity and fusion of information in recommended resources
- Recommendation of resources of different nature: news, reviews, scientific papers, etc.
- Recommendation of resources belonging to different multimedia: text, image, audio, video
- Recommendation of resources annotated in different languages
Heterogeneity and fusion of information in contextual features
- Contextualisation of user preferences (e.g., user preferences at work, on holidays, etc.)
- Cross context recommendations (e.g., by merging information about location, time, social aspects, etc.)
- Multi-dimensional recommendation based on several contextual features (e.g., physical and social environment, device and network settings, external events, etc.)