Generating Usage-related Questions for Preference Elicitation in Conversational Recommender Systems
A key distinguishing feature of conversational recommender systems over traditional recommender systems is theirability to elicit user preferences using natural language. Currently, the predominant approach to preference elicitation is to ask questions ...
A Comprehensive Survey on Automated Machine Learning for Recommendations
Deep recommender systems (DRS) are critical for current commercial online service providers, which address the issue of information overload by recommending items that are tailored to the user’s interests and preferences. They have unprecedented feature ...
Dynamic Bi-layer Graph Learning for Context-aware Sequential Recommendation
Sequential recommendations have received great attention in recent years due to their wide application in e-commerce, trip planning, and online education. Contexts reveal the intention of a user in a transaction such as consuming or purchasing an item, ...
GRIDS: Personalized Guideline Recommendations while Driving Through a New City
Drive tourism has become increasingly popular in the past decade; however, driving through a new city is challenging because the road and traffic environments vary significantly across cities. A driver used to driving in one city may face severe ...
Towards a Causal Decision-Making Framework for Recommender Systems
Causality is gaining more and more attention in the machine learning community and consequently also in recommender systems research. The limitations of learning offline from observed data are widely recognized, however, applying debiasing strategies like ...
Ranking the causal impact of recommendations under collider bias in k-spots recommender systems
The first objective of recommender systems is to provide personalized recommendations for each user. However, personalization may not be its only use. Past recommendations can be further analyzed to gain global insights into users’ behavior with respect ...
