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Found 5 papers, 0 papers with code
One button machine for automating feature engineering in relational databases
Feature engineering is one of the most important and time consuming tasks in predictive analytics projects.
Generating Dialogue Agents via Automated Planning
Dialogue systems have many applications such as customer support or question answering.
An Evaluation Framework for Interactive Recommender System
Interactive recommender systems present an opportunity to engage the user in the process by allowing them to interact with the recommendations, provide feedback and impact the results in real-time.
IRF: Interactive Recommendation through Dialogue
Recent research focuses beyond recommendation accuracy, towards human factors that influence the acceptance of recommendations, such as user satisfaction, trust, transparency and sense of control. We present a generic interactive recommender framework that can add interaction functionalities to non-interactive recommender systems. We take advantage of dialogue systems to interact with the user and we design a middleware layer to provide the interaction functions, such as providing explanations for the recommendations, managing users preferences learnt from dialogue, preference elicitation and refining recommendations based on learnt preferences.
User Profiling from Reviews for Accurate Time-Based Recommendations
However, temporal aspects of a user profile may not always be explicitly available and so we may need to infer this information from available resources.
