People have information needs of varying complexity, which can be solved by an intelligent agent able to answer questions formulated in a proper way, eventually considering user context and preferences. In a scenario in which the user profile can be considered as a question, intelligent agents able to answer questions can be used to find the most relevant answers for a given user. In this work we propose a novel model based on Artificial Neural Networks to answer questions with multiple answers by exploiting multiple facts retrieved from a knowledge base. The model is evaluated on the factoid Question Answering and top-n recommendation tasks of the bAbI Movie Dialog dataset. After assessing the performance of the model on both tasks, we try to define the long-term goal of a conversational recommender system able to interact using natural language and to support users in their information seeking processes in a personalized way.
|Number of pages
|CEUR Workshop Proceedings
|Published - 2 Mar 2017
|AI*IA Workshop on Deep Understanding and Reasoning: A Challenge for Next-generation Intelligent Agents 2016 - Genova, Italy
Duration: 28 Nov 2016 → 28 Nov 2016