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 supporting users in their information seeking processes in a personalized way.
You can find more details about our approach in the paper: https://arxiv.org/abs/1702.02367
Project link: https://github.com/aleSuglia/imnamap