User Memory Reasoning for Conversational Recommendation

We study a conversational recommendation model which dynamically manages users' past (offline) preferences and current (online) requests through a structured and cumulative user memory knowledge graph, to allow for natural interactions and accurate recommendations. For this study, we create a new Memory Graph (MG) Conversational Recommendation parallel corpus called MGConvRex with 7K+ human-to-human role-playing dialogs, grounded on a large-scale user memory bootstrapped from real-world user scenarios. MGConvRex captures human-level reasoning over user memory and has disjoint training/testing sets of users for zero-shot (cold-start) reasoning for recommendation. We propose a simple yet expandable formulation for constructing and updating the MG, and a reasoning model that predicts optimal dialog policies and recommendation items in unconstrained graph space. The prediction of our proposed model inherits the graph structure, providing a natural way to explain the model's recommendation. Experiments are conducted for both offline metrics and online simulation, showing competitive results

Modeling Spoken Decision Making Dialogue and Optimization of its Dialogue Strategy

This paper presents a spoken dialogue framework that helps users in making decisions. Users often do not have a definite goal or criteria for selecting from a list of alternatives. Thus the system has to bridge this knowledge gap and also provide the users with an appropriate alternative together with the reason for this recommendation through dialogue. We present a dialogue state model for such decision making dialogue. To evaluate this model, we implement a trial sightseeing guidance system and collect dialogue data. Then, we optimize the dialogue strategy based on the state model through reinforcement learning with a natural policy gradient approach using a user simulator trained on the collected dialogue corpus.