Requirements Elicitation in Cognitive Service for Recommendation

TL;DR

This paper introduces a two-phase requirement elicitation method for cognitive service recommendation, integrating sequence planning and real-time detection to improve response relevance and accuracy.

Contribution

It proposes a novel framework combining requirement sequence planning and real-time detection with a response generation model, enhancing conversational recommendation systems.

Findings

Significant improvement on DuRecDial dataset results

Effective prediction of user requirements during conversation

Enhanced response relevance and recommendation accuracy

Abstract

Nowadays, cognitive service provides more interactive way to understand users' requirements via human-machine conversation. In other words, it has to capture users' requirements from their utterance and respond them with the relevant and suitable service resources. To this end, two phases must be applied: I.Sequence planning and Real-time detection of user requirement, II.Service resource selection and Response generation. The existing works ignore the potential connection between these two phases. To model their connection, Two-Phase Requirement Elicitation Method is proposed. For the phase I, this paper proposes a user requirement elicitation framework (URef) to plan a potential requirement sequence grounded on user profile and personal knowledge base before the conversation. In addition, it can also predict user's true requirement and judge whether the requirement is completed based on the user's utterance during the conversation. For the phase II, this paper proposes a response generation model based on attention, SaRSNet. It can select the appropriate resource (i.e. knowledge triple) in line with the requirement predicted by URef, and then generates a suitable response for recommendation. The experimental results on the open dataset \emph{DuRecDial} have been significantly improved compared to the baseline, which proves the effectiveness of the proposed methods.