  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.
