Abstract: An aspect of the invention relates to the field of an automatic chat support and analytics (recommender) engine for digital sales. The analytics engine interactively and iteratively collects the feature value of a client's profile by automatically generating questions in different styles and extracting the semantic information from the client's responses. The accumulated feature information for the client can enhance the capability of a recommender engine. Specifically, for item recommendation, the recommender engine includes components for item scoring and its confidence estimation, feature importance scoring, missing feature inference and confidence estimation by smoothing the corresponding feature values from the most similar clients. The recommender engine also involves a client response analytics component, which performs a quality check by evaluating the consistency between the inferred feature value from the similar clients and the extracted one from the client's response.

Abstract: An aspect of the invention relates to the field of an automatic chat support and analytics (recommender) engine for digital sales. The analytics engine interactively and iteratively collects the feature value of a client's profile by automatically generating questions in different styles and extracting the semantic information from the client's responses. The accumulated feature information for the client can enhance the capability of a recommender engine. Specifically, for item recommendation, the recommender engine includes components for item scoring and its confidence estimation, feature importance scoring, missing feature inference and confidence estimation by smoothing the corresponding feature values from the most similar clients. The recommender engine also involves a client response analytics component, which performs a quality check by evaluating the consistency between the inferred feature value from the similar clients and the extracted one from the client's response.