Telecommunication field package recommending method based on intelligent customer service robot interaction

Abstract

The invention provides a telecommunication field package recommending method based on intelligent customer service robot interaction. The method comprises the following steps: a. acquiring a user interest model; and b. recommending individual demand-satisfied package service for a user by adopting a decision tree algorithm. The invention further provides an intelligent customer service robot system recommending engine device. Compared with the existing recommending method, the telecommunication field package recommending method is based on a scene interaction model in a recommending process, and can be used for carrying out similarity calculation according to similarity among the calculation labels, calculating the similarity among the labels by combining similarity between semantic similarity calculation with the traditional TF-IDF (Term Frequency-Inverse Document Frequency) in the similarity calculation, better reflecting the characteristics of users and resources by applying labels to show resource and user models, and improving the recommending quality.

Description

technical field [0001] The present invention relates to a method for controlling package recommendation in the telecommunications field based on dialogue between intelligent customer service robots, especially based on the process of interaction between users and intelligent robots, which acquires user interest and behavior models, understands real-time needs of users, introduces decision tree algorithms, and recommends objects It is a telecom package business and belongs to the field of personalized recommendation technology. Background technique [0002] With the development of the Internet, information has grown exponentially, and this information has gradually become an important source of information for answering users' questions. Traditional search engines return a large number of relevant or irrelevant web page links, and it is difficult for users to quickly find the answers they want from these links. Therefore, people long for an intelligent information retrieval ...

AI Technical Summary

Problems solved by technology

In the current intelligent automatic question answering system, the main focus is on the knowledge base, shallow semantic analysis and other issues, and has not considered the semantic understanding combined with the user's personalized characteristics, and the user's interest tendency is obtained through interaction for modeling and personalization Recommendations and other issues

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