A method and device for recommendation based on session

Abstract

The object of the present invention is to provide a method and device for real-time recommendation based on session. Based on the current session between the current user and its current customer, and according to the attribute information of the current customer, the matching information of the current session is obtained from the knowledge base. The recommended answer corresponding to the current question in ; the recommended answer is provided to the current user; wherein, the structure of the knowledge in the knowledge base includes <客户属性，问题，答案>; Compared with the prior art, the present invention effectively utilizes the existing knowledge, facilitates the current user to answer the current customer's inquiry, and improves the user experience.< / 客户属性，问题，答案>

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a technique for recommending based on sessions. Background technique [0002] For departments or companies that require a large number of voice calls, they have accumulated a large amount of voice materials, which include a lot of experience and knowledge. However, due to the lack of effective management, it is difficult for new employees to benefit from it. A major problem is that manual speech translation requires a lot of manpower and material resources, and it is not easy for new employees to master the extracted knowledge proficiently. [0003] Existing solutions mainly rely on manual knowledge extraction, and rely on users to memorize and apply them. However, the existing methods have the following disadvantages: 1) Manual extraction is difficult to ensure the comprehensiveness of knowledge, and it depends entirely on personal experience. Knowledge is ignored, o...

AI Technical Summary

Problems solved by technology

But due to the lack of effective management, it is difficult for new employees to benefit from it

A major problem is that manual speech translation requires a lot of manpower and material resources, and it is not easy for new employees to master the extracted knowledge proficiently

[0003] Existing solutions mainly rely on manual knowledge extraction, and rely on users to memorize and apply them. However, the existing methods have the following disadvantages: 1) Manual extraction is difficult to ensure the comprehensiveness of knowledge, and it depends entirely on personal experience. Knowledge is ignored, or bad knowledge is extracted, so it is difficult to guarantee the optimal answer; 2) The memory of knowledge takes time, which increases the workload of users, and may not be able to remember the correct knowledge at the right time; Knowledge is optimized, good knowledge cannot get more exposure, and bad knowledge will not reduce the number of exposures

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