# A Conversation History-Based Q&A Cache Mechanism for Multi-Layered Chatbot Services
## 6 Conclusions  
The proposed cache mechanism is responsible for improving the response time of a multi-layered, multi-chatbot system. It is included in the multi-layered broker system to help the broker provide faster answers to user questions. The caching mechanism aims to store often-asked relevant Q&A sets based on conversational history and keywords. Using the cache mechanism lightens system processes and improves response time by sending immediate answers to questions from the cache memory in the broker.  
Experiments with four cases having different numbers of Q&A pairs were conducted to show the performance of the presented cache mechanism. The results of this experi  
Figure 10: Experimental results for response time.  
ment show that the cache mechanism performed well. This mechanism works based on the relevance of Q&A pairs based on the same keywords and frequency based on the conversational history. The experiment achieved up to a 94.00% hit ratio in contextual-question sequences with 100 Q&A sets and cache size 30, which was better than 66.70% in random-question sequences with 50 Q&A sets and cache size 30.  
As was mentioned, in a situation where cloud computing is mainstream, it is a general trend to use edge computing to improve the response speed of the cloud. In the case of chatbots, having a broker with a cache function on the edge has an advantage in terms of reaction speed. In the case of the knowledge coverage of the chatbot system, it is natural that a multi-chatbot with several exposed chatbots is helpful from the point of view of collective intelligence rather than a single chatbot. However, in a multi-chatbot system, if the client uses a structure in which a client asks a question to each chatbot and selects an answer, the slowdown will become more serious. The usage of the cache mechanism improves the speed of a broker.  
We used the concepts of relevance and frequency in the cache mechanism in this paper. As one of our future works, we plan to use the concept of likes in addition to relevance and frequency. This idea will promote competition among chatbots for the advancement of knowledge. In addition, to make the cache mechanism more intelligent, one needs to use an ontology or knowledge graph. The knowledge graph allows the cache mechanism to consider the meaning of words based on conversation history and ontology.  
Conceptualization, O.M. and D.K.; methodology, O.M. and D.K.; software, O.M.; validation, D.K.; formal analysis, D.K.; investigation, O.M. and D.K.; resources, O.M. and D.K.; data curation, D.K.; writing--original draft preparation, O.M.; writing--review and editing, D.K.; visualization, O.M.; supervision, D.K.; project administration, D.K.; funding acquisition, D.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program, grant number IITP-2021-2016-0-00465, supervised by the ITTP (Institute for Information & Communications Technology Planning & Evaluation).  
Publicly available datasets were analyzed in this study. This data can be found here: [[https://code.google.com/archive/p/aiml-en-us-foundation-alice/downloads](https://code.google.com/archive/p/aiml-en-us-foundation-alice/downloads)]([https://code.google.com/archive/p/aiml-en-us-foundation-alice/downloads](https://code.google.com/archive/p/aiml-en-us-foundation-alice/downloads)).  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2021-2016-0-00465) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).  
The authors declare no conflict of interest.