Abstract
Chatbot technologies have made our lives easier. To create a chatbot with high intelligence, a significant amount of knowledge processing is required. However, this can slow down the reaction time; hence, a mechanism to enable a quick response is needed. This paper proposes a cache mechanism to improve the response time of the chatbot service; while the cache in CPU utilizes the locality of references within binary code executions, our cache mechanism for chatbots uses the frequency and relevance information which potentially exists within the set of Q&A pairs. The proposed idea is to enable the broker in a multi-layered structure to analyze and store the keyword-wise relevance of the set of Q&A pairs from chatbots. In addition, the cache mechanism accumulates the frequency of the input questions by monitoring the conversation history. When a cache miss occurs, the broker selects a chatbot according to the frequency and relevance, and then delivers the query to the selected chatbot to obtain a response for answer. This mechanism showed a significant increase in the cache hit ratio as well as an improvement in the average response time.
Highlights
Due to development in Internet technologies, online learning has grown from Internetpropelled distance education to online higher education enrollments
A related sequence is a set of question and answer (Q&A) pairs stored in order of relevance to each other to have humanlike and content-based conversations
The cache size is the number of Q&A pairs that can be saved in the cache
Summary
Due to development in Internet technologies, online learning has grown from Internetpropelled distance education to online higher education enrollments. Online education has such benefits as saving money and time, choosing an area of interest and a course of study individually [1]. The demand for online lectures is increasing, and the combination of MOOC and chatbot is becoming more useful [5]. It helps overcome one of the limitations and weaknesses of MOOC, which is taking a long time to receive feedback [6]. The main goal of this chatbot is to answer specific user questions to shorten the time it takes to receive feedback from a lecturer
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