CLASSY: A Conversational Aware Suggestion System

Abstract

Over the last few years, pervasive systems have seen some interesting development. Nevertheless, human–human interaction can also take advantage of those systems by using their ability to perceive the surrounding environment. In this work, we have developed a pervasive system – named CLASSY – that is aware of the conversational context and suggests documents potentially useful to the users based on an Information Retrieval system, and proposed a new scoring approach that uses semantics and distance based on proximity data in order to classify the relationship between tokens.

Conversational Document Prediction to Assist Customer Care Agents

“…This is a prevalent pattern that is found in around 5-9% of all customer care conversations in multiple domains that we have reviewed. To identify such documents, the agents manually extract the keywords from the conversation and search over their customer service knowledge base (Habibi and Popescu-Belis, 2015;Ferreira et al, 2019). Table 1 shows a conversation where the agent provides a URL 2 to the user.…”

“…Habibi and Popescu-Belis (2015) proposed a document recommender system by extracting keywords from a conversation using topic modeling techniques. Ferreira et al (2019) have used a similar keyword extraction framework and reported their results on a proprietary dataset.…”