The Technological Gap Between Virtual Assistants and Recommendation Systems

Abstract

Virtual assistants, also known as intelligent conversational systems such as Google's Virtual Assistant and Apple's Siri, interact with human-like responses to users' queries and finish specific tasks. Meanwhile, existing recommendation technologies model users' evolving, diverse and multi-aspect preferences to generate recommendations in various domains/applications, aiming to improve the citizens' daily life by making suggestions. The repertoire of actions is no longer limited to the one-shot presentation of recommendation lists, which can be insufficient when the goal is to offer decision support for the user, by quickly adapting to his/her preferences through conversations. Such an interactive mechanism is currently missing from recommendation systems. This article sheds light on the gap between virtual assistants and recommendation systems in terms of different technological aspects. In particular, we try to answer the most fundamental research question, which are the missing technological factors to implement a personalized intelligent conversational agent for producing accurate recommendations while taking into account how users behave under different conditions. The goal is, instead of adapting humans to machines, to actually provide users with better recommendation services so that machines will be adapted to humans in daily life.

6 pages, Report

References in corpus (7)

• Distributed Representations of Words and Phrases and their Compositionality
• A Neural Conversational Model
• A Personalized System for Conversational Recommendations
• Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems
• Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning
• Attentive Memory Networks: Efficient Machine Reading for Conversational Search
• Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce

Cited by in corpus (2)

• Towards Personalized Dialog Policies for Conversational Skill Discovery
• On Estimating the Training Cost of Conversational Recommendation Systems