A Personalized System for Conversational Recommendations

Abstract

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system.

References in corpus (3)

• Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech
• Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System
• GEMINI: A Natural Language System for Spoken-Language Understanding

Cited by in corpus (13)

• Advances and Challenges in Conversational Recommender Systems: A Survey
• Preference-based Search using Example-Critiquing with Suggestions
• Evaluating Conversational Recommender Systems: A Landscape of Research
• Deep Conversational Recommender in Travel
• Towards Knowledge-Based Recommender Dialog System
• Personalizing a Dialogue System with Transfer Reinforcement Learning
• INSPIRED: Toward Sociable Recommendation Dialog Systems
• Reinforcement Learning for Personalized Dialogue Management
• The Technological Gap Between Virtual Assistants and Recommendation Systems
• Controllable Gradient Item Retrieval
• Exploring Interactions with Voice-Controlled TV
• Conversational Help for Task Completion and Feature Discovery in Personal Assistants
• On Estimating the Training Cost of Conversational Recommendation Systems