A personalized system for conversational recommendations

Searching for and making decisions about information is becoming increasingly dif-ficult 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 restau-rants, 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 inquir-ing 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 influ-ences 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. 1