srVPA: A Multi-Domain Conversational Service Recommendation Approach

With the widespread use of Virtual Personal Assistants (VPAs) in people’s daily lives, research on conversational recommender systems (CRSs) has become a hot topic in services computing. A key challenge is to reduce search space of candidate services for providing satisfying recommendations in fewer dialogue rounds. Firstly, user portraits are used to record long-term user preferences for assisting personalized recommendations; however, traditional label-based user portraits cannot reach at higher precision of quantified preferences and higher timeliness of preference updates along with time. For this challenge, this paper imports Multi-domain Dialogue-based User Portrait (MDUP) model to record multi-domain numerical preferences and continuously accumulates long-term preference updates, so that candidate services can be matched more precisely. Secondly, recommender systems usually compose massive services from multiple domains to meet coarse-grained user needs, and in order to improve the search, filtering and sorting efficiency of services during dialogue, this paper models potential connections among services of different domains by introducing service feature dependency to effectively compress the scale of candidate services. Combined the two perspectives together, this paper proposes a new service pre-sorting algorithm and a novel dialogue policy (intra-domain feedback-based iteration and inter-domain backtracking), then introduces architecture and working process of a novel service recommendation VPA (srVPA). Experiment demonstrates that compared with existing CRSs, the importation of MDUP and service feature dependency into srVPA significantly improve dialogue efficiency and user satisfaction on recommendation results.