Sentimentally enhanced conversation recommender system
A conversation recommender system (CRS) aims to provide high-quality recommendations to users in fewer conversation turns. Existing studies often rely on knowledge graphs and entities mentioned in dialogues to enhance the representation of entity information. However, they fail to thoroughly explore users’ emotional tendencies toward entities or effectively differentiate the varying impacts of different entities on user preferences. Recently, Liu et.al. proposed an innovative Sentimentally Enhanced Conversation Recommender system (SECR). In this presentation, the SECR method will be explained and described how this method captured users’ emotional inclinations toward entities.
Conversational movie recommender system using knowledge graphs
Conversational movie recommender systems that leverage knowledge graphs can significantly enhance the user experience by providing more personalized and informative recommendations. By incorporating structured information about movies, actors, directors, genres, and other relevant entities, these systems can go beyond basic ratings and uncover deeper connections between user preferences and movie attributes. This allows for more nuanced recommendations, such as suggesting movies based on an actor’s filmography, a director’s style, or even connections between seemingly unrelated preferences.