Unlike the traditional recommender systems that rely on historical data such as clicks or purchases, a conversational recommender system (CRS) aims to provide a personalized recommendation through a natural conversation. The conversational interaction facilitates capturing not only explicit preference from mentioned items but also implicit states, such as a user’s current situation and emotional states from a dialogue context. Nevertheless, existing CRSs fall short of fully exploiting a dialogue context since they primarily derive explicit user preferences from the items and item-attributes mentioned in a conversation. To address this limitation and attain a comprehensive understanding of a dialogue context, we proposeCoreSense, aconversationalrecommender system enhanced with social commonsenseknowledge. In other words, CoreSense exploits the social commonsense knowledge graph ATOMIC to capture the user’s implicit states, such as a user’s current situation and emotional states, from a dialogue context. Thus, the social commonsense knowledge-augmented CRS can provide a more appropriate recommendation from a given dialogue context. Furthermore, we enhance the collaborative filtering effect by utilizing the user’s states inferred from commonsense knowledge as an improved criterion for retrieving other dialogues of similar interests. Extensive experiments on CRS benchmark datasets show that CoreSense provides human-like recommendations and responses based on inferred user states, achieving significant performance improvements.

Numerous large-scale knowledge graphs (KGs) fundamentally represent two-view KGs: an ontology-view KG with abstract classes in ontology and an instance-view KG with specific collections of entities instantiated from ontology classes. Two-view KG embedding aims to jointly learn continuous vector representations of entities and relations in the aforementioned two-view KGs. In essence, an ontology schema exhibits a tree-like structure guided by class hierarchies, which leads classes to form inheritance hierarchies. However, existing two-view KG embedding models neglect those hierarchies, which provides the necessity to reflect class inheritance. On the other hand, KG is constructed based on a pre-defined ontology schema that includes heterogeneous relations between classes. Furthermore, these relations are defined within the scope of those among classes since instances inherit all the properties of their corresponding classes, which reveals structural similarity between two multi-relational networks. Despite the consideration to bridge the gap among two-view KG representations, existing methods ignore the existence of structural similarity between two-view KGs. To address these issues, we propose a novel two-view KG embedding model, CISS, considering Class Inheritance and Structural Similarity between two-view KGs. To deal with class inheritance, we utilize class sets, each of which is composed of sibling classes, to learn fine-grained class representations. In addition, we configure virtual instance-view KG from clustered instances and compare subgraph representations of two-view KGs to enhance structural similarity between them. Experimental results show our superior performance compared to existing models.