Context-aware Service Recommendation based on Knowledge Graph Embedding (Extended Abstract)

As a class of context-aware systems, context-aware service recommendation (CASR) aims to bind high-quality services to users, w.r.t. their context requirements (e.g., invocation time, location, social profiles, connectivity). However, current CASR lacks a rich context modelling and does not allow for multi-relational interactions between users and services in different contexts. We propose a context-sensitive service recommendation, by constructing a contextual service knowledge graph (C-SKG), which we translated into a low-dimensional vector space to facilitate its processing. Dilated Recurrent Neural Networks are applied to allow a context-aware C-SKG embedding, based on the principles of subgraph-aware proximity. A recommendation algorithm, finally, returns the top-rated services w.r.t. the target user’s context and the proximity degrees.