Interactive Recommendations by Combining User-Item Preferences with Linked Open Data
Surya Teja Kallumadi, William H. Hsu · 2018
Recent advances in graph and network embeddings have been utilized for the purpose of providing recommendations. Hybrid recommender systems have shown the efficacy of using side information associated with entities. In this work we show how domain specific knowledge can be used to define meta paths within these heterogeneous domains and how these path constrained random walks can be used to embed user preferences in heterogeneous domains. The semantic embeddings generated from heterogeneous knowledge sources combined with user preferences can be used to refine a user's information needs. This representation modeling of users, entities and their associated properties opens up new modalities of interactions for the users to gravitate towards their requirements. In this work we propose the use of semantic embeddings for two kinds of interactive recommendation modalities: 1) exemplar based recommendations 2) "less like this/ more like this" style recommendations. In our opinion providing these modalities would boost the expressive power of exploratory search and recommender systems.