Representation learning in heterogeneous information networks for user modeling and recommendations - Kallumadi, Surya (Kansas State University, 2018)
Current research in the field of recommender systems takes into consideration the interaction between users and items; we call this the homogeneous setting. In most real world systems, however these interactions are heterogeneous, i.e., apart from users and items there are other types of entities present within the system, and the interaction between the users and items occurs in multiple contexts and scenarios. The presence of multiple types of entities within a heterogeneous information network, opens up new interaction modalities for generating recommendations to the users. The key contribution of the proposed dissertation is representation learning in heterogeneous information networks for the recommendations task. Query-based information retrieval is one of the primary ways in which meaningful nuggets of information is retrieved from large amounts of data. Here the query is represented as a user's information need. In a homogeneous setting, in the absence of type and contextual side information, the retrieval context for a user boils down to the user's preferences over observed items. In a heterogeneous setting, information regarding entity types and preference context is available. Thus query-based contextual recommendations are possible in a heterogeneous network. The contextual query could be type-based (e.g., directors, actors, movies, books etc.) or value-based (e.g., based on tag values, genre values such as ``Comedy", ``Romance") or a combination of Types and Values. Exemplar-based information retrieval is another technique for of filtering information, where the objective is to retrieve similar entities based on a set of examples. This dissertation proposes approaches for recommendation tasks in heterogeneous networks, based on these retrieval mechanisms present in traditional information retrieval domain.
Keywords: Recommender systems; Heterogeneous information networks; Representation learning; Metapath; Music recommendations; User modeling