Narrative-Driven Recommendation as Complex Task

Current-generation recommendation algorithms are often focused on generic ratings prediction and item ranking tasks based on a user’s past preferences. However, many scenarios are more complex with specific criteria and constraints on which items are relevant. This paper focuses on a particular type of complex recommendation needs: Narrative-Driven Recommendation (NDR), where users describe their needs in short narratives, often with one or more example items that fit that need, against a background of historical preferences that may not be spelled out in the narrative, but do play a role in their considerations. We show that such complex needs are common on the Web, yet current-generation systems offer limited to no support for these needs. We focus on narrative-driven book recommendation in the context of LibraryThing (LT) users posting recommendation requests in the discussion forums. We provide an analysis of these needs in terms of their structure, the relevance aspects they cover, and what types of data and algorithms fits these aspects. Subsequently, we propose several new algorithms that take advantage of these narratives and example items as well as hybrid systems, most of which significantly outperform classic collaborative filtering. We show that NDR is indeed a complex scenario that requires further study. Our findings have consequences for system design and development not only in the book domain, but also in other domains where users express focused recommendation needs, such as movies, television, games and music.