Current-generation recommendation algorithms are often focused on generic ratings prediction and item ranking tasks based on a user’s past preferences. However, many recommendations 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, 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. Previous work has shown that numerous examples of such complex needs exist on the Web, yet current-generation systems offer limited to no support for these needs. In this paer, we focus on narrative-driven book recommendation in the context of LibraryThing users posting recommendation requests in the discussion forums. We propose several new algorithms that take advantage of these narratives and example items as well as hybrid systems, the majority of which significantly outperform classic collaborative filtering. We show that narrative-driven recommendation 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.
Bogers, T., & Koolen, M. (2018). “I’m looking for something like ...”: Combining Narratives and Example Items for Narrative-driven Book Recommendation. In CEUR Workshop Proceedings (Vol. 2290, pp. 35–43). CEUR-WS.