Name
Abstract | ||
|---|---|---|
Research into recommendation algorithms has made great strides in recent years. However, these algorithms are typically applied in relatively straightforward scenarios: given information about a user's past preferences, what will they like in the future? Recommendation is often more complex: evaluating recommended items never takes place in a vacuum, and it is often a single step in the user's more complex background task. In this paper, we define a specific type of recommendation scenario called narrative-driven recommendation, where the recommendation process is driven by both a log of the user's past transactions as well as a narrative description of their current interest(s). Through an analysis of a set of real-world recommendation narratives from the LibraryThing forums, we demonstrate the uniqueness and richness of this scenario and highlight common patterns and properties of such narratives. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3109859.3109893 | RecSys |
Keywords | Field | DocType |
Narrative-driven Recommendation, Query-driven Recommendation, Complex Recommendation, Conversational Recommenders | Data mining,Uniqueness,World Wide Web,Computer science,Narrative | Conference |
ISBN | Citations | PageRank |
978-1-4503-4652-8 | 3 | 0.38 |
References | Authors | |
16 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Toine Bogers | 1 | 370 | 35.89 |
| Marijn Koolen | 2 | 348 | 45.15 |