The document discusses narrative-driven recommendation, where users describe their recommendation needs in a natural language narrative along with examples of past preferences. It finds that a significant percentage of recommendation requests online take this form. An analysis of book recommendation narratives found they commonly include aspects like content, engagement, familiarity and metadata. Future work is needed to better understand complex needs, extract relevant signals from narratives, and develop algorithms that can satisfy such needs.

1. DEFINING AND SUPPORTING
NARRATIVE-DRIVEN RECOMMENDATION
TOINE BOGERS
MARIJN KOOLEN
RECSYS 2017, COMO, ITALY
AALBORG UNIVERSITY COPENHAGEN
HUYGENS ING
A A L B O R G
U N I V E R S I T Y

2. SETTING THE SCENE
▸ Great strides have been made in ratings prediction and item ranking in the
past decade(s)
– Relatively straightforward scenario: given the past, predict the future
▸ However, recommendation is often a more complex problem!
– Evaluation of list of recommended items takes place in context
– Often only a single step in satisfying a more complex need
– Many constraints placed on which recommendations are interesting
▸ We focus on a specific complex scenario: narrative-driven recommendation
– Scenario where users provide (1) a rich narrative description of their
recommendation need as well as (2) an overview of (relevant) past
preferences
2

3. SETTING THE SCENE
▸ Great strides have been made in ratings prediction and item ranking in the
past decade(s)
– Relatively straightforward scenario: given the past, predict the future
▸ However, recommendation is often a more complex problem!
– Evaluation of list of recommended items takes place in context
– Often only a single step in satisfying a more complex need
– Many constraints placed on which recommendations are interesting
▸ We focus on a specific complex scenario: narrative-driven recommendation
– Scenario where users provide (1) a rich narrative description of their
recommendation need as well as (2) an overview of (relevant) past
preferences
2
I’m looking for manly books about manly issues, that
aren’t too gritty, but make you think as much as you
laugh. So far these examples I have on my bookshelf:
‘About a Boy’ and ‘High Fidelity’ by Nick Hornby,
‘Train Man’ by Hitori Nakano. Have you any other
manly books for manly men such as I?

4. SETTING THE SCENE 3

5. MOTIVATION
▸ Why is this interesting?
– Challenging problem that is currently going
unsolved
– Better understanding of need narratives could teach
us about recommendation (needs) in general
– Connections to many existing sub-domains in RecSys
■ Conversational recommenders
■ Critiquing-based recommenders
■ Semantic recommenders
■ Interface design (eliciting needs)
4

6. NARRATIVE-DRIVEN RECOMMENDATION
▸ Two components
1. Information about user preferences
■ User profiles containing explicit or
implicit feedback
■ Focused ‘mini-profiles’ in the form
of positive/negative examples
2. Narrative description of user need
■ One or more natural language
sentences describing aspects of
the item(s) desired by the user
■ (Optional) context of use
5
Films not about crime where a
regular person (not a cop, spy,
etc) has a mystery to solve, not
related to crime or conspiracies,
but still has to do “detective
work” following clues and
leads. An example would be
last year’s Walter Mitty, where
the protagonist tracks down a
photographer to recover a photo
negative he needs. Any ideas?Hey y'all, My book
club is looking for
something to read.

7. ANALYSIS – HOW COMMON IS THIS?
▸ Explicit input & feedback always require more effort → volume of narratives is lower
▸ Does not mean it is an uncommon problem!
– Current systems do not support NDR in terms of interface & functionality
– If tools are lacking, then the problem could be invisible?
▸ One productive source of narrative needs are online discussion forums
– Example: LibraryThing’s book discussion forums (193K threads, 6.1M posts, 2M users)
■ Annotated random sample of 3,924 forum threads
■ 13.1% (n = 517) were requests for books to discover → ~25K narrative requests on LT
■ Same story on rival websites and other domains
■ Hundreds of thousands of rich requests available on the Web
■ Millions of complex recommendation needs going unmet?
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8. ANALYSIS – WHAT DO THEY LOOK LIKE?
▸ A different sample of 974 first posts from LibraryThing were
annotated for their narrative recommendation components
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Context of use?
Total
Yes No
Examples?
Yes 209 (21.5%) 352 (36.1%) 561 (57.6%)
No 170 (17.5%) 243 (24.9%) 413 (42.4%)
Total 379 (38.9%) 595 (61.1%) 974

9. ANALYSIS – WHAT DO THEY LOOK LIKE?
▸ Annotated 974 book recommendation narratives with seven relevance aspects
– Accessibility Accessibility in terms of the language, length, or level of difficulty.
– Content Aspects such as topic, plot, genre, style, or comprehensiveness.
– Engagement Books that fit a particular mood or interest or provide a particular
reading experience.
– Familiarity Books that are similar to known books or related to a previous
experience.
– Metadata Books with a certain title or by a certain author, editor, illustrator,
publisher, in a particular format, or written or published in certain year or period.
– Novelty Books with content that is novel to the reader, unusual or quirky.
– Socio-Cultural Books related to the user’s socio-cultural background or values,
books that are popular or obscure, or books that have had a particular cultural or
social impact.
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10. ANALYSIS – WHAT DO THEY LOOK LIKE?
▸ Distribution of the seven narrative aspects
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A C E F M N S
Accessibility 137 96 41 48 28 8 27
Content 598 157 267 176 26 98
Engagement 196 88 40 11 24
Familiarity 326 74 17 45
Metadata 179 11 25
Novelty 34 10
Socio-cultural 133

11. DISCUSSION
▸ Presented a preliminary analysis of a non-standard, complex recommendation
scenario
▸ Future work is needed on
– Exploring complex needs in other domains (games, travel, music, movies, …)
– Eliciting complex needs from users
■ Conversational recommendation, critiquing, interface design
– Understanding signals present in complex needs
■ How do we extract this signal (NLP, text mining, semantic analysis)
■ How reliably can we extract this signal?
– Satisfying complex needs
■ Developing algorithms that can incorporate such aspects beyond preferences
■ How does this signal help to improve recommendation quality?
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12. QUESTIONS?
Interested in complex
recommendation? Come to
the ComplexRec workshop
tomorrow morning in Room 3!