This document describes SIRUP, a system for generating serendipitous recommendations of TV programs. It aims to trigger curiosity in users through novel, unexpected recommendations that also have potential to be relevant based on the user's profile. SIRUP performs a novelty check using Linked Open Data paths and components to find innovative connections between programs. It also estimates a user's coping potential based on the diversity of genres and formats in their profile. An experiment with 165 users found that SIRUP was better able to model serendipity and achieved higher precision and catalog coverage than alternatives using only BBC metadata or a combined approach. Therefore, SIRUP demonstrates that serendipitous recommendations can trigger curiosity in users.

1. SIRUP
SERENDIPITY IN RECOMMENDATION THROUGH USER PERCEPTION
Valentina Maccatrozzo
Manon Terstall
Lora Aroyo
Guus Schreiber
Vrije Universiteit Amsterdam

2. ➤ Many on-demand services for TV content
➤ Too much time time to choose
➤ Recommender systems when lacking information build filter
bubbles around users
➤ There is a strong need for serendipity to keep people engaged
with content
INTRODUCTION

3. CURIOSITY THEORY TO UNDERSTAND SERENDIPITY
➤ The subjectivity of serendipity depends on:
➤ the knowledge of the user
➤ how much the user is keen on knowing more, better known
as curiosity
➤ Curiosity is a strong desire to know or learn something

4. SIRUP
NOVELTY CHECK
COPING
POTENTIAL CHECK
level of
CURIOSITY
in a TV programme
level of
SERENDIPITY
caused by TV programme
knowledge of
user
keen on
knowing more
RQ1: Do serendipitous
recommendations trigger
curiosity in users?

5. NOVELTY CHECK
➤ We use LOD paths with cosine similarity measure
➤ LOD paths allows for innovative connections
➤ We use types and properties of paths as input to the cosine
similarity measure
Reggie Yates’s
Extreme South
Africa
The Sky
at Night
Extreme
(musical
band)
Queen
(musical
band)
Brian
May
influenced by
has member
is presenter of
RQ2: Can we perform the novelty check of
TV programmes with respect to the user
profile using LOD paths components?

6. COPING POTENTIAL CHECK
➤ Challenging estimation:
➤ incomplete information about user’s tastes
➤ preferences change over time
➤ unknown attitude towards new content
➤ Simplified approach:
➤ count the unique instances of genres and formats as
indicators of the coping potential
RQ3: Can we estimate the coping potential of a
user with the diversity of genres and formats in
the user profile?

7. EXPERIMENT
➤ 290 British participants: 165 participants’ answers used
➤ 1460 BBC programmes aired from September 7th and 20th 2015
➤ Online questionnaire:
1. 8 ratings to build user profile
2. favourite genres, formats and demographics
3. evaluation of recommendations:
1. I did not think of this TV programme, but it seems interesting to me. (Interest)
2. This TV programme does not seem interesting to me. (Interest)
3. I am surprised to get this TV programme recommended. (Unexpectedness)
4. This recommendation fits my personal preferences. (Relevance)

8. RECOMMENDATIONS GENERATION
➤ Three rankings:
➤ cosine similarity based on BBC metadata (Baseline)
➤ cosine similarity based on LOD patterns (SIRUP)
➤ cosine similarity based on LOD patterns and BBC metadata
➤ 2 programmes per intervals (low, medium, high similarity
values)

9. RESULTS
➤ We analysed results in different ways:
➤ Comparison of the distributions of the similarity values
(Wilcoxon Signed Rank test)
➤ Serendipity (Logistic Regression)
➤ Precision
➤ Catalog coverage

10. BASELINE - BBC METADATA
➤ Comparison of the distributions of the similarity values:
➤ interest: the rank of the distribution of the similarity values is low when interest is low
➤ relevance: the rank of the distribution of the similarity values is low when relevance is
low
➤ unexpectedness: non-significant difference
➤ Serendipity: non significant model
➤ Precision:
➤ 63% for interest
➤ 64% for relevance
➤ 67% overall
➤ Catalog coverage: 35,41%

11. SIRUP - LOD PATHS COMPONENTS
➤ Comparison of the distributions of the similarity values:
➤ interest: the rank of the distribution of the similarity values is significantly higher when interest is high.
➤ relevance: the rank of the distribution of the similarity values is significantly higher when relevance is high.
➤ unexpectedness: the rank of the distribution of the similarity values is significantly lower when unexpectedness is high.
➤ Serendipity:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.0018 0.4325 -9.252 <2e-16
simValue 2.4372 1.1480 2.123 0.0338
genre diversity 0.7878 0.3207 2.457 0.0140
format diversity 0.1742 0.3478 0.501 0.6164
➤ Precision:
➤ 68% for interest
➤ 69% for relevance
➤ 71% overall
➤ Catalog coverage: 47,40%

12. COMBINED APPROACH
➤ Comparison of the distributions of the similarity values:
➤ interest: the rank of the distribution of the similarity values is lower when interest is higher;
➤ relevance: the rank of the distribution of the similarity values is lower when relevance is
low;
➤ unexpectedness: the rank of the distribution of the similarity values is lower when
unexpectedness is higher.
➤ Serendipity: non significant model
➤ Precision:
➤ 67% for interest
➤ 65% for relevance
➤ 69% overall
➤ Catalog coverage: 34,59%

13. WRAPPING UP
➤ We found that only SIRUP allows us to model significantly
serendipitous recommendations.
➤ SIRUP allows us to reach the highest precision and the
highest catalog coverage.
HENCE
➤ Serendipitous recommendations trigger curiosity in users.
➤ Novelty check can successfully been performed with LOD
path components.
➤ Also a simplified estimation of the coping potential is
beneficial.

14. THANK YOU
e-mail: v.maccatrozzo@vu.nl
twitter: @valentina_mac
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