TY - GEN
T1 - Tell me what you want
T2 - 31st ACM Conference on Hypertext and Social Media, HT 2020
AU - Eberhard, Lukas
AU - Walk, Simon
AU - Helic, Denis
PY - 2020/7/13
Y1 - 2020/7/13
N2 - Recommender systems are efficient exploration tools providing their users with valuable suggestions about items, such as products or movies. However, in scenarios where users have more specific ideas about what they are looking for (e.g., they provide describing narratives, such as "Movies with minimal story, but incredible atmosphere, such as No Country for Old Men"), traditional recommender systems struggle to provide relevant suggestions. In this paper, we study this problem by investigating a large collection of such narratives from the movie domain. We start by empirically analyzing a dataset containing free-text narratives representing movie suggestion requests from reddit users as well as community suggestions to those requests. We find that community suggestions are frequently more diverse than requests, making a recommendation task a challenging one. In a prediction experiment, we use embedding algorithms to assess the importance of request features including movie descriptions, genres, and plot keywords, by computing recommendations. Our findings suggest that, in our dataset, positive movies and keywords have the strongest, whereas negative movie features have the weakest predictive power. We strongly believe that our new insights into narratives for recommender systems represent an important stepping stone towards novel applications, such as interactive recommender applications.
AB - Recommender systems are efficient exploration tools providing their users with valuable suggestions about items, such as products or movies. However, in scenarios where users have more specific ideas about what they are looking for (e.g., they provide describing narratives, such as "Movies with minimal story, but incredible atmosphere, such as No Country for Old Men"), traditional recommender systems struggle to provide relevant suggestions. In this paper, we study this problem by investigating a large collection of such narratives from the movie domain. We start by empirically analyzing a dataset containing free-text narratives representing movie suggestion requests from reddit users as well as community suggestions to those requests. We find that community suggestions are frequently more diverse than requests, making a recommendation task a challenging one. In a prediction experiment, we use embedding algorithms to assess the importance of request features including movie descriptions, genres, and plot keywords, by computing recommendations. Our findings suggest that, in our dataset, positive movies and keywords have the strongest, whereas negative movie features have the weakest predictive power. We strongly believe that our new insights into narratives for recommender systems represent an important stepping stone towards novel applications, such as interactive recommender applications.
KW - Empirical analysis
KW - Narrative-driven recommendations
KW - Recommender systems
UR - http://www.scopus.com/inward/record.url?scp=85089500591&partnerID=8YFLogxK
U2 - 10.1145/3372923.3404818
DO - 10.1145/3372923.3404818
M3 - Conference paper
AN - SCOPUS:85089500591
T3 - Proceedings of the 31st ACM Conference on Hypertext and Social Media, HT 2020
SP - 301
EP - 306
BT - Proceedings of the 31st ACM Conference on Hypertext and Social Media, HT 2020
PB - Association of Computing Machinery
Y2 - 13 July 2020 through 15 July 2020
ER -