TY - JOUR
T1 - The Importance of Context When Recommending TV Content
T2 - Dataset and Algorithms
AU - Kristoffersen, Miklas Strøm
AU - Shepstone, Sven Ewan
AU - Tan, Zheng-Hua
PY - 2020
Y1 - 2020
N2 - Home entertainment systems feature in a variety of usage scenarios with one or more simultaneous users, for whom the complexity of choosing media to consume has increased rapidly over the last decade. Users' decision processes are complex and highly influenced by contextual settings, but data supporting the development and evaluation of context-aware recommender systems are scarce. In this paper we present a dataset of self-reported TV consumption enriched with contextual information of viewing situations. We show how choice of genre associates with, among others, the number of present users and users' attention levels. Furthermore, we evaluate the performance of predicting chosen genres given different configurations of contextual information, and compare the results to contextless predictions. The results suggest that including contextual features in the prediction cause notable improvements, and both temporal and social context show significant contributions.
AB - Home entertainment systems feature in a variety of usage scenarios with one or more simultaneous users, for whom the complexity of choosing media to consume has increased rapidly over the last decade. Users' decision processes are complex and highly influenced by contextual settings, but data supporting the development and evaluation of context-aware recommender systems are scarce. In this paper we present a dataset of self-reported TV consumption enriched with contextual information of viewing situations. We show how choice of genre associates with, among others, the number of present users and users' attention levels. Furthermore, we evaluate the performance of predicting chosen genres given different configurations of contextual information, and compare the results to contextless predictions. The results suggest that including contextual features in the prediction cause notable improvements, and both temporal and social context show significant contributions.
KW - Context awareness
KW - TV
KW - data collection
KW - recommender systems
UR - http://www.scopus.com/inward/record.url?scp=85085602828&partnerID=8YFLogxK
U2 - 10.1109/TMM.2019.2944214
DO - 10.1109/TMM.2019.2944214
M3 - Journal article
VL - 22
SP - 1531
EP - 1541
JO - I E E E Transactions on Multimedia
JF - I E E E Transactions on Multimedia
SN - 1520-9210
IS - 6
M1 - 8851276
ER -