@inproceedings{Bogina2020,
title = {Visualizing Program Genres' Temporal-Based Similarity in Linear TV Recommendations},
author = {Veronika Bogina, Julia Sheidin, Tsvi Kuflik, Shlomo Berkovsky},
url = {https://dl.acm.org/doi/abs/10.1145/3399715.3399813},
doi = {10.1145/3399715.3399813},
isbn = {9781450375351},
year = {2020},
date = {2020-09-28},
booktitle = {Proceedings of the International Conference on Advanced Visual Interfaces},
journal = {Electronic Commerce Research and Applications},
abstract = {There is an increasing evidence that data visualization is an important and useful tool for quick understanding and filtering of large amounts of data. In this paper, we contribute to this body of work with a study that compares chord and ranked list for presentation of a temporal TV program genre similarity in next-program recommendations. We consider genre similarity based on the similarity of temporal viewing patterns. We discover that chord presentation allows users to see the whole picture and improves their ability to choose items beyond the ranked list of top similar items. We believe that similarity visualization may be useful for the provision of both the recommendations and their explanations to the end users.},
keywords = {Recommender Systems},
pubstate = {published},
tppubtype = {inproceedings}
}
There is an increasing evidence that data visualization is an important and useful tool for quick understanding and filtering of large amounts of data. In this paper, we contribute to this body of work with a study that compares chord and ranked list for presentation of a temporal TV program genre similarity in next-program recommendations. We consider genre similarity based on the similarity of temporal viewing patterns. We discover that chord presentation allows users to see the whole picture and improves their ability to choose items beyond the ranked list of top similar items. We believe that similarity visualization may be useful for the provision of both the recommendations and their explanations to the end users.