2018 AFieldStudyofRelatedVideoRecomm

(Zhong et al., 2018) ⇒ Yifan Zhong, Tahir Lazaro Sousa Menezes, Vikas Kumar, Qian Zhao, and F. Maxwell Harper. (2018). “A Field Study of Related Video Recommendations: Newest, Most Similar, Or Most Relevant?.” In: Proceedings of the 12th ACM Conference on Recommender Systems. ISBN:978-1-4503-5901-6 doi:10.1145/3240323.3240395

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Abstract

Many video sites recommend videos related to the one a user is watching. These recommendations have been shown to influence what users end up exploring and are an important part of a recommender system. Plenty of methods have been proposed to recommend related videos, but there has been relatively little work that compares competing strategies. We describe a field study of related video recommendations, where we deploy algorithms to recommend related movie trailers. Our results show that recency - and similarity-based algorithms yield the highest click-through rates, and that the recency-based algorithm leads to the most trailer-level engagement. Our findings suggest the potential to design non-personalized yet effective related item recommendation strategies.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2018 AFieldStudyofRelatedVideoRecomm	Qian Zhao F. Maxwell Harper Yifan Zhong Tahir Lazaro Sousa Menezes Vikas Kumar			A Field Study of Related Video Recommendations: Newest, Most Similar, Or Most Relevant?				10.1145/3240323.3240395		2018