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- W2894060880 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." @default.
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- W2894060880 date "2018-09-27" @default.
- W2894060880 modified "2023-09-23" @default.
- W2894060880 title "A field study of related video recommendations" @default.
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- W2894060880 doi "https://doi.org/10.1145/3240323.3240395" @default.
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