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SIGMOD
2010
ACM

Recsplorer: recommendation algorithms based on precedence mining

10 years 2 months ago
Recsplorer: recommendation algorithms based on precedence mining
We study recommendations in applications where there are temporal patterns in the way items are consumed or watched. For example, a student who has taken the Advanced Algorithms course is more likely to be interested in Convex Optimization, but a student who has taken Convex Optimization need not be interested in Advanced Algorithms in the future. Similarly, a person who has purchased the Godfather I DVD on Amazon is more likely to purchase Godfather II sometime in the future (though it is not strictly necessary to watch/purchase Godfather I beforehand). We propose a precedence mining model that estimates the probability of future consumption based on past behavior. We then propose Recsplorer: a suite of recommendation algorithms that exploit the precedence information. We evaluate our algorithms, as well as traditional recommendation ones, using a real course planning system. We use existing transcripts to evaluate how well the algorithms perform. In addition, we augment our experime...
Aditya G. Parameswaran, Georgia Koutrika, Benjamin
Added 18 Jul 2010
Updated 18 Jul 2010
Type Conference
Year 2010
Where SIGMOD
Authors Aditya G. Parameswaran, Georgia Koutrika, Benjamin Bercovitz, Hector Garcia-Molina
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