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 experiments with a user study on the live system where users rate their recommendations.