Abstract:
Given a user’s purchase/rating trajectories, sequential recommendation recommends the next few items they’re most likely to buy/review. It helps users choose their favorites. This manuscript uses hybrid associations models (HAM) to generate sequential recommendations based on three factors: users’ long-term preferences, sequential, high-order, and low-order association patterns in their most recent purchases/ratings, and synergies among those items.HAM represents associations with simplistic pooling and element-wise product. We compared HAM models to the latest, state-of-the-art methods on six public benchmark datasets in three experimental settings. In all experiments, HAM models outperform the state of the art. 46.6 percent better. HAM models also outperform state-of-the-art methods in run-time performance testing. and accelerate by 139.7 folds.
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