Research Area: Machine Learning
Sequential recommendation aims to identify and recommend the next few items for a user that the user is most likely to purchase/review, given the users purchase/rating trajectories. It becomes an effective tool to help users select favorite items from a variety of options. In this manuscript, we developed hybrid associations models (HAM) to generate sequential recommendations using three factors: 1) users long-term preferences, 2) sequential, high-order and low-order association patterns in the users most recent purchases/ratings, and 3) synergies among those items. HAM uses simplistic pooling to represent a set of items in the associations, and element-wise product to represent item synergies of arbitrary orders. We compared HAM models with the most recent, state-of-the-art methods on six public benchmark datasets in three different experimental settings. Our experimental results demonstrate that HAM models significantly outperform the state of the art in all the experimental settings, with an improvement as much as 46.6%. In addition, our run-time performance comparison in testing demonstrates that HAM models are much more efficient than the state-of-the-art methods, and are able to achieve significant speedup as much as 139.7 folds.
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Author(s) Name: Bo Peng; Zhiyun Ren; Srinivasan Parthasarathy; Xia Ning
Journal name: IEEE Transactions on Knowledge and Data Engineering ( Early Access )
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Publisher name: IEEE
DOI: 10.1109/TKDE.2021.3049692
Volume Information: Page(s): 1 - 1
