Recurrent Recommendation with Local Coherence
Recurrent Recommendation with Local Coherence
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DOI：
10.1145/3289600.3291024
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发表时间：
2019-01
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影响因子：
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通讯作者：
Jianling Wang;James Caverlee
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文献类型：
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作者：
Jianling Wang;James Caverlee
我们提出了一种新的以局部一致性为中心的用户-项目评分的时间相关预测模型——也就是说，虽然用户和项目都在不断变化，但在短期序列内，特定用户或项目的邻域可能是一致的。该框架的三个独特特征是：（i）它通过提取隐藏在反馈序列中的局部连贯性来合并隐式和显式反馈； (ii) 它使用并行循环神经网络来捕获用户和项目的演变，从而产生双因素推荐模型； (iii)它结合了连贯性增强的一致潜在因素和动态潜在因素，以平衡短期变化与长期趋势，以改进推荐。通过在 Goodreads 和 Amazon 上的实验，我们发现所提出的模型在预测用户偏好方面可以优于最先进的模型。
We propose a new time-dependent predictive model of user-item ratings centered around local coherence -- that is, while both users and items are constantly in flux, within a short-term sequence, the neighborhood of a particular user or item is likely to be coherent. Three unique characteristics of the framework are: (i) it incorporates both implicit and explicit feedbacks by extracting the local coherence hidden in the feedback sequences; (ii) it uses parallel recurrent neural networks to capture the evolution of users and items, resulting in a dual factor recommendation model; and (iii) it combines both coherence-enhanced consistent latent factors and dynamic latent factors to balance short-term changes with long-term trends for improved recommendation. Through experiments on Goodreads and Amazon, we find that the proposed model can outperform state-of-the-art models in predicting users' preferences.