计算机科学
滤波器(信号处理)
推荐系统
噪音(视频)
协方差
协同过滤
序列(生物学)
利用
偏爱
数据挖掘
高斯分布
情报检索
人工智能
数学
统计
计算机视觉
生物
量子力学
图像(数学)
物理
遗传学
计算机安全
作者
Haibo Liu,Z. Deng,Liang Wang,Jinjia Peng,Shi Feng
标识
DOI:10.1145/3604915.3608782
摘要
Sequential Recommendation aims to predict the next item by mining out the dynamic preference from user previous interactions. However, most methods represent each item as a single fixed vector, which is incapable of capturing the uncertainty of item-item transitions that result from time-dependent and multifarious interests of users. Besides, they struggle to effectively exploit side information that helps to better express user preferences. Finally, the noise in user’s access sequence, which is due to accidental clicks, can interfere with the next item prediction and lead to lower recommendation performance. To deal with these issues, we propose DLFS-Rec, a simple and novel model that combines Distribution-based Learnable Filters with Side information for sequential Recommendation. Specifically, items and their side information are represented by stochastic Gaussian distribution, which is described by mean and covariance embeddings, and then the corresponding embeddings are fused to generate a final representation for each item. To attenuate noise, stacked learnable filter layers are applied to smooth the fused embeddings. Extensive experiments on four public real-world datasets demonstrate the superiority of the proposed model over state-of-the-art baselines, especially on cold start users and items. Codes are available at https://github.com/zxiang30/DLFS-Rec.
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