Matching User with Item Set: Collaborative Bundle Recommendation with Deep Attention Network
Matching User with Item Set: Collaborative Bundle Recommendation with Deep Attention Network
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DOI：
10.24963/ijcai.2019/290
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发表时间：
2019-08
期刊：
影响因子：
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通讯作者：
Liang Chen;Yang Liu;Xiangnan He;Lianli Gao;Zibin Zheng
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文献类型：
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作者：
Liang Chen;Yang Liu;Xiangnan He;Lianli Gao;Zibin Zheng
大多数推荐研究都集中在向用户推荐单个项目上，例如对用户和项目之间交互建模的协作过滤的大量工作。然而，在许多现实场景中，平台需要向用户展示一组物品，例如，将多个物品作为一个捆绑包出售的营销策略。在这项工作中，我们考虑向用户推荐一组项目，即Bundle Recommendation任务，它涉及用户和一组项目之间的交互建模。我们提出了一种名为DAM的神经网络解决方案，即Deep attention Multi-Task model的缩写，它有两个特殊的设计：1)我们设计了一个分解关注网络，将项目嵌入聚合到一个bundle中，得到bundle的表示；2)我们以多任务的方式联合建模用户束交互和用户项交互，以缓解用户束交互的稀缺性。在真实数据集上的大量实验表明，DAM优于最先进的解决方案，验证了我们在DAM中注意力设计和多任务学习的有效性。
Most recommendation research has been concentrated on recommending single items to users, such as the considerable work on collaborative filtering that models the interaction between a user and an item. However, in many real-world scenarios, the platform needs to show users a set of items, e.g., the marketing strategy that offers multiple items for sale as one bundle.In this work, we consider recommending a set of items to a user, i.e., the Bundle Recommendation task, which concerns the interaction modeling between a user and a set of items. We contribute a neural network solution named DAM, short for Deep Attentive Multi-Task model, which is featured with two special designs: 1) We design a factorized attention network to aggregate the item embeddings in a bundle to obtain the bundle's representation; 2) We jointly model user-bundle interactions and user-item interactions in a multi-task manner to alleviate the scarcity of user-bundle interactions. Extensive experiments on a real-world dataset show that DAM outperforms the state-of-the-art solution, verifying the effectiveness of our attention design and multi-task learning in DAM.