Learning a Joint Search and Recommendation Model from User-Item Interactions
Learning a Joint Search and Recommendation Model from User-Item Interactions
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DOI：
10.1145/3336191.3371818
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发表时间：
2020-01
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影响因子：
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通讯作者：
Hamed Zamani
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文献类型：
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作者：
Hamed Zamani
现有的信息检索排序模型学习是基于显式或隐式查询文档相关性信息进行训练的。在本文中，我们研究了学习基于用户-项目交互的检索模型的任务。我们的模型对于具有丰富的用户项交互数据的系统有潜在的应用，例如浏览和推荐，其中需要一个准确的搜索引擎。这包括媒体流服务和电子商务网站等。受到协同过滤的神经方法和信息检索的语言建模方法的启发，我们的模型被联合优化以预测用户-项目交互并重建项目文本描述。更详细地说，我们的模型学习用户和项目表示，以便它们可以准确预测未来的用户-项目交互，同时为每个项目生成有效的一元语言模型。我们在电影和产品搜索和推荐的背景下对四个不同数据集进行的实验表明，我们的模型除了提供与最先进的混合推荐模型相当的性能外，还大大优于竞争性检索基线。
Existing learning to rank models for information retrieval are trained based on explicit or implicit query-document relevance information. In this paper, we study the task of learning a retrieval model based on user-item interactions. Our model has potential applications to the systems with rich user-item interaction data, such as browsing and recommendation, in which having an accurate search engine is desired. This includes media streaming services and e-commerce websites among others. Inspired by the neural approaches to collaborative filtering and the language modeling approaches to information retrieval, our model is jointly optimized to predict user-item interactions and reconstruct the item textual descriptions. In more details, our model learns user and item representations such that they can accurately predict future user-item interactions, while generating an effective unigram language model for each item. Our experiments on four diverse datasets in the context of movie and product search and recommendation demonstrate that our model substantially outperforms competitive retrieval baselines, in addition to providing comparable performance to state-of-the-art hybrid recommendation models.