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主题： Learning Term Discrimination
摘要： 文档索引是有效信息检索（IR）的关键组件。经过诸如词干和停用词删除之类的预处理步骤之后，文档索引通常会存储term-frequencies（tf）。与tf（仅反映一个术语在文档中的重要性）一起，传统的IR模型使用诸如反文档频率（idf）之类的术语区分值（TDV）在检索过程中偏向于区分性术语。在这项工作中，我们建议使用浅层神经网络学习TDV，以进行文档索引，该浅层神经网络可以近似TF-IDF和BM25等传统的IR排名功能。我们的建议在nDCG和召回方面均优于传统方法，即使很少有带有正标签的查询文档对作为学习数据。我们学到的TDV用于过滤区分度为零的词汇，不仅可以显着降低倒排索引的内存占用量，而且可以加快检索过程（BM25的速度提高了3倍），而不会降低检索质量。
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The 2021 SIGIR workshop on eCommerce is hosting the Coveo Data Challenge for "In-session prediction for purchase intent and recommendations". The challenge addresses the growing need for reliable predictions within the boundaries of a shopping session, as customer intentions can be different depending on the occasion. The need for efficient procedures for personalization is even clearer if we consider the e-commerce landscape more broadly: outside of giant digital retailers, the constraints of the problem are stricter, due to smaller user bases and the realization that most users are not frequently returning customers. We release a new session-based dataset including more than 30M fine-grained browsing events (product detail, add, purchase), enriched by linguistic behavior (queries made by shoppers, with items clicked and items not clicked after the query) and catalog meta-data (images, text, pricing information). On this dataset, we ask participants to showcase innovative solutions for two open problems: a recommendation task (where a model is shown some events at the start of a session, and it is asked to predict future product interactions); an intent prediction task, where a model is shown a session containing an add-to-cart event, and it is asked to predict whether the item will be bought before the end of the session.
最新论文
The 2021 SIGIR workshop on eCommerce is hosting the Coveo Data Challenge for "In-session prediction for purchase intent and recommendations". The challenge addresses the growing need for reliable predictions within the boundaries of a shopping session, as customer intentions can be different depending on the occasion. The need for efficient procedures for personalization is even clearer if we consider the e-commerce landscape more broadly: outside of giant digital retailers, the constraints of the problem are stricter, due to smaller user bases and the realization that most users are not frequently returning customers. We release a new session-based dataset including more than 30M fine-grained browsing events (product detail, add, purchase), enriched by linguistic behavior (queries made by shoppers, with items clicked and items not clicked after the query) and catalog meta-data (images, text, pricing information). On this dataset, we ask participants to showcase innovative solutions for two open problems: a recommendation task (where a model is shown some events at the start of a session, and it is asked to predict future product interactions); an intent prediction task, where a model is shown a session containing an add-to-cart event, and it is asked to predict whether the item will be bought before the end of the session.
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