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个性化搜索的关键是基于历史行为构建用户画像。针对缺乏历史数据的用户,基于组的个性化模型被提出,这些方法在对结果重排时,将相似用户的画像考虑在内。然而,现有的寻找相似的用户的方法大多简单地基于搜索行为中词汇或主题的相似性。本文提出了一种基于神经网络的增强方法,在语义空间中突出相似用户的作用。此外,我们认为,当用户只包含有限的历史行为时,基于行为的相似用户仍然不足以帮助用户理解新的查询。为了解决这个问题,我们将朋友网络引入个性化搜索中,以另一种方式确定用户之间的亲密度关系。由于朋友关系往往是基于相似的背景或兴趣而形成的,所以在朋友网络中自然隐藏着大量个性化的信息。在搜索行为和朋友关系的融合下,相似用户在基于组的个性化搜索中更为可靠地得到了强化。具体来说,我们分别针对用户的搜索行为和朋友关系将其划分到多个朋友圈。这两种朋友圈是互补的,从而构建一个更全面的群体画像来实现搜索结果个性化。实验结果表明,与现有个性化模型相比,本文提出的模型有了显著的提升。

http://playbigdata.ruc.edu.cn/dou/publication/2021_SIGIR_FriendGraph.pdf

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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.

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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.

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