Entity recommendation, providing users with an improved search experience by proactively recommending related entities to a given query, has become an indispensable feature of today’s Web search engine. Existing studies typically only consider the query issued at the current timestep while ignoring the in-session user search behavior (short-term search history) or historical user search behavior across all sessions (long-term search history) when generating entity recommendations. As a consequence, they may fail to recommend entities of interest relevant to a user’s actual information need. In this work, we believe that both short-term and long-term search history convey valuable evidence that could help understand the user’s search intent behind a query, and take both of them into consideration for entity recommendation. Furthermore, there has been little work on exploring whether the use of other companion tasks in Web search such as document ranking as auxiliary tasks could improve the performance of entity recommendation. To this end, we propose a multi-task learning framework with deep neural networks (DNNs) to jointly learn and optimize two companion tasks in Web search engines: entity recommendation and document ranking, which can be easily trained in an end-to-end manner. Specifically, we regard document ranking as an auxiliary task to improve the main task of entity recommendation, where the representations of queries, sessions, and users are shared across all tasks and optimized by the multi-task objective during training. We evaluate our approach using large-scale, real-world search logs of a widely-used commercial Web search engine. We also performed extensive ablation experiments over a number of facets of the proposed multi-task DNN model to figure out their relative importance. The experimental results show that both short-term and long-term search history can bring significant improvements in recommendation effectiveness, and the combination of both outperforms using either of them individually. In addition, the experiments show that the performance of both entity recommendation and document ranking can be significantly improved, which demonstrates the effectiveness of using multi-task learning to jointly optimize the two companion tasks in Web search.

中文翻译：

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Web 搜索中实体推荐和文档排序的多任务学习

实体推荐通过主动向给定查询推荐相关实体来为用户提供改进的搜索体验，已成为当今 Web 搜索引擎不可或缺的功能。现有研究通常只考虑在当前时间步发出的查询，而在生成实体推荐时忽略会话中用户搜索行为（短期搜索历史）或所有会话中的历史用户搜索行为（长期搜索历史）。结果，他们可能无法推荐与用户的实际信息需求相关的感兴趣的实体。在这项工作中，我们相信短期和长期搜索历史都传达了有价值的证据，可以帮助理解用户在查询背后的搜索意图，并将它们都考虑到实体推荐中。此外，在探索在 Web 搜索中使用其他伴随任务（如文档排名）作为辅助任务是否可以提高实体推荐的性能方面的工作很少。为此，我们提出了一个带有深度神经网络 (DNN) 的多任务学习框架，用于联合学习和优化 Web 搜索引擎中的两个伴随任务：实体推荐和文档排名，可以轻松地进行端到端的训练方式。具体来说，我们将文档排序作为改进实体推荐主要任务的辅助任务，其中查询、会话和用户的表示在所有任务之间共享，并在训练期间通过多任务目标进行优化。我们使用广泛使用的商业 Web 搜索引擎的大规模真实搜索日志来评估我们的方法。我们还对提出的多任务 DNN 模型的多个方面进行了广泛的消融实验，以确定它们的相对重要性。实验结果表明，短期和长期搜索历史都可以显着提高推荐效果，并且两者的组合优于单独使用它们中的任何一个。此外，实验表明，实体推荐和文档排序的性能都可以显着提高，这证明了使用多任务学习联合优化 Web 搜索中的两个伴随任务的有效性。实验结果表明，短期和长期搜索历史都可以显着提高推荐效果，并且两者的组合优于单独使用它们中的任何一个。此外，实验表明，实体推荐和文档排序的性能都可以显着提高，这证明了使用多任务学习联合优化 Web 搜索中的两个伴随任务的有效性。实验结果表明，短期和长期搜索历史都可以显着提高推荐效果，并且两者的组合优于单独使用它们中的任何一个。此外，实验表明，实体推荐和文档排序的性能都可以显着提高，这证明了使用多任务学习联合优化 Web 搜索中的两个伴随任务的有效性。