Related or ideal follow-up suggestions to a web query in search engines are often optimized based on several different parameters -- relevance to the original query, diversity, click probability etc. One or many rankers may be trained to score each suggestion from a candidate pool based on these factors. These scorers are usually pairwise classification tasks where each training example consists of a user query and a single suggestion from the list of candidates. We propose an architecture that takes all candidate suggestions associated with a given query and outputs a suggestion block. We discuss the benefits of such an architecture over traditional approaches and experiment with further enforcing each individual metric through mixed-objective training.
翻译:对搜索引擎中的网络查询的相关或理想的后续建议往往根据若干不同参数优化 -- -- 与原始查询、多样性、点击概率等有关。 一名或多名军阶人员可接受培训,以便根据这些因素从候选人人才库中评分每项建议。这些得分者通常是对称的分类任务,其中每个培训范例包括用户查询和候选人名单中单一建议。我们建议一个结构,接受与特定查询和产出有关的所有候选人建议,一个建议块。我们讨论了这种架构比传统办法的好处,并试验如何通过混合目标培训进一步实施每项指标。