使用段特定上下文嵌入的查询聚类
Query Clustering using Segment Specific Context Embeddings
摘要：
本文提出了一种新的查询聚类方法来捕捉用户查询搜索引擎的广泛兴趣领域。我们利用NLP -word 2 vec的最新进展，并将其扩展到query 2 vec，查询的矢量表示，基于查询上下文，从查询的顶部搜索结果中获得，并在查询矢量上使用高度可扩展的Divide & Merge聚类算法，以获得聚类。我们已经在零售、旅游、健康、电话等多个细分市场尝试了这种方法，发现集群在发现具有高盈利潜力的用户兴趣领域方面非常有效。
英文摘要：
This paper presents a novel query clustering approach to capture the broad interest areas of users querying search engines. We make use of recent advances in NLP - word2vec and extend it to get query2vec, vector representations of queries, based on query contexts, obtained from the top search results for the query and use a highly scalable Divide & Merge clustering algorithm on top of the query vectors, to get the clusters. We have tried this approach on a variety of segments, including Retail, Travel, Health, Phones and found the clusters to be effective in discovering user's interest areas which have high monetization potential.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
