Where Can I Buy a Boulder?: Searching for Offline Retail Locations

Sandro Bauer, Filip Radlinski, Ryen W. White
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引用次数: 3

Abstract

People commonly need to purchase things in person, from large garden supplies to home decor. Although modern search systems are very effective at finding online products, little research attention has been paid to helping users find places that sell a specific product offline. For instance, users searching for an apron are not typically directed to a nearby kitchen store by a standard search engine. In this paper, we investigate "where can I buy"-style queries related to in-person purchases of products and services. Answering these queries is challenging since little is known about the range of products sold in many stores, especially those which are smaller in size. To better understand this class of queries, we first present an in-depth analysis of typical offline purchase needs as observed by a major search engine, producing an ontology of such needs. We then propose ranking features for this new problem, and learn a ranking function that returns stores most likely to sell a queried item or service, even if there is very little information available online about some of the stores. Our final contribution is a new evaluation framework that combines distance with store relevance in measuring the effectiveness of such a search system. We evaluate our method using this approach and show that it outperforms a modern web search engine.
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我在哪里可以买到巨石?:搜索线下零售地点
人们通常需要亲自购买物品,从大型园艺用品到家居装饰。尽管现代搜索系统在寻找在线产品方面非常有效,但很少有研究关注如何帮助用户找到线下销售特定产品的地方。例如,搜索围裙的用户通常不会被标准搜索引擎引导到附近的厨房商店。在本文中,我们研究了与亲自购买产品和服务相关的“我可以在哪里购买”式查询。回答这些问题是具有挑战性的,因为人们对许多商店出售的产品范围知之甚少,尤其是那些尺寸较小的产品。为了更好地理解这类查询,我们首先对一个主要搜索引擎观察到的典型离线购买需求进行了深入分析,生成了这类需求的本体。然后,我们为这个新问题提出了排名特性,并学习了一个排名函数,该函数返回最有可能出售所查询的商品或服务的商店,即使关于某些商店的在线信息很少。我们最后的贡献是一个新的评估框架,它结合了距离和商店相关性来衡量这样一个搜索系统的有效性。我们使用这种方法评估我们的方法,并表明它优于现代网络搜索引擎。
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