Various e-commerce platforms allow sellers to register, describe and organize their own products, using tags and other textual metadata. The quality of these textual descriptors is essential for the effectiveness of e-commerce information services such as search and product recommendation, and thus, for the ability of consumers to find desired products. In this paper, we focus on a particular, widely used textual descriptors of products, tags. We argue that sellers may not be the “best” providers of tag information for products either because of their inability to do so (they were not “trained” for that) or due to an explicit intent to fool the system in order to promote their products with inadequate or imprecise tags (tag spam). To deal with these issues, we may rely on automatic tag recommendation techniques to improve the quality of the tags suggested to describe a given product. In this context, the main novel contribution of our work is a set of new tag recommendation techniques that take advantage of product search result data (in particular the search queries and product clicks from these queries) to improve the quality of the recommended tags. Our main hypothesis is that the set of queries collectively issued by the consumers of the e-market place, along with corresponding clicks, reflect a more trustworthy view of the products; thus those queries and clicks can be exploited as a source of high quality (e.g., more diverse) tags to describe the products. We propose new solutions, including some based on deep learning, that translate this main hypothesis into new features and methods for recommending tags for products. Our manual and automatic evaluations, using real data from one of the largest e-commerce sites in Brazil, show that indeed tags created by sellers contain a lot of noise. On the other hand, our proposed search-boosted tag recommenders are highly effective in suggesting relevant tags, with gains of more than 16% in recommendation effectiveness against the state-of-the-art. Even more, our experiments show that the suggested tags provide a potentially better data source for e-commerce search than the original tags assigned by product sellers.

各种电子商务平台允许卖家使用标签和其他文本元数据来注册，描述和组织自己的产品。这些文本描述符的质量对于电子商务信息服务（例如搜索和产品推荐）的有效性至关重要，因此对于消费者找到所需产品的能力也至关重要。在本文中，我们重点关注产品，标签的特定，广泛使用的文本描述符。我们认为，卖方可能不是由于产品标签信息的“最佳”提供者（由于其这样做能力不强（他们没有对此进行“培训”），或者由于明显意图欺骗该系统以促进其产品销售而未成为卖方。标签标记不正确或不准确的产品（标记垃圾邮件）。为了解决这些问题，我们可能会依靠自动标签推荐技术，可提高建议描述给定产品的标签质量。在这种情况下，我们工作的主要新颖贡献是一套新的标签推荐技术，它们利用产品搜索结果数据（尤其是这些查询的搜索查询和产品点击）来提高推荐标签的质量。我们的主要假设是，由电子市场的消费者集体发出的查询集以及相应的点击次数反映了对产品更可信的看法；因此，这些查询和点击可被用作描述产品的高质量（例如，更多样化）标签的来源。我们提出了新的解决方案，包括一些基于深度学习的解决方案，这些解决方案将这一主要假设转化为推荐产品标签的新功能和新方法。我们使用来自巴西最大的电子商务网站之一的真实数据进行的手动和自动评估表明，卖家创建的标签确实包含很多噪音。另一方面，我们提出的搜索增强型标签推荐器在建议相关标签方面非常有效，与最新技术相比，推荐效果提高了16％以上。更进一步，我们的实验表明，与产品销售商分配的原始标签相比，建议的标签为电子商务搜索提供了可能更好的数据源。