Refining Product Search Algorithms Through Modeling
In a significant stride towards advancing product search models, Amazon has created a unique dataset composed of search queries, their associated potential results, and judgment labels for the relevance of results. This dataset, however, is not publicly available in a specific form that includes "difficult search queries in multiple languages."
The dataset, totalling 97,345 English queries, 15,180 Spanish queries, and 18,127 Japanese queries, is intended to advance research in ranking strategies for product search models. It includes up to 40 potential results for each query, providing a diverse range of search queries, as evidenced by the number of queries in each language.
While Amazon does not openly publish a dataset explicitly described as "difficult search queries" in multiple languages, there are alternative ways to obtain data relevant to this purpose.
- Using Amazon's Customer Reviews or Product Metadata Datasets: These datasets, publicly shared in some research contexts, can be used as a proxy and supplemented with manual or automatic generation of query data.
- Building a Multilingual Scraper: Targeting Amazon localized sites (for example, amazon.co.uk, amazon.de, amazon.co.jp), you can collect search results using tools like Octoparse, ScrapeStorm, or ParseHub. Analyzing these search results can help derive "difficult queries" by manually assessing search success or product relevance.
- Exploring Academic Datasets: Datasets related to search queries in multilingual settings, available from research datasets repositories or conferences, can also be considered for practice or research on complex or multilingual search queries.
It's essential to note that these alternatives would not be Amazon-specific, but they can provide valuable insights into multilingual search queries and product search models.
The image used in this article does not provide evidence of the diverse range of search queries in the dataset, nor does it contain search queries or potential results, judgment labels for the relevance of results, or relate to the advancement of ranking strategies for product search models. Image credit for this article is provided by Flickr user Robbert Noordzij.
- The absence of an Amazon-specific dataset containing 'difficult search queries' in multiple languages can be addressed through the use of other relevant datasets, such as Amazon's Customer Reviews or Product Metadata, or by building a multilingual scraper to collect search results from localized Amazon sites.
- Academic datasets related to search queries in multilingual settings, available from research datasets repositories or conferences, can also be considered for practice or research on complex or multilingual search queries, contributing to the wider field of 'research' in 'data-and-cloud-computing' and 'AI' technology.
