Query suggestion for E-commerce sites

WSDM 2011: 765-774, Hong Kong, February 2011
Query suggestion for E-commerce sites
Mohammad AlHasan, Nish Parikh, Gyanit Singh, Neel Sundaresan
eBay Authors

Query suggestion module is an integral part of every search engine. It helps search engine users narrow or broaden their searches. Published work on query suggestion methods has mainly focused on the web domain. But, the module is also popular in the domain of e-commerce for product search.

In this paper, we discuss query suggestion and its methodologies in the context of e-commerce search engines. We show that dynamic inventory combined with long and sparse tail of query distribution poses unique challenges to build a query suggestion method for an e-commerce marketplace.

We compare and contrast the design of a query suggestion system for web search engines and e-commerce search engines. Further, we discuss interesting measures to quantify the effectiveness of our query suggestion methodologies. We also describe the learning gained from exposing our query suggestion module to a vibrant community of millions of users.

Another publication from the same author: Nish Parikh

In proceedings of the Workshop on Log-based Personalization (the 4th WSCD workshop) at WSDM 2014

A Large Scale Query Logs Analysis for Assessing Personalization Opportunities in E-commerce Sites

Neel Sundaresan, Zitao Liu

Personalization offers the promise of improving online search and shopping experience. In this work, we perform a large scale analysis on the sample of eBay query logs, which involves 9.24 billion session data spanning 12 months (08/2012-07/2013) and address the following topics

(1) What user information is useful for personalization;

(2) Importance of per-query personalization

(3) Importance of recency in query prediction.

In this paper, we study these problems and provide some preliminary conclusions


Another publication from the same category: Machine Learning and Data Science

WWW '17 Perth Australia April 2017

Drawing Sound Conclusions from Noisy Judgments

David Goldberg, Andrew Trotman, Xiao Wang, Wei Min, Zongru Wan

The quality of a search engine is typically evaluated using hand-labeled data sets, where the labels indicate the relevance of documents to queries. Often the number of labels needed is too large to be created by the best annotators, and so less accurate labels (e.g. from crowdsourcing) must be used. This introduces errors in the labels, and thus errors in standard precision metrics (such as P@k and DCG); the lower the quality of the judge, the more errorful the labels, consequently the more inaccurate the metric. We introduce equations and algorithms that can adjust the metrics to the values they would have had if there were no annotation errors.

This is especially important when two search engines are compared by comparing their metrics. We give examples where one engine appeared to be statistically significantly better than the other, but the effect disappeared after the metrics were corrected for annotation error. In other words the evidence supporting a statistical difference was illusory, and caused by a failure to account for annotation error.