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

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
Categories
eBay Authors
Abstract

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 author:

In ECIR 2014 (To Appear)

A Study of Query Term Deletion using Large-scale E-commerce Search Logs

Bishan Yang, Nish Parikh, Gyanit Singh, Neel Sundaresan

Query term deletion is one of the commonly used strategies for query rewriting. In this paper, we study the problem of query term deletion using large-scale e-commerce search logs. Especially we focus on queries that do not lead to user clicks and aim to predict a reduced and better query that can lead to clicks by term deletion. Accurate prediction of term deletion can potentially help users recover from poor search results and improve shopping experience.

To achieve this,we use various term-dependent and query-dependent measures as features and build a classifier to predict which term is the most likely to be deleted from a given query. Different from previous work on query term deletion, we compute the features not only based on the query history and the available document collection, but also conditioned on the query category, which captures the high-level context of the query.

We validate our approach using a large collection of query sessions logs from a leading e-commerce site, and show that it provides promising performance in term deletion prediction, and significantly outperforms baselines that rely on query history and corpus-based statistics without incorporating the query context information.

Keywords

Another publication from the same category: Economics

Proceedings of the Sixteenth ACM Conference on Economics and Computation (EC '15). ACM, New York, NY, USA (2015)

Canary in the e-Commerce Coal Mine: Detecting and Predicting Poor Experiences Using Buyer-to-Seller Messages

Dimitriy Masterov, Uwe Mayer, Steve Tadelis

Reputation and feedback systems in online marketplaces are often biased, making it difficult to ascertain the quality of sellers. We use post-transaction, buyer-to-seller message traffic to detect signals of unsatisfactory transactions on eBay. We posit that a message sent after the item was paid for serves as a reliable indicator that the buyer may be unhappy with that purchase, particularly when the message included words associated with a negative experience. The fraction of a seller's message traffic that was negative predicts whether a buyer who transacts with this seller will stop purchasing on eBay, implying that platforms can use these messages as an additional signal of seller quality.