Nish Parikh

Query suggestion for E-commerce sites

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.

Inferring semantic query relations from collective user behavior

In this paper we describe how high quality transaction data comprising of online searching, product viewing, and product buying activity of a large online community can be used to infer semantic relationships between queries. We work with a large scale query log consisting of around 115 million queries from eBay. We discuss various techniques to infer semantic relationships among queries and show how the results from these methods can be combined to measure the strength and depict the kinds of relationships. Further, we show how this extraction of relations can be used to improve search relevance, related query recommendations, and recovery from null results in an eCommerce context.

Rewriting null e-commerce queries to recommend products

In e-commerce applications product descriptions are often concise. E-Commerce search engines often have to deal with queries that cannot be easily matched to product inventory resulting in zero recall or null query situations. Null queries arise from differences in buyer and seller vocabulary or from the transient nature of products. In this paper, we describe a system that rewrites null e-commerce queries to find matching products as close to the original query as possible. The system uses query relaxation to rewrite null queries in order to match products. Using eBay as an example of a dynamic marketplace, we show how using temporal feedback that respects product category structure using the repository of expired products, we improve the quality of recommended results. The system is scalable and can be run in a high volume setting. We show through our experiments that high quality product recommendations for more than 25% of null queries are achievable.

Buzz-based recommender system

In this paper, we describe a buzz-based recommender system based on a large source of queries in an eCommerce application. The system detects bursts in query trends. These bursts are linked to external entities like news and inventory information to find the queries currently in-demand which we refer to as buzz queries. The system follows the paradigm of limited quantity merchandising, in the sense that on a per-day basis the system shows recommendations around a single buzz query with the intent of increasing user curiosity, and improving activity and stickiness on the site. A semantic neighborhood of the chosen buzz query is selected and appropriate recommendations are made on products that relate to this neighborhood.

User behavior in zero-recall ecommerce queries

User expectation and experience for web search and eCommerce (product) search are quite different. Product descriptions are concise as compared to typical web documents. User expectation is more specific to find the right product. The difference in the publisher and searcher vocabulary (in case of product search the seller and the buyer vocabulary) combined with the fact that there are fewer products to search over than web documents result in observable numbers of searches that return no results (zero recall searches). In this paper we describe a study of zero recall searches. Our study is focused on eCommerce search and uses data from a leading eCommerce sites user click stream logs. There are 3 main contributions of our study: 1) The cause of zero recall searches; 2) A study of users reaction and recovery from zero recall; 3) A study of differences in behavior of power users versus novice users to zero recall searches.

A Study of Query Term Deletion Using Large-Scale E-commerce Search Logs

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. Specifically, 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. Our approach is data-driven. We investigate the large-scale query history and the document collection, verify the usefulness of previously proposed features, and also propose to incorporate the query category information into the term deletion predictors. We observe that training within-category classifiers can result in much better performance than training a unified classifier. We validate our approach using a large collection of query sessions logs from a leading e-commerce site and demonstrate that our approach provides promising performance in query term deletion prediction.

E-commerce product search: personalization, diversification, and beyond

The focus of this tutorial will is e-commerce product search. Several challenges appear in this context, both from a research standpoint as well as an application standpoint. We present various approaches adopted in the industry, review well-known research techniques developed over the last decade, draw parallels to traditional web search highlighting the new challenges in this setting, and dig deep into some of the algorithmic and technical approaches developed. A specific approach that advances theoretical techniques and illustrates practical impact considered here is of identifying most suited results quickly from a large database. Settings span cold start users and advanced users for whom personalization is possible. In this context, top-

A user-tunable approach to marketplace search

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Modeling Seller Listing Strategies

and skylines are discussed as they form a key approach that spans the web, data mining, and database communities. These present powerful tools for search across multi-dimensional items with clear preferences within each attribute, like product search as opposed to regular web search.

A software system for buzz-based recommendations

The notion of relevance is key to the performance of search engines as they interpret the user queries and respond with matching results. Online search engines have used other features beyond pure IR features to return relevant matching documents. However, over-emphasis on relevance could lead to redundancy in search results. In document search, diversity is simply the variety of documents that span the result set. In an online marketplace the diversity in the result set is represented by items for sale by different sellers at different prices with different sales options. For such a marketplace, in order to minimize query abandonment and the risk of dissatisfaction to the average user, several factors like diversity, trust and value need to be taken into account. Previous work in this field [4] has shown an impossibility result that there exists no such function that can optimize for all these factors. Since these factors and the measures associated with the factors could be subjective we take an approach of giving the control back to the user. In this paper we describe an interface which enables users to have more control over the optimization function used to present the results. We demonstrate this for search on eBay - one of the largest online marketplaces with a vibrant user community and dynamic inventory. We use an algorithm based on bounded greedy selection [5] to construct the result set based on parameters specified by the user.