AI driven Information Discovery in eCommerce

  in Special Issue   Posted on July 30, 2019

Information for the Special Issue

Submission Deadline: Wed 01 Apr 2020
Journal Impact Factor : 2.391
Journal Name : Information Processing and Management
Journal Publisher:
Website for the Special Issue:
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Special Issue Call for Papers:


Search, ranking, and recommendation have applications ranging from traditional web search, to document databases, to vertical search systems. In the age of big data, eCommerce websites have accumulated large amounts of user personal information and behavioral data. Moreover, human-generated and machine-generated business data has been experiencing an exponential growth. This calls for sophisticated technologies from a wide spectrum of areas including information retrieval, machine learning, artificial intelligence, statistics, econometrics, and psychology, to explore how to effectively take advantage of such high-volume data to drive sales and user experience.

In this special issue we will explore approaches for search, recommendations, business analytics, computational advertising, and other related aspects of Information Discovery in the eCommerce domain. The task is superficially the same as web-page search (fulfill a user\’s information need), but how this is achieved is very much different. On leading eCommerce websites (such as eBay, Flipkart, Amazon, and Alibaba), the traditional web-page ranking features are either not present or are present in a very different form. The entities that need to be discovered (the information that fulfills the need) might be unstructured, associated with structure, semi-structured, or have facets such as: price, ratings, title, description, seller location, and so on. Domains with such facets raise interesting research challenges such as a) relevance and ranking functions that take into account the tradeoffs across various facets with respect to the input query b) recommendations based on entity similarity, user location (e.g. shipping cost). These challenges require an inherent understanding of product attributes, user behavior, and the query context. Unlike document and web search, product sites are also characterized by the presence of a dynamic inventory with a high rate of change and turnover, and a long tail of query distribution.

Outside of search but still within Information Retrieval, the same feature in different domains can have radically different meaning. For example, in email filtering the presence of “Ray-Ban” along with a price is a strong indication of spam, but within an auction setting this likely indicates a valid product for sale. Another example is natural language translation; company names, product names, and even product descriptions do not translate well with existing tools. Similar problems exist with knowledge graphs that are not customized to match the product domain. In addition to the above topics, this special issue will also focus on AI and machine learning enhanced business analytics approaches for understanding online shopping and consumer behaviors. Another area of focus is computational modeling and analysis of advertising and other promotional forms in eCommerce. The main objective of this special issue is to publish an up-to-date high-quality set of papers that deal with AI driven information discovery in the eCommerce domain.

All journal submissions will be reviewed by at least three reviewers recruited by the editors of the special issue. We expect to accept about 10 papers.

Topics of interest


The special issue relates to all aspects of eCommerce search and recommendations. Research topics and challenges that are usually encountered in this domain include:

  • Machine learning techniques such as online learning and deep learning for eCommerce applications
  • Semantic representation for users, products, and services & Semantic understanding of queries
  • Structured data and faceted search, for example, converting unstructured data to its structured form
  • The use of domain specific facets in search and other IR tasks, and how those facets are chosen
  • Counterfactual learning
  • Query intent, suggestion, and auto-completion
  • Temporal dynamics for Search and Recommendation
  • Models for relevance and ranking for multi-faceted entities
  • Recall-oriented search for eCommerce including deterministic sorting of results lists (e.g. price low to high)
  • Click models for eCommerce domain
  • Session aware, and session-oriented search and recommendation
  • Construction and use of knowledge graph, and ontologies for search and recommendations
  • Personalization & contextualization, and the use of personal facets such as age, gender, location etc.
  • Indexing and search in a rapidly changing environment (for example, an auction site)
  • Efficiency and scalability
  • Diversity in product search and recommendations
  • Strategies for resolving extremely low (or no) recall queries
  • The use of external features such as reviews and ratings in ranking
  • User interfaces (mobile, desktop, voice, etc.) and personalization
  • Reviews and sentiment analysis
  • The use of social signals in ranking and beyond
  • The balance between business requirements and user requirements (revenue vs relevance)
  • Trust and security
  • Live experimentation
  • Questions and answering, chat bots for eCommerce
  • Cross-Lingual search and machine translation
  • Fashion eCommerce
  • Resources and data sets
  • Computational advertising
  • Display advertising
  • Sponsored search advertising
  • Keyword advertising
  • Social advertising
  • Real-time bidding
  • Recommender systems
  • Advertising personalization
  • Advertising decisions and strategy optimization
  • Advertising retrieval
  • Ecommerce Analytics
  • Real-time recommendation
  • Big data analytics in eCommerce
  • Predictive Analytics for eCommerce
  • Retail Analytics in eCommerce

Important Dates

Time lines for a journal special issue are often dictated by the number of revisions a paper might need and the time it takes to make a set of edits, however the following dates are expected to be followed:

  • Initial submissions due: November 15, 2019
  • Initial reviewer feedback: February 1, 2020
  • Second Submission: April 1, 2020
  • Final Decision: May 1, 2020
  • Publication: June 1, 2020

Special Issue Editors

The editors are listed here in alphabetical order by last name.

Surya Kallumadi

Surya is a Data Scientist with The Home Depot\’s search science team with a focus on core search, query understanding, and ranking. He received his Ph.D. from Kansas State University. His thesis research was in the area of recommendations in heterogeneous information networks. He has worked in the industry on multiple occasions as a research intern at eBay in the fields of information retrieval and structured data. He has worked with the data science team at Flipkart in the fields of search and query understanding. Surya is the co-chair of the SIGIR workshop on eCommerce, in addition he has organized 3 Heterogeneous Information Network Analysis (HINA \’11,\’13,\’15) workshops at IJCAI.

Andrew Trotman

Andrew gained a B.A. in the 1980s, an M.Sc. in the 1990s, and a Ph.D. in the 2000s. He has worked in the field of information retrieval since 1992 when he worked in industry designing and implementing one of the first commercially successful digital libraries (BioMedNet). He has worked in industry for a total of 11 years at such establishments as: Elsevier, PubMed, and eBay. His academic career spanned 12 years at the University of Otago (New Zealand) and has seen him as a member of the SIGIR Executive committee (2010-2013), general chair for SIGIR 2014, INEX (2008-2010), ADCS 2012, and ADCS 2018, as well as program chair for ADCS 2009 and ADCS 2010, and a frequent member of the senior program committee for top conferences such as SIGIR and CIKM. Andrew has published over 100 papers (according to Google Scholar), has chaired 8 SIGIR workshops, and is the designer and primary author of the ATIRE and JASS open source search engines. Andrew has been a co-editor of 2 special issues of the Information Retrieval Journal.

Yanwu Yang

Yanwu is a full professor in the School of Management, Huazhong University of Science and Technology, and head of the ISEC research group (Internet Sciences and Economic Computing). His research interests include computational advertising, advertising decisions, web personalization, and user modeling. Yang has a Ph.D in computer science from the graduate school of l’ecole Nationale Superieure d\’Arts et Metiers. He serves as an associate editor or editorial board member for several journals including Information Processing & Management and The Journal of Finance and Data Science.

Yulei Zhang

Yulei is an associate professor of Information Systems in the W. A. Franke College of Business at Northern Arizona University. He got his Ph.D in Management Information Systems from the University of Arizona. His research interests include social media analytics, text and web mining, knowledge management, human computer interaction and information technology adoption.

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