Challenges and research opportunities in eCommerce search and recommendations

With the rapid adoption of online shopping, academic research in the eCommerce domain has gained traction. However, significant research challenges remain, spanning from classic eCommerce search problems such as matching textual queries to multi-modal documents and ranking optimization for two-sided marketplaces to human-computer interaction and recommender systems for discovery and browsing. These research areas are important for understanding customer behavior, driving engagement, and improving product discoverability and conversion. In this article we identify the challenges and highlight research opportunities to improve the eCommerce customer experience.

[1]  Jamie Callan,et al.  Deeper Text Understanding for IR with Contextual Neural Language Modeling , 2019, SIGIR.

[2]  Mark Sanderson,et al.  How Do People Interact in Conversational Speech-Only Search Tasks: A Preliminary Analysis , 2017, CHIIR.

[3]  Yujing Hu,et al.  Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application , 2018, KDD.

[4]  Matthew Pearson,et al.  Bias and Reciprocity in Online Reviews: Evidence From Field Experiments on Airbnb , 2015, EC.

[5]  Fernando Diaz,et al.  Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommendation Systems , 2018, CIKM.

[6]  Thomas Blake,et al.  Returns to Consumer Search: Evidence from eBay , 2016, EC.

[7]  Yiqun Liu,et al.  User Intent, Behaviour, and Perceived Satisfaction in Product Search , 2018, WSDM.

[8]  Xiao Li,et al.  Learning query intent from regularized click graphs , 2008, SIGIR '08.

[9]  Krisztian Balog,et al.  Head First: Living Labs for Ad-hoc Search Evaluation , 2014, CIKM.

[10]  Steven Tadelis Two-sided e-commerce marketplaces and the future of retailing , 2016 .

[11]  Beste F. Yuksel,et al.  Brains or Beauty , 2017, ACM Trans. Internet Techn..

[12]  Jie Yang,et al.  The Role of Attributes in Product Quality Comparisons , 2020, CHIIR.

[13]  Milad Shokouhi,et al.  Behavioral dynamics on the web: Learning, modeling, and prediction , 2013, TOIS.

[14]  Andrei Broder,et al.  A taxonomy of web search , 2002, SIGF.

[15]  W. Bruce Croft,et al.  User Intent Prediction in Information-seeking Conversations , 2019, CHIIR.

[16]  Ricardo Baeza-Yates Semantic Query Understanding , 2017, SIGIR '17.

[17]  Elad Haramaty,et al.  Why Do People Buy Seemingly Irrelevant Items in Voice Product Search?: On the Relation between Product Relevance and Customer Satisfaction in eCommerce , 2020, WSDM.

[18]  Craig MacDonald,et al.  Exploiting query reformulations for web search result diversification , 2010, WWW '10.

[19]  Iadh Ounis,et al.  FACTS-IR: Fairness, Accountability, Confidentiality, Transparency, and Safety in Information Retrieval , 2019 .

[20]  M. de Rijke,et al.  Improving Outfit Recommendation with Co-supervision of Fashion Generation , 2019, WWW.

[21]  Mounia Lalmas,et al.  Tutorial on Online User Engagement: Metrics and Optimization , 2019, WWW.

[22]  Marilyn A. Walker,et al.  Learning to Predict Problematic Situations in a Spoken Dialogue System: Experiments with How May I Help You? , 2000, ANLP.

[23]  Mark Sanderson,et al.  Extracting audio summaries to support effective spoken document search , 2017, J. Assoc. Inf. Sci. Technol..

[24]  Andrew Trotman,et al.  The Architecture of eBay Search , 2017, eCOM@SIGIR.

[25]  Maxine Eskénazi,et al.  Let's go public! taking a spoken dialog system to the real world , 2005, INTERSPEECH.

[26]  Tefko Saracevic,et al.  RELEVANCE: A review of and a framework for the thinking on the notion in information science , 1997, J. Am. Soc. Inf. Sci..

[27]  Filip Radlinski,et al.  Towards Conversational Recommender Systems , 2016, KDD.

[28]  Oren Kurland,et al.  Query Expansion Using Word Embeddings , 2016, CIKM.

[29]  Shubhra Kanti Karmaker Santu,et al.  On Application of Learning to Rank for E-Commerce Search , 2017, SIGIR.

[30]  Maarten de Rijke,et al.  OpenSearch: Lessons Learned from an Online Evaluation Campaign , 2018, ACM J. Data Inf. Qual..

[31]  Paul N. Bennett,et al.  Leading Conversational Search by Suggesting Useful Questions , 2020, WWW.

[32]  Hinda Haned,et al.  Actionable Interpretability through Optimizable Counterfactual Explanations for Tree Ensembles , 2019, ArXiv.

[33]  Faizan Javed,et al.  JointMap: Joint Query Intent Understanding For Modeling Intent Hierarchies in E-commerce Search , 2020, SIGIR.

[34]  David Carmel,et al.  Promoting Relevant Results in Time-Ranked Mail Search , 2017, WWW.

[35]  Henriette Cramer,et al.  Assessing and addressing algorithmic bias in practice , 2018, Interactions.

[36]  M. de Rijke,et al.  Do News Consumers Want Explanations for Personalized News Rankings , 2017 .

[37]  Alex Pentland,et al.  Fair, Transparent, and Accountable Algorithmic Decision-making Processes , 2017, Philosophy & Technology.

[38]  Xiaofeng Meng,et al.  Query Understanding through Knowledge-Based Conceptualization , 2015, IJCAI.

[39]  S. Robertson The probability ranking principle in IR , 1997 .

[40]  Gilles Brassard,et al.  Alambic: a privacy-preserving recommender system for electronic commerce , 2008, International Journal of Information Security.

[41]  Maarten de Rijke,et al.  News Comments: Exploring, Modeling, and Online Prediction , 2010, ECIR.

[42]  Enhong Chen,et al.  Context-aware query suggestion by mining click-through and session data , 2008, KDD.

[43]  Krisztian Balog,et al.  Extended Overview of the Living Labs for Information Retrieval Evaluation (LL4IR) CLEF Lab 2015 , 2015, CLEF.

[44]  M. de Rijke,et al.  To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions , 2019, SIGIR.

[45]  M. de Rijke,et al.  A Survey of Query Auto Completion in Information Retrieval , 2016, Found. Trends Inf. Retr..

[46]  Daria Sorokina,et al.  Amazon Search: The Joy of Ranking Products , 2016, SIGIR.

[47]  J. Rowley Product search in e‐shopping: a review and research propositions , 2000 .

[48]  Michael Chau,et al.  The Impact of Query Suggestion in E-Commerce Websites , 2011, WEB.

[49]  Elad Haramaty,et al.  Multi-Objective Ranking Optimization for Product Search Using Stochastic Label Aggregation , 2020, WWW.

[50]  Andrew Trotman,et al.  Report on the SIGIR 2019 Workshop on eCommerce (ECOM19) , 2019, ArXiv.

[51]  Mohit Sharma,et al.  Mining E-Commerce Query Relations using Customer Interaction Networks , 2018, WWW.

[52]  Paul Resnick,et al.  Trust among strangers in internet transactions: Empirical analysis of eBay' s reputation system , 2002, The Economics of the Internet and E-commerce.

[53]  M. de Rijke,et al.  Learning Latent Vector Spaces for Product Search , 2016, CIKM.

[54]  Paul N. Bennett,et al.  Generating Clarifying Questions for Information Retrieval , 2020, WWW.

[55]  Thorsten Joachims,et al.  Fairness of Exposure in Rankings , 2018, KDD.

[56]  Giuseppe Riccardi,et al.  Automated Natural Spoken Dialog , 2002, Computer.

[57]  Maarten de Rijke,et al.  ViTOR: Learning to Rank Webpages Based on Visual Features , 2019, WWW.

[58]  M. de Rijke,et al.  Unbiased Learning to Rank: Counterfactual and Online Approaches , 2019, WWW.

[59]  David Lazer,et al.  Auditing Autocomplete: Suggestion Networks and Recursive Algorithm Interrogation , 2019, WebSci.

[60]  Brian D. Davison,et al.  Learning to rank for freshness and relevance , 2011, SIGIR.

[61]  Dominik Kowald,et al.  The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study , 2019, ECIR.

[62]  Bhaskar Mitra,et al.  An Introduction to Neural Information Retrieval , 2018, Found. Trends Inf. Retr..

[63]  Mohit Sharma,et al.  A Taxonomy of Queries for E-commerce Search , 2018, SIGIR.