Learning to rank and discover for E-commerce search

Anjan Goswami, Chengxiang Zhai, Prasant Mohapatra

Research output: Chapter in Book/Report/Conference proceedingConference contribution


E-Commerce (E-Com) search is an emerging problem with multiple new challenges. One of the primary challenges constitutes optimizing it for relevance and revenue and simultaneously maintaining a discovery strategy. The problem requires designing novel strategies to systematically “discover” promising items from the inventory, that have not received sufficient exposure in search results while minimizing the loss of relevance and revenue because of that. To this end, we develop a formal framework for optimizing E-Com search and propose a novel epsilon-explore Learning to Rank (eLTR) paradigm that can be integrated with the traditional learning to rank (LTR) framework to explore new or less exposed items. The key idea is to decompose the ranking function into (1) a function of content-based features, (2) a function of behavioral features, and introduce a parameter epsilon to regulate their relative contributions. We further propose novel algorithms based on eLTR to improve the traditional LTR used in the current E-Com search engines by “forcing” exploration of a fixed number of items while limiting the relevance drop. We also show that eLTR can be considered to be monotonic sub-modular and thus we can design a greedy approximation algorithm with a theoretical guarantee. We conduct experiments with synthetic data and compare eLTR with a baseline random selection and an upper confidence bound (UCB) based exploration strategies. We show that eLTR is an efficient algorithm for such exploration. We expect that the formalization presented in this paper will lead to new research in the area of ranking problems for E-com marketplaces.

Original languageEnglish (US)
Title of host publicationMachine Learning and Data Mining in Pattern Recognition - 14th International Conference, MLDM 2018, Proceedings
EditorsPetra Perner
Number of pages16
ISBN (Print)9783319961323
StatePublished - 2018
Event14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018 - New York, United States
Duration: Jul 15 2018Jul 19 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10935 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018
Country/TerritoryUnited States
CityNew York

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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