Vaclav Kosar's face photo
Vaclav Kosar
Software, Machine Learning, & Business

Result Diversification in Web Search and Recommenders

Increase coverage in web search and recommendation via re-ranking diversification factor
  • Since user already seen results above, and they haven’t clicked and continue reading
  • What next the results should be?
  • Increase diversity to satisfy all users for reduction of global relevance

Diverse Results Example

  • word apple can mean a company or a fruit
  • web search should cover both of these meanings (aspects)

diverse web results for word apple - a company and a fruit

Query Reformulations for Web Search Diversification

How to Query Reformulations Work?

  • How relevant the document to the user, given already seen higher results?
  • Each document added to results should cover different aspect
  • Previous methods: similarity between docs using maximal marginal relevance
  • Paper contribution: similarity between sub-queries

Sub-Query Generation

  • query reformulations provided by three major Web search engines
  • Created probably via query log mining
  • related sub-queries + suggested sub-queries
  • relative importance generated sub-queries from centralized ranking of documents covering them

Personalized Re-Ranking

  • Managing Popularity Bias in Recommender Systems with Personalized Re-ranking (2019, Uni of Colorado Boulder)
  • Document → Item, Query → User, Aspect → long-tail vs short-head
  • Goal: Relevant but cover both long-tail (rare) and short-head (popular)
  • Use “Smooth” xQuAD - maintain some ratio of long tail items
  • personalize based on how much user interacted with long-tail vs short-head items (ratio)
  • Vaclav’s opinion: why not make item popularity more continuous instead of using 2 categories?
  • Adding small diversification can improve NDCG

Coverage and Submodularity

Coverage is a submodularity and diminishing returns problem - read more here

Created on 05 Mar 2020. Updated on: 11 Jun 2022.

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