Improving search results with prior similar queries

Yashar Moshfeghi, Kristiyan Velinov, Peter Triantafillou

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

3 Citations (Scopus)


This paper describes a novel approach to re-ranking search engine result pages (SERP): Its fundamental principle is to re-rank results to a given query, based on exploiting evidence gathered from past similar search queries. Our approach is inspired by collaborative filtering, with the main challenge being to find the set of similar queries, while also taking efficiency into account. In particular, our approach aims to address this challenge by proposing a combination of a similarity graph and a locality sensitive hashing scheme. We construct a set of features from our similarity graph and build a prediction model using the Hoeffding decision tree algorithm. We have evaluated the effectiveness of our model in terms of P@1, MAP@10, and nDCG@10, using the Yandex Data Challenge data set. We have compared the performance of our model against two baselines, namely, the Yandex initial ranking and the decision tree model learnt on the same set of features when extracted based on query repetition (i.e. excluding the evidence of similar queries in our approach). Our results reveal that the proposed approach consistently and (statistically) significantly outperforms both baselines.
Original languageEnglish
Title of host publicationProceedings of the 25th ACM International on Conference on Information and Knowledge Management
Place of PublicationNew York, NY, USA
Number of pages4
Publication statusPublished - 2016

Publication series

NameCIKM '16


  • collaborative filtering
  • information retrieval
  • results re-ranking


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