NDLI: Modularity-Based Query Clustering for Identifying Users Sharing a Common Condition

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Online News Tracking for Ad-Hoc Queries

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Probabilistic Multileave for Online Retrieval Evaluation

Twitter Sentiment Analysis with Deep Convolutional Neural Networks

Anchoring and Adjustment in Relevance Estimation

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Personalized Semantic Ranking for Collaborative Recommendation

Active Learning for Entity Filtering in Microblog Streams

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Context-aware Point-of-Interest Recommendation Using Tensor Factorization with Social Regularization

Adaptive User Engagement Evaluation via Multi-task Learning

Compact Snippet Caching for Flash-based Search Engines

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Sub-document Timestamping of Web Documents

Modularity-Based Query Clustering for Identifying Users Sharing a Common Condition

Content Provider	ACM Digital Library
Author	Yom-Tov, Elad Harel, Maayan Gal-On
Abstract	We present an algorithm for identifying users who share a common condition from anonymized search engine logs. Input to the algorithm is a set of seed phrases that identify users with the condition of interest with high precision albeit at a very low recall. We expand the set of seed phrases by clustering queries according to the pages users clicked following these queries and the temporal ordering of queries within sessions, emphasizing the subgraph containing seed phrases. To this end, we extend modularity-based clustering such that it uses the information in the initial seed phrases as well as other queries of users in the population of interest. We evaluate the performance of the proposed method on two datasets, one of mood disorders and the other of anorexia, by classifying users according to the clusters in which they appeared and the phrases contained thereof, and show that the area under the receiver operating characteristic curve (AUC) obtained by these methods exceeds 0.87. These results demonstrate the value of our algorithm for both identifying users for future research and to gain better understanding of the language associated with the condition.
Starting Page	819
Ending Page	822
Page Count	4
File Format	PDF
ISBN	9781450336215
DOI	10.1145/2766462.2767798
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-08-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	User condition Modularity-based clustering Clustering queries Temporal ordering Users clicks
Content Type	Text
Resource Type	Article

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