Elements of information theory
Elements of information theory
Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Query clustering using user logs
ACM Transactions on Information Systems (TOIS)
Detecting dominant locations from search queries
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Entropy of search logs: how hard is search? with personalization? with backoff?
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Automatically identifying localizable queries
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Integration of news content into web results
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Statistical Language Models for Information Retrieval A Critical Review
Foundations and Trends in Information Retrieval
Spatio-temporal models for estimating click-through rate
Proceedings of the 18th international conference on World wide web
Towards recency ranking in web search
Proceedings of the third ACM international conference on Web search and data mining
Fast online learning through offline initialization for time-sensitive recommendation
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Inferring and using location metadata to personalize web search
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
User action interpretation for personalized content optimization in recommender systems
Proceedings of the 20th ACM international conference on Information and knowledge management
Query recommendation using query logs in search engines
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
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In this paper, we present our approach for geographic personalization of a content recommendation system. More specifically, our work focuses on recommending query topics to users. We do this by mining the search query logs to detect trending local topics. For a set of queries we compute their counts and what we call buzz scores, which is a metric for detecting trending behavior. We also compute the entropy of the geographic distribution of the queries as means of detecting their location affinity. We cluster the queries into trending topics and assign the topics to their corresponding location. Human editors then select a subset of these local topics and enter them into a recommendation system. In turn the recommendation system optimizes a pool of trending local and global topics by exploiting user feedback. We present some editorial evaluation of the technique and results of a live experiment. Inclusion of local topics in selected locations into the global pool of topics resulted in more than 6% relative increase in user engagement with the recommendation system compared to using the global topics exclusively.