Personalizing search via automated analysis of interests and activities
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic identification of user interest for personalized search
Proceedings of the 15th international conference on World Wide Web
Efficient bayesian hierarchical user modeling for recommendation system
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Random walks on the click graph
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
User profiles for personalized information access
The adaptive web
Quicklink selection for navigational query results
Proceedings of the 18th international conference on World wide web
Predicting user interests from contextual information
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A characterization of online browsing behavior
Proceedings of the 19th international conference on World wide web
Result enrichment in commerce search using browse trails
Proceedings of the fourth ACM international conference on Web search and data mining
Predictive client-side profiles for personalized advertising
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A timeline-based algorithm for personalized tag recommendation
WISS'10 Proceedings of the 2010 international conference on Web information systems engineering
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Traditional information retrieval models assume that users express their information needs via text queries (i.e., their "talk"). In this poster, we consider Web browsing behavior outside of interactions with retrieval systems (i.e., users' "walk") as an alternative source of signal describing users' information needs, and compare it to the query-expressed information needs on a large dataset. Our findings demonstrate that information needs expressed in different behavior modalities are largely non-overlapping, and that past behavior in each modality is the most accurate predictor of future behavior in that modality. Results also show that browsing data provides a stronger source of signal than search queries due to its greater volume, which explains previous work that has found implicit behavioral data to be a valuable source of information for user modeling and personalization.