The Journal of Machine Learning Research
Convex Optimization
Retrieval evaluation with incomplete information
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
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
History repeats itself: repeat queries in Yahoo's logs
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Mining long-term search history to improve search accuracy
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A large-scale evaluation and analysis of personalized search strategies
Proceedings of the 16th international conference on World Wide Web
Privacy-enhancing personalized web search
Proceedings of the 16th international conference on World Wide Web
To personalize or not to personalize: modeling queries with variation in user intent
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Matching task profiles and user needs in personalized web search
Proceedings of the 17th ACM conference on Information and knowledge management
Inferring private information using social network data
Proceedings of the 18th international conference on World wide web
To divide and conquer search ranking by learning query difficulty
Proceedings of the 18th ACM conference on Information and knowledge management
ACM Transactions on Computer-Human Interaction (TOCHI)
Anonymizing user profiles for personalized web search
Proceedings of the 19th international conference on World wide web
Privacy Violations Using Microtargeted Ads: A Case Study
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
Personalizing web search using long term browsing history
Proceedings of the fourth ACM international conference on Web search and data mining
Personalized social recommendations: accurate or private
Proceedings of the VLDB Endowment
UPS: efficient privacy protection in personalized web search
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
The Filter Bubble: What the Internet Is Hiding from You
The Filter Bubble: What the Internet Is Hiding from You
Probabilistic models for personalizing web search
Proceedings of the fifth ACM international conference on Web search and data mining
Modeling the impact of short- and long-term behavior on search personalization
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Detecting Trends in Social Bookmarking Systems: A del.icio.us Endeavor
International Journal of Data Warehousing and Mining
Measuring personalization of web search
Proceedings of the 22nd international conference on World Wide Web
When web personalization misleads bucket testing
Proceedings of the 1st workshop on User engagement optimization
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Online service platforms (OSPs), such as search engines, news-websites, ad-providers, etc., serve highly personalized content to the user, based on the profile extracted from her history with the OSP. In this paper, we capture OSP's personalization for an user in a new data structure called the personalization vector (?), which is a weighted vector over a set of topics, and present efficient algorithms to learn it. Our approach treats OSPs as black-boxes, and extracts η by mining only their output, specifically, the personalized (for an user) and vanilla (without any user information) contents served, and the differences in these content. We believe that such treatment of OSPs is a unique aspect of our work, not just enabling access to (so far hidden) profiles in OSPs, but also providing a novel and practical approach for retrieving information from OSPs by mining differences in their outputs. We formulate a new model called Latent Topic Personalization (LTP) that captures the personalization vector in a learning framework and present efficient inference algorithms for determining it. We perform extensive experiments targeting search engine personalization, using data from both real Google users and synthetic setup. Our results indicate that LTP achieves high accuracy (R-pre = 84%) in discovering personalized topics.For Google data, our qualitative results demonstrate that the topics determined by LTP for a user correspond well to his ad-categories determined by Google.