Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
NiagaraCQ: a scalable continuous query system for Internet databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Continuously adaptive continuous queries over streams
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Communications of the ACM
Personalized web search by mapping user queries to categories
Proceedings of the eleventh international conference on Information and knowledge management
Continuous queries over data streams
ACM SIGMOD Record
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Scaling personalized web search
WWW '03 Proceedings of the 12th international conference on World Wide Web
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
Adaptive web search based on user profile constructed without any effort from users
Proceedings of the 13th international conference on World Wide Web
High-Availability Algorithms for Distributed Stream Processing
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
CubeSVD: a novel approach to personalized Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
Automatic identification of user goals in Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
MAPS: approximate publish/subscribe functionality in peer-to-peer networks
Proceedings of the 1st international workshop on Advanced data processing in ubiquitous computing (ADPUC 2006)
Fostering knowledge sharing by inverse search
Proceedings of the 4th international conference on Knowledge capture
Approximate Information Filtering in Peer-to-Peer Networks
WISE '08 Proceedings of the 9th international conference on Web Information Systems Engineering
Closing Information Gaps with Inverse Search
PAKM '08 Proceedings of the 7th International Conference on Practical Aspects of Knowledge Management
Discovery is never by chance: designing for (un)serendipity
Proceedings of the seventh ACM conference on Creativity and cognition
Social search and need-driven knowledge sharing in Wikis with Woogle
Proceedings of the 5th International Symposium on Wikis and Open Collaboration
Personalized social search based on the user's social network
Proceedings of the 18th ACM conference on Information and knowledge management
Building a desktop search test-bed
ECIR'07 Proceedings of the 29th European conference on IR research
MinervaDL: an architecture for information retrieval and filtering in distributed digital libraries
ECDL'07 Proceedings of the 11th European conference on Research and Advanced Technology for Digital Libraries
Top-k publish-subscribe for social annotation of news
Proceedings of the VLDB Endowment
| Hi-index | 0.00 |
Major search engines currently use the history of a user's actions (e.g., queries, clicks) to personalize search results. In this paper, we present a new personalized service, query-specific web recommendations (QSRs), that retroactively answers queries from a user's history as new results arise. The QSR system addresses two important subproblems with applications beyond the system itself: (1) Automatic identification of queries in a user's history that represent standing interests and unfulfilled needs. (2) Effective detection of interesting new results to these queries. We develop a variety of heuristics and algorithms to address these problems, and evaluate them through a study of Google history users. Our results strongly motivate the need for automatic detection of standing interests from a user's history, and identifies the algorithms that are most useful in doing so. Our results also identify the algorithms, some which are counter-intuitive, that are most useful in identifying interesting new results for past queries, allowing us to achieve very high precision over our data set.