Ontology Based Personalized Search
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
The Journal of Machine Learning Research
LDA-based document models for ad-hoc retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A large-scale analysis of query logs for assessing personalization opportunities
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
Web search personalization with ontological user profiles
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
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
A statistical comparison of tag and query logs
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Predicting user interests from contextual information
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
ACM Transactions on Computer-Human Interaction (TOCHI)
Towards query log based personalization using topic models
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Personalizing web search using long term browsing history
Proceedings of the fourth ACM international conference on Web search and data mining
Improving social bookmark search using personalised latent variable language models
Proceedings of the fourth 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
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Personalisation is an important area in the field of IR that attempts to adapt ranking algorithms so that the results returned are tuned towards the searcher's interests. In this work we use query logs to build personalised ranking models in which user profiles are constructed based on the representation of clicked documents over a topic space. Instead of employing a human-generated ontology, we use novel latent topic models to determine these topics. Our experiments show that by subtly introducing user profiles as part of the ranking algorithm, rather than by re-ranking an existing list, we can provide personalised ranked lists of documents which improve significantly over a non-personalised baseline. Further examination shows that the performance of the personalised system is particularly good in cases where prior knowledge of the search query is limited.