Modern Information Retrieval
Context-sensitive information retrieval using implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th 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
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
A large-scale evaluation and analysis of personalized search strategies
Proceedings of the 16th international conference on World Wide Web
Confidence-weighted linear classification
Proceedings of the 25th international conference on Machine learning
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
Predicting user interests from contextual information
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Sparse Online Learning via Truncated Gradient
The Journal of Machine Learning Research
Ranking model adaptation for domain-specific search
Proceedings of the 18th ACM conference on Information and knowledge management
Model adaptation via model interpolation and boosting for web search ranking
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Knowledge transfer for cross domain learning to rank
Information Retrieval
Predicting short-term interests using activity-based search context
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Inferring and using location metadata to personalize web search
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Deep Learning Approaches to Semantic Relevance Modeling for Chinese Question-Answer Pairs
ACM Transactions on Asian Language Information Processing (TALIP)
Probabilistic models for personalizing web search
Proceedings of the fifth ACM international conference on Web search and data mining
Personalized ranking model adaptation for web search
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Enhancing personalized search by mining and modeling task behavior
Proceedings of the 22nd international conference on World Wide Web
Learning deep structured semantic models for web search using clickthrough data
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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RankNet is one of the widely adopted ranking models for web search tasks. However, adapting a generic RankNet for personalized search is little studied. In this paper, we first continue-trained a variety of RankNets with different number of hidden layers and network structures over a previously trained global RankNet model, and observed that a deep neural network with five hidden layers gives the best performance. To further improve the performance of adaptation, we propose a set of novel methods categorized into two groups. In the first group, three methods are proposed to properly assess the usefulness of each adaptation instance and only leverage the most informative instances to adapt a user-specific RankNet model. These assessments are based on KL-divergence, click entropy or a heuristic to ignore top clicks in adaptation queries. In the second group, two methods are proposed to regularize the training of the neural network in RankNet: one of these methods regularize the error back-propagation via a truncated gradient approach, while the other method limits the depth of the back propagation when adapting the neural network. We empirically evaluate our approaches using a large-scale real-world data set. Experimental results exhibit that our methods all give significant improvements over a strong baseline ranking system, and the truncated gradient approach gives the best performance, significantly better than all others.