Context Attentive Document Ranking and Query Suggestion
We present a context-aware neural ranking model to exploit users' on-task search activities and enhance retrieval performance. In particular, a two-level hierarchical recurrent neural network is introduced to learn search context representation of individual queries, search tasks, and corresponding dependency structure by jointly optimizing two companion retrieval tasks: document ranking and query suggestion. To identify the variable dependency structure between search context and users' ongoing search activities, attention at both levels of recurrent states are introduced. Extensive experiment comparisons against a rich set of baseline methods and an in-depth ablation analysis confirm the value of our proposed approach for modeling search context buried in search tasks.
Code (5)
Tasks
Document RankingRetrievalSimilar Papers 제목 키워드 기반
Multi-Task Learning for Document Ranking and Query Suggestion
We propose a multi-task learning framework to jointly learn document ranking and query suggestion for web search. It consists of two major components, a document ranker, and a query recommender. Document ranker combines …
Document RankingMulti-Task LearningContrastive Learning of User Behavior Sequence for Context-Aware Document Ranking
Context information in search sessions has proven to be useful for capturing user search intent. Existing studies explored user behavior sequences in sessions in different ways to enhance query suggestion or document ran…
Contrastive LearningData AugmentationDocument RankingKeyword Embeddings for Query Suggestion
Nowadays, search engine users commonly rely on query suggestions to improve their initial inputs. Current systems are very good at recommending lexical adaptations or spelling corrections to users' queries. However, they…
RetrievalSentenceSentence EmbeddingSentence-EmbeddingCounterfactual Learning To Rank for Utility-Maximizing Query Autocompletion
Conventional methods for query autocompletion aim to predict which completed query a user will select from a list. A shortcoming of this approach is that users often do not know which query will provide the best retrieva…
counterfactualInformation RetrievalLearning-To-RankRetrievalCo-BERT: A Context-Aware BERT Retrieval Model Incorporating Local and Query-specific Context
BERT-based text ranking models have dramatically advanced the state-of-the-art in ad-hoc retrieval, wherein most models tend to consider individual query-document pairs independently. In the mean time, the importance and…
Learning-To-RankRe-RankingRetrieval