Source-linked AI summary
Context Attentive Document Ranking and Query Suggestion
Wasi Uddin Ahmad, Kai-Wei Chang, Hongning Wang
TL;DR
Search tasks contain interdependent queries and clicks that can inform retrieval, but existing approaches have limits in representing context and dependencies across retrieval tasks. CARS encodes task context with a two-level hierarchical recurrent network and attention, jointly optimizing document ranking and query suggestion. Experiments on AOL logs report significant improvements over classical and neural baselines.
Problem
Existing methods represent on-task context with limited features or fail to model sequential dependencies across retrieval tasks, despite their shared underlying intent.
Source: §1 · p. 1Method
CARS uses a two-level hierarchical recurrent neural network with attention to model query and click context, jointly optimizing document ranking and query suggestion.
Source: §1 · pp. 1–2Results
On AOL search logs, CARS significantly improved over a rich set of classical and neural baselines for document ranking and query suggestion.
Source: §1 · p. 2Takeaways & Limitations
The experiments support the value of modeling search context buried in search tasks for retrieval.
Source: §1 · p. 2, §5 · p. 9CARS models search tasks independently and passively waits for users’ next query and click, leaving across-task relatedness and online operation for future work.
Source: §5 · p. 9
Abstract
from arXiv · showhide
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.
1 INTRODUCTION
Search tasks contain temporally dependent queries, clicks, and other interactions that can provide useful context for retrieval. CARS addresses limits in existing context representations and cross-task modeling with a hierarchical recurrent model that jointly learns document ranking and query suggestion.
1 INTRODUCTION
- Search tasks include temporally dependent queries, clicks, and other interactions that can enrich retrieval, especially when user intent is ambiguous.Prior work reported that short-term search history becomes more important as a session progresses and that on-task behavior yielded promising retrieval gains.Source: §1 · p. 1
- Existing context methods rely on keywords, statistical features, or hand-crafted rules, limiting coverage and making variable-range dependencies difficult to capture.Keywords can suffer from vocabulary gaps, while statistical features can be unreliable with sparse observations; dependency structure is often imposed in advance.Source: §1 · p. 1
- Earlier multi-task retrieval methods use auxiliary tasks but do not model sequential dependencies across retrieval tasks, limiting their use of search-sequence information.Ranking and query suggestion can share underlying intent, and their interactions can reveal context beyond submitted queries and clicked documents.Source: §1 · p. 1
- CARS jointly learns document ranking and query suggestion while modeling dependencies in users’ in-task query and click sequences.It uses a two-level hierarchical recurrent neural network and attention at both recurrent-state levels to represent search context and its variable dependencies.Source: §1 · pp. 1–2
- On AOL search logs, CARS significantly improved over a rich set of classical and neural baselines, supporting the value of modeling search-task context.The evaluation covers document ranking and query suggestion.Source: §1 · p. 2
2 RELATED WORKS
Prior task-based retrieval work derives context from activity features or learns models of intent and future behavior, while neural approaches increasingly model session sequences. The paper identifies a remaining gap: existing approaches do not jointly represent variable dependencies across search actions and retrieval tasks.
2 RELATED WORKS
- Data-driven approaches extract context from queries and clicks, statistical features, or hand-crafted rules, but their representations can limit generalizability and task fit.Examples include keyword-based reranking, statistical context features, and rules for characterizing search-context changes.Source: §2 · p. 2
- Model-driven approaches infer intent or future behavior, but predefined model spaces and transition structures can restrict the interactions they learn.Examples include variable-length hidden Markov models and reinforcement-learning approaches to user-system interactions.Source: §2 · p. 2
- Neural session models learn query or reformulation representations and hierarchical query sequences, but typically use context from a single retrieval task.The paper notes that such models therefore cannot use reinforcement between different retrieval tasks, and many do not distinguish the influence of different actions in a sequence.Source: §2 · p. 2
- Existing multi-task retrieval work uses one task to regularize another, but does not model the dependency structure of in-task query and click sequences.CARS instead learns context-attentive representations to model these dependencies and mutually enhance document ranking and query suggestion.Source: §2 · p. 2
3 A CONTEXT ATTENTIVE RANKING AND SUGGESTION MODEL
CARS models search-task context from users’ queries and clicks to support document ranking and query suggestion. Its hierarchical recurrent encoders and attention summarize past activity, while joint learning couples the two retrieval tasks.
3.1 Problem Statement
- A search task comprises queries and result interactions tied to an information need, whose changing context can clarify ambiguous queries and guide ranking and suggestions.The paper defines a task as a maximum subset of queries corresponding to a particular information need.Source: §3.1 · pp. 2–3
- CARS jointly uses query and click sequences to learn search context for document ranking and query suggestion.The paper presents this as the first framework of its kind to use both sequences and jointly optimize the two tasks.Source: §3.1 · p. 3
3.2 Learning Search Context Representations
- Separate lower-level encoders represent query and document text, while upper-level query and click encoders summarize past on-task activity.The lower-level encoders use BiLSTMs and inner attention to form fixed-length representations.Source: §3.1 · p. 3, §3.2 · pp. 3–4
- Attention weights past query and click states, whose summaries are concatenated into a shared context representation used for both retrieval tasks.The attention reflects relevance to the current query, not to a particular retrieval purpose.Source: §3.2 · p. 4
3.3 Joint Learning of Ranking and Suggestion
- The ranker treats clicks as relevance labels, and the authors leave more advanced click modeling and learning-to-rank approaches for future work.They note that user clicks are known to be biased.Source: §3.3 · pp. 4–5
- CARS couples ranking and suggestion through regularized multi-task learning, sharing parameters while retaining task-specific components.Its ranking objective uses click feedback, while its suggestion objective models the next query from prior queries and clicks.Source: §3.3 · p. 5
4 EXPERIMENTS AND RESULTS
On AOL search-log experiments, CARS significantly outperformed traditional and neural baselines in document ranking and query suggestion. Ablations show distinct roles for in-task context and cross-task training, with performance patterns varying by task length and training-data size.
4.1 Dataset and Experimental Setups
- Experiments used AOL logs split into background, training, validation, and test periods; tasks were segmented by consecutive-query similarity and required at least two queries.Candidate documents were sampled around BM25-ranked recorded clicks, with document titles used as content.Source: §4.1 · p. 6
4.2 Experiment Results
- CARS significantly outperformed traditional and neural IR baselines in document ranking and all query-suggestion baselines in discrimination and generation.Tables 2 and 3 report the document-ranking and query-suggestion comparisons, respectively.Source: Table 2 · p. 6, §4.2 · p. 7, Table 3 · p. 7
4.3 Abalation Analysis and Discussions
- Removing in-task queries and clicks reduced ranking and suggestion quality; past clicks mattered more for ranking, while past queries were crucial for suggestion.With query-recommender attention off, removing in-task context incurred reported losses of 3% and 8.9% in NDCG@1 and 30.7% and 0.8% in BLEU-1, respectively.Source: §4.3 · p. 7
- Disabling the query recommender lowered ranking NDCG@1 by 3.1%, while disabling the ranker did not reduce suggestion performance.The authors conjecture that clicked documents already provide information to the recommender.Source: §4.3 · p. 8
- 6.9% and 5.6% MAP drops followed removal of the session-click encoder in long and short tasks, respectively; longer tasks were harder across ranking models.The test set comprised 66.5% short, 27.24% medium, and 6.26% long tasks; filtering queries without clicks removed some longer tasks.Source: §4.3 · p. 8
- At Q3, ranking and suggestion use attention over session-encoder states; the example task also highlights high-attention words in queries and documents.Figure 5 compares ranking and suggestion across query positions; the reported gap between CARS and MNSRF narrows from L4 to L7 as users often repeat queries in longer tasks.Source: Figure 4 · p. 8, Table 5 · p. 9, Figure 5 · p. 9
- CARS’s average BLUE score rose 41.4% from short to long tasks, while its advantage over M-NSRF was larger on short than long tasks, 40.3% versus 22.9%.The authors report that longer tasks amplify CARS’s query-suggestion advantage; past clicks also help suggestion as tasks lengthen.Source: §4.3 · p. 8, Figure 2 · p. 8
- CARS improved quickly on both tasks with less training data, which the authors attribute to exploiting signals from its two companion retrieval tasks.Figure 3 compares multi-task models’ sample complexity for document ranking and query suggestion.Source: §4.3 · p. 8, Figure 3 · p. 8
5 CONCLUSION AND FUTURE WORKS
CARS models search context by jointly learning document ranking and query suggestion, using a two-level hierarchical recurrent network. The authors identify cross-task modeling, online operation, and broader applications as future directions.
5 CONCLUSION AND FUTURE WORKS
- CARS jointly models document ranking and query suggestion with a two-level hierarchical recurrent network to learn search context and dependency structure.Source: §5 · p. 9
- The current solution models search tasks independently, leaving cross-task relationships such as users’ long-term search interests for future work.Source: §5 · p. 9
- The authors also propose studying online operation and exploring CARS in application areas beyond web search.Source: §5 · pp. 9–10