Abstract
Query recommendation systems are ubiquitous in modern search engines, assisting users in producing effective queries to meet their information needs. However, these systems require a large amount of data to produce good recommendations, such as a large collection of documents to index and query logs. In particular, query logs and user data are not available in cold start scenarios. Query logs are expensive to collect and maintain and require complex and time-consuming cascading pipelines for creating, combining, and ranking recommendations. To address these issues, we frame the query recommendation problem as a generative task, proposing a novel approach called Generative Query Recommendation (GQR). GQR uses an LLM as its foundation and does not require to be trained or fine-tuned to tackle the query recommendation problem. We design a prompt that enables the LLM to understand the specific recommendation task, even using a single example. We then improved our system by proposing a version that exploits query logs called Retriever-Augmented GQR (RA-GQR). RA-GQr dynamically composes its prompt by retrieving similar queries from query logs. GQR approaches reuses a pre-existing neural architecture resulting in a simpler and more ready-to-market approach, even in a cold start scenario. Our proposed GQR obtains state-of-the-art performance in terms of NDCG@10 and clarity score against two commercial search engines and the previous state-of-the-art approach on the Robust04 and ClueWeb09B collections, improving on average the NDCG@10 performance up to ~4% on Robust04 and ClueWeb09B w.r.t the previous best competitor. RA-GQR further improve the NDCG@10 obtaining an increase of ~11%, ~6\% on Robust04 and ClueWeb09B w.r.t the best competitor. Furthermore, our system obtained ~59% of user preferences in a blind user study, proving that our method produces the most engaging queries.
Overview
-
The GQR system introduces a novel method for generating query recommendations using GPT-3, without relying on traditional query logs.
-
Evaluations on datasets like Robust04 and ClueWeb09B, as well as a set of AOL query logs, demonstrate the system's superior performance using metrics such as Clarity Score (SCS) and Normalized Discounted Cumulative Gain (NDCG@10).
-
User studies show higher engagement and a 100% success rate for rare queries, indicating the system’s robustness and potential for practical application.
Generating Query Recommendations without Query Logs
The presented paper introduces a novel system for generating query recommendations without relying on traditional query logs, termed the GQR (Query Generation and Recommendation) system. Leveraging sophisticated natural language models, specifically GPT-3, the proposed system is evaluated across several metrics and datasets to validate its efficacy compared to existing baseline methods and commercial query recommendation systems.
Evaluation Metrics and Datasets
To assess the performance of the GQR system, the authors employed two established corpora, Robust04 and ClueWeb09B, alongside a set of AOL query logs. They utilized two evaluation protocols:
- Substitution Protocol: A direct comparison based on the individual query recommendation metrics.
- Concat Protocol: An indirect evaluation that measures the improvement when user queries are supplemented with multiple recommendations.
Key performance metrics include the Clarity Score (SCS) and Normalized Discounted Cumulative Gain (NDCG@10), which were meticulously analyzed to gauge the system’s consistency and relevance.
Key Results
Substitution Protocol
- Clarity Score: The GQR (GPT-3) system yielded the highest average scores on both datasets. For Robust04, it achieved an average SCS of 10.65 with a very low standard deviation (±0.08), indicating stable performance. On ClueWeb09B, the SCS averaged 11.12 with a similarly low standard deviation (±0.10).
- NDCG@10: The GQR (GPT-3) system significantly outperformed other systems. It showed an improvement of +23% and +27% in the NDCG@10 scores for the Robust04 and ClueWeb09B datasets, respectively, compared to the best competing system.
Concat Protocol
- Clarity Score: Across varying rank levels (number of concatenated query recommendations), GQR (GPT-3) consistently outperformed other systems. For instance, at rank 6, it exhibited an increase in the SCS to 56.67 on Robust04.
- NDCG@10: The incremental enhancement of query performance was observed, with a notable 6% and 5% enhancement on Robust04 and ClueWeb09B datasets, respectively.
User Engagement and Long Tail Query Analysis
The user study conducted across multiple annotator groups demonstrated the GQR system’s superiority in user engagement. The system received approximately 59% of the overall user preferences, compared to 26% and 15% for the two commercial systems.
Additionally, the GQR (GPT-3) system exhibited robust performance in generating recommendations for rare (long-tail) queries, achieving a 100% success rate in suggesting relevant queries. This contrasts with other systems that struggled to provide recommendations consistently for such queries.
Prompt Study Analysis
Two separate prompt studies addressed the impact of:
- The number of examples in the prompt context.
- The specific content of the examples in the prompt.
Results indicated that:
- Number of Examples: The performance, in terms of SCS and NDCG@10, remained stable regardless of the number of examples (ranging from 1 to 10) in the prompt.
- Specific Prompt Context: Different prompts with varying examples did not significantly alter the system’s effectiveness, suggesting that GQR (GPT-3) performs robustly irrespective of specific example content.
Discussion
The findings show that the GQR (GPT-3) system not only surpasses its commercial counterparts in both metric and user engagement evaluations but also demonstrates superior capability in handling queries of varying frequencies. The stability across different prompt configurations underlines the adaptability of the model.
Implications and Future Developments
Practically, the implementation of a GQR-like system can significantly enhance user search experiences by providing precise and valuable query suggestions without dependency on historical query logs, which may be sparse or unavailable. Theoretically, this approach eases the reliance on query histories, promoting privacy and potentially leveling the playing field for new search engines or domains with limited historical data.
Future advancements could investigate integrating domain-specific training to further enhance the relevance of the generated queries or refining prompt engineering techniques to optimize GQR system performance further. Additionally, exploring more diverse datasets would offer greater insight into the system’s versatility across various use cases.
In conclusion, the proposed GQR (GPT-3) system substantially advances the state-of-the-art in query recommendation, demonstrating superior performance across multiple evaluation protocols and user studies.
Create an account to read this summary for free: