Federated Search Using Query Log Evidence

• Type: Article in International Conference Proceedings Book
• Date: 2022

Authors

Damas, J (Author) Other; Devezas, J (Author) Other; Sérgio Nunes (Author) FEUP

Conference proceedings International

Title: Progress in Artificial Intelligence - 21st EPIA Conference on Artificial Intelligence, EPIA 2022, Lisbon, Portugal, August 31 - September 2, 2022, Proceedings

Pages: 794-805

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Other information

Authenticus ID: P-00X-93Y

DOI: 10.1007/978-3-031-16474-3_64

Abstract (EN): In this work, we targeted the search engine of a sports-related website that presented an opportunity for search result quality improvement. We reframed the engine as a Federated Search instance, where each collection represented a searchable entity type within the system, using Apache Solr for querying each resource and a Python Flask server to merge results. We extend previous work on individual search term weighing, making use of past search terms as a relevance indicator for user selected documents. To incorporate term weights we define four strategies combining two binary variables: integration with default relevance (linear scaling or linear combination) and search term frequency (raw value or log-smoothed). To evaluate our solution, we extracted two query sets from search logs: one with frequently submitted queries, and another with ambiguous result access patterns. We used click-through information as a relevance proxy and tried to mitigate its limitations by evaluating under distinct IR metrics, including MRR, MAP and NDCG. Moreover, we also measured Spearman rank correlation coefficients to test similarities between produced rankings and reference orderings according to user access patterns. Results show consistency across all metrics in both sets. Previous search terms were key to obtaining a higher effectiveness, with runs that used pure search term frequency performing best. Compared to the baseline, our best strategies were able to maintain quality on frequent queries and improve retrieval effectiveness on ambiguous queries, with up to six percentage points better performance on most metrics.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12