Context-based Entity Recommendation on Real-Life Knowledge Work in Context (RLKWiC dataset)

<h2><a href="../records/11059573">RLKWiC</a> Add-on: Benchmarking Dataset for Entity Recommendation</h2> <p>This benchmark, built on top of the Real-Life Knowledge Work in Context (<a href="../records/11059573">RLKWiC</a>) dataset, is designed to evaluate context-based entity recommendation by simulating a scenario where participants receive entities extracted from their activities across their defined contexts.&nbsp;</p> <p>In total, 1850 entity recommendations were generated across 56 contexts. After deduplication, these entities were presented to participants for explicit relevance assessment on a 3-point scale:</p> <ul> <li>0 [Irrelevant]: Signifying a lack of relevance between the recommended entity and the context.</li> <li>1 [Relevant] Denoting a connection between the entity and the context, although it may not fully represent it.</li> <li>2 [Representative]: The entity closely aligns with the context, indicating a high level of relevance where the context can be inferred to be about this entity.</li> </ul> <p>Participants could also suggest additional relevant entities. The resulting dataset comprises 1067 entities with explicit relevance scores, offering a resource for benchmarking entity recommendation in real-life knowledge work.</p> <h3><strong>Paper: </strong><a href="https://dl.acm.org/doi/10.1145/3640457.3688068" target="_blank" rel="noopener">Context-based Entity Recommendation for Knowledge Workers: Establishing a Benchmark on Real-life Data</a></h3>