The Dark Side of Relevance: The Effect of Non-Relevant Results on Search Behavior
Bibliographic record
Abstract
Understanding and modelling user behavior with search results is important to both search engine designers and the design of effectiveness measures. It is well established that users are less likely to view lower ranked search results, and recent research has shown that the type of relevant documents can influence when people stop examining results. However, while existing measures and research consider that relevant documents vary in utility and make use of relevance grades or preference judgments, non-relevant documents are largely all treated the same. In this paper, we show that the nature of non-relevant material affects users’ willingness to further explore a ranked list of search results. We first broaden our notion of non-relevant documents and define a spectrum of possible search engine result pages (SERPs). At one end of the spectrum, the search results were filled with off-topic non-relevant documents, and at the other end, the non-relevant documents were all on-topic, but failed to match the required sub-topic of the search task. We conducted a user study where participants used a mobile search interface to find answers to questions, and collected participants’ behavior while interacting with different SERPs on our spectrum. Our results show that user examination of search results, and time to query abandonment, is influenced by the coherence and type of non-relevant documents included in the SERP. When the SERP is coherent on an egregious topic, users spend the least amount of time before abandoning and are less likely to request to view more results. The time they spend increases as the SERP quality improves, and users are more likely to request to view more results when the SERP contains diversified non-relevant results on multiple subtopics. Our research implies that to improve information retrieval evaluation, we should be assessing the degree of non-relevance in search results as well as the degree of relevance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".