What effect do outliers have on vector search results?

Outliers can skew similarity measurements negatively during vector searches because they represent data points that differ significantly from the majority of the data in the dataset. When calculating similarity, especially in algorithms like cosine similarity or Euclidean distance, outliers can disproportionately influence the overall distance or angle calculations. This can lead to misleading results, where the true patterns or similarities among the majority of the data points may be obscured by the presence of these extreme values.

For instance, if a dataset primarily consists of vectors that represent typical user behavior, but you have a couple of vectors representing highly abnormal behavior (the outliers), then when searching for similar items or users, the search algorithm may incorrectly prioritize these unusual entries due to their relatively large distances in the vector space. This can degrade the quality of search results, making them less relevant or accurate based on the standard behavior patterns.

The other options do not accurately capture the impact of outliers in vector search scenarios. While enhancing diversity could seem appealing, the way outliers function typically disrupts the coherence of search results rather than enriching them.