עברית English

User d651cba51ccc29c0f3f2b0cb779defd5 Responses

← Back to Responses List

Survey Response Breakdown - Survey 178

User ID Submitted At Duration Ideal budget cosine_similaritykl View Response
d651cba51... d651cba51ccc29c0f3f2b0cb779defd5 09-07-2026 11:08 113s [50, 40, 10] 60%40% View Response

User ID: d651cba51ccc29c0f3f2b0cb779defd5

Survey ID: 178

Ideal budget: [50, 40, 10]
⏱️ Response Time: 113s
Survey Summary
Choice Percentage
cosine_similarity (Rank) 60%
kl (Rank) 40%
Pairs List
Pair #1
Original choice: Option 2
Pair Score: 0.13
Score = min(Cosine Similarity advantage, KL advantage). Advantage = how much better one vector is than the other in each metric. Higher score = clearer choice.
Choice Option Type
✓ [55, 10, 35] cosine_similarity: {best: 0.82, worst: 0.75}
[25, 30, 45] kl: {best: -0.31, worst: -0.38}
Pair #2
Original choice: Option 2
Pair Score: 0.11
Score = min(Cosine Similarity advantage, KL advantage). Advantage = how much better one vector is than the other in each metric. Higher score = clearer choice.
Choice Option Type
✓ [70, 10, 20] cosine_similarity: {best: 0.86, worst: 0.82}
[20, 70, 10] kl: {best: -0.23, worst: -0.32}
Pair #3
Original choice: Option 1
Pair Score: 0.11
Score = min(Cosine Similarity advantage, KL advantage). Advantage = how much better one vector is than the other in each metric. Higher score = clearer choice.
Choice Option Type
✓ [60, 10, 30] cosine_similarity: {best: 0.84, worst: 0.80}
[25, 35, 40] kl: {best: -0.26, worst: -0.35}
Pair #4
Original choice: Option 2
Pair Score: 0.11
Score = min(Cosine Similarity advantage, KL advantage). Advantage = how much better one vector is than the other in each metric. Higher score = clearer choice.
Choice Option Type
[70, 10, 20] cosine_similarity: {best: 0.86, worst: 0.82}
✓ [30, 30, 40] kl: {best: -0.23, worst: -0.32}
Pair #5
Original choice: Option 2
Pair Score: 0.11
Score = min(Cosine Similarity advantage, KL advantage). Advantage = how much better one vector is than the other in each metric. Higher score = clearer choice.
Choice Option Type
[60, 10, 30] cosine_similarity: {best: 0.84, worst: 0.76}
✓ [30, 25, 45] kl: {best: -0.29, worst: -0.35}
Pair #6
Original choice: Option 2
Pair Score: 0.10
Score = min(Cosine Similarity advantage, KL advantage). Advantage = how much better one vector is than the other in each metric. Higher score = clearer choice.
Choice Option Type
✓ [60, 10, 30] cosine_similarity: {best: 0.84, worst: 0.80}
[20, 45, 35] kl: {best: -0.29, worst: -0.35}
Pair #7
Original choice: Option 1
Pair Score: 0.10
Score = min(Cosine Similarity advantage, KL advantage). Advantage = how much better one vector is than the other in each metric. Higher score = clearer choice.
Choice Option Type
✓ [65, 10, 25] cosine_similarity: {best: 0.86, worst: 0.82}
[20, 70, 10] kl: {best: -0.23, worst: -0.33}
Pair #8
Original choice: Option 1
Pair Score: 0.10
Score = min(Cosine Similarity advantage, KL advantage). Advantage = how much better one vector is than the other in each metric. Higher score = clearer choice.
Choice Option Type
✓ [75, 10, 15] cosine_similarity: {best: 0.86, worst: 0.82}
[30, 30, 40] kl: {best: -0.23, worst: -0.31}
Pair #9
Original choice: Option 2
Pair Score: 0.10
Score = min(Cosine Similarity advantage, KL advantage). Advantage = how much better one vector is than the other in each metric. Higher score = clearer choice.
Choice Option Type
[10, 55, 35] cosine_similarity: {best: 0.71, worst: 0.63}
✓ [25, 20, 55] kl: {best: -0.45, worst: -0.55}
Pair #10
Original choice: Option 1
Pair Score: 0.10
Score = min(Cosine Similarity advantage, KL advantage). Advantage = how much better one vector is than the other in each metric. Higher score = clearer choice.
Choice Option Type
[10, 65, 25] cosine_similarity: {best: 0.73, worst: 0.68}
✓ [20, 30, 50] kl: {best: -0.41, worst: -0.52}