Measure similarity, neighbor influence, and weighted item relevance. Test inputs across flexible recommendation scenarios easily. See ranked outputs, plots, and downloads in seconds clearly.
The chart compares neighbor ratings and distances. Selected neighbors appear first after sorting.
| Entity | Feature 1 | Feature 2 | Feature 3 | Observed Rating |
|---|---|---|---|---|
| Target Profile | 4.0 | 3.5 | 4.5 | Unknown |
| Neighbor 1 | 4.2 | 3.4 | 4.8 | 4.9 |
| Neighbor 2 | 2.5 | 3.0 | 2.7 | 2.8 |
| Neighbor 3 | 4.6 | 4.7 | 4.2 | 4.7 |
| Neighbor 4 | 3.8 | 3.2 | 3.9 | 4.1 |
| Neighbor 5 | 1.9 | 2.1 | 1.8 | 2.2 |
1. Euclidean distance
d = √((x1-y1)² + (x2-y2)² + (x3-y3)²)
2. Manhattan distance
d = |x1-y1| + |x2-y2| + |x3-y3|
3. Similarity score
similarity = 1 / (1 + distance)
4. Weighted KNN prediction
predicted rating = Σ(similarity × rating) / Σ(similarity)
5. Simple KNN prediction
predicted rating = Σ(rating) / k
Lower distance means higher similarity. Weighted mode gives closer neighbors more influence, which is often preferred in recommendation problems.
It estimates a recommendation score for a target user or item by comparing nearby neighbors and their known ratings using KNN logic.
Three features keep the tool simple while still showing how multi-dimensional similarity affects distance, neighbor ranking, and final prediction quality.
Use weighted mode when closer neighbors should matter more. It usually produces more realistic recommendations because similarity directly affects each rating’s contribution.
Euclidean measures straight-line separation. Manhattan adds absolute step-by-step differences. Your choice depends on how you want feature gaps to influence similarity.
Smaller k is more sensitive to local patterns. Larger k is more stable. Test several values and compare prediction behavior.
No. It is best for learning, testing assumptions, and quick estimation. Production systems usually use larger datasets, validation, and pipeline controls.
Confidence is derived from average similarity among selected neighbors. Higher similarity suggests a stronger local match to the target profile.
CSV helps you reuse computed rows in spreadsheets. PDF is useful for reports, sharing, and keeping a clean static snapshot.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.