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1. Finding Similar Items, Similarity
of Sets, and Collaborative
Filtering
By U MANJUNATH
1GG21CS051

2. Introduction to Similarity Measures
Quantifying Relationships
Measuring how alike or dissimilar objects are
Key Applications
Recommendation systems, search engines, anomaly
detection

3. Set Similarity: Jaccard
Similarity
Jaccard Index
Ratio of intersection to
union of two sets
Applications
Document similarity, image
recognition, clustering

4. Item-to-Item Similarity:
Cosine Similarity
Vector Space
Items represented as
vectors in multi-
dimensional space
Cosine of Angle
Measures the angle
between vectors, ranging
from 0 to 1

5. Collaborative Filtering: User-User
Similarity
User Profiles
Based on past interactions with items
Similarity Score
Measured using metrics like Pearson correlation
Recommendations
Users similar to the target are used for recommendations

6. Collaborative Filtering: Item-
Item Similarity
1 Item Profiles
Representing features and attributes
2 Similarity Calculation
Based on co-occurrence or shared preferences
3 Recommendations
Items similar to those liked by the user are suggested

7. Applications of Similarity-Based
Approaches
Recommendation Systems
Suggesting products, movies, music, etc.
Search Engines
Ranking search results based on relevance
Anomaly Detection
Identifying outliers or unusual patterns

8. Challenges and Limitations
1
Data Sparsity
Limited user interactions and incomplete information
2
Cold Start
Difficulty in recommending items to new users
3
Scalability
Computational challenges with large datasets

9. Conclusion and Future Directions
1
Promising Tools
For understanding data and making intelligent predictions
2
Advancements in AI
Developing more robust and scalable similarity measures
3
Future Applications
Exploring new areas like personalized medicine and
social networks