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![Content Based, Collaborative
Filtering[CF] and Hybrid
Recommendation System
• Content Based systems focus on properties of items.
Similarity of items is determined by measuring the
similarity in their properties.
Needs History Data.
• Collaborative-Filtering systems focus on the
relationship between users and items. Similarity of
items is determined by the similarity of the ratings of
those items by the users who have rated both items.
Source-http://infolab.stanford.edu/~ullman/mmds/ch9.pdf](https://image.slidesharecdn.com/recommendationsystem-150717043249-lva1-app6891/85/Recommendation-system-4-320.jpg)
![Similarity Notion
• Pearson Correlation - measures the tendency of the numbers[User
Preferences] to move together proportionally. When this tendency is high, the correlation is
close to 1
• Spearman Correlation – Rank based on user preference
• Euclidean Distance - based on the distance between users. Smaller the
distance, more similarity in users.
• Tanimoto Coefficient – based on number of items in common
• LogLikelihood Similarity
How to code?](https://image.slidesharecdn.com/recommendationsystem-150717043249-lva1-app6891/85/Recommendation-system-7-320.jpg)
Uploaded byAkshat Thakar
Recommendation system
The document discusses recommendation systems in web and digital analytics, focusing on collaborative filtering, content-based filtering, and hybrid approaches. Key concepts include various similarity measurement techniques such as Pearson and Euclidean distance and the importance of factors like novelty and diversity in recommendations. Technologies mentioned include Mahout, R with Hadoop, and emerging Spark algorithms.
More Related Content
Recommendation system
- 1.
- 2. Precursor Awareness about Analytics •Jargon Buster • Recommendation System for Web/Digital Analytics • Technology
- 3. • Sentiment Analysis Clustering •Collaborative-based filtering • Item based • User Based Recommendation Similarity Measurement–Pearson, Tanimoto Algorithm - K-means Similarity Measurement - Euclidean Classification NLP • Content-based filtering • Regression • Decision Tree • SVM • NN • Voice Recognition • Video Analytics
- 4. Content Based, Collaborative Filtering[CF]and Hybrid Recommendation System • Content Based systems focus on properties of items. Similarity of items is determined by measuring the similarity in their properties. Needs History Data. • Collaborative-Filtering systems focus on the relationship between users and items. Similarity of items is determined by the similarity of the ratings of those items by the users who have rated both items. Source-http://infolab.stanford.edu/~ullman/mmds/ch9.pdf
- 5. How users aresimilar? CF - User Similarity Similarity Notion User Neighborhood User Based Recommender #1 #2 #3 User Id Item Id Rating Data Model
- 6. CF - ItemSimilarity How items are similar? Similarity Notion Item Based Recommender#1 #2 #3 User Id Item Id Rating Data Model Item-neighborhood Source-http://www.theregister.co.uk/2006/08/15/beer_diapers/
- 7. Similarity Notion • PearsonCorrelation - measures the tendency of the numbers[User Preferences] to move together proportionally. When this tendency is high, the correlation is close to 1 • Spearman Correlation – Rank based on user preference • Euclidean Distance - based on the distance between users. Smaller the distance, more similarity in users. • Tanimoto Coefficient – based on number of items in common • LogLikelihood Similarity How to code?
- 8. How Similarity Definitionaffects Neighborhood formation? Source: http://www.slideshare.net/Cataldo/apache-mahout-tutorial-recommendation-20132014 Mahout In Action Threshold based neighborhood
- 9. Evaluation • Evaluate Topn Recommendations • Precision and Recall Relevant Non Relevant Search Result Shown True Positive False Positive Search result Not Shown False Negative True Negative Source-https://en.wikipedia.org/wiki/Precision_and_recall
- 10. System Solutioning -More than Algorithm Accuracy • Business Goal Injection • Novelty – avoiding repeated recommendations • Diversity – How diverse are recommended items? Does it include all sub topics? • Positive Feedback • Negative Feedback source: http://www.slideshare.net/Zhenv5/diversity-and-novelty-for-recommendation-system
- 11. Technology • Mahout – Hadoop(optional),Java. Lot of stable algorithms. • R Rhadoop Lot of Statistics packages. • Spark Emerging Technology Algorithms are getting added
Editor's Notes
- #4 Recommendation borrows lot of statistical methods and ML techniques. So same algo can occur in clustering or new one. Algo we disccuss today and frequqnetly used one.
- #5 How many of you like gmail, android and google search. Tech Talk, Technical Blogs, Books. I do not know buying behavior, but social behavior.
- #6 Facebook news. Tech talkl, blogs, reading books. Next certification cource.
- #7 Baby items. Beer and diaper. Evaluate both User Similarity vs Item Similarity. How to keep items in same isle.
- #8 School of thought. Preference values. Rank.
- #11 Baby items for old age people, or gap between two kids, two laptop purshases. Google searches.