Recommender System Using SAS - Spears Business · Collaborative Recommender System 8 • Create...
Transcript of Recommender System Using SAS - Spears Business · Collaborative Recommender System 8 • Create...
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2017
SAS Analytics Day
Recommender System Using SAS
Shanmugavel Gnanasekar
Ravishankar Subramanian
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Business Goal• Provide personalized suggestions to the
users based on their preferences. They aid in the decision-making process for the users and make their experience enjoyable.
Cons• These system suffers from inaccuracy.
• To build recommendation system using only ratings.
• Perform text mining on user reviews and combine it with original model to improve its accuracy
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Content-Based Filtering Method
Collaborative-Filtering Method
Data Preparation Create User ProfileCreate Business
ProfileCreate IDF Attributes
Provide Recommendations
Evaluate
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#SCSUG2016 4
Data SnapshotUser Review dataset.
Business Information Dataset
The data collected over 263,000 ratings provided by 21,000 unique users for over 4,000 different restaurants.
name
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Content Based Filtering• It works by learning user preference or profile which is inferred from user ratings
and reviews.
• Then restaurants matching user’s tastes are recommended
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Definitions• Business Profile: Provided in the dataset.
• IDF(Inverse Document Frequency): Created based on number of times an attribute appears in restaurants.
𝐼𝐷𝐹=1
(𝑚𝑎𝑥(1, 𝑛 𝑡𝑖𝑚𝑒𝑠 𝑖𝑡 𝑎𝑝𝑝𝑒𝑎𝑟𝑠 𝑖𝑛 𝑜𝑡h𝑒𝑟 𝑟𝑒𝑠𝑡𝑎𝑢𝑟𝑎𝑛𝑡𝑠))
• User Profile: Build it based on the ratings provided by the user to a restaurant.
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Profiles for Content-Based Filtering
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User Profile is created by aggregating all the individual ratings given by a user to various restaurants.
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Business Profile
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IDF values for various features
IDF Table
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Collaborative Recommender System
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• Create Business and User profile.
• Identify n Neighbors for current user (For our study we used 20 neighbors)
• Recommend top restaurants rated by neighbor weighted by their similarity measure to the given user
Create User ProfileCreate Business
ProfileFind Neighbors
Create Recommendations
Evaluate
Flow for collaborative-based filtering method
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Top Five Suggestions Based On Rating (Collaborative)
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Cluster User Review
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DJCrowd
Music
Club
LoudDance Rock
Concept Link
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Review Clusters
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Cluster Name Descriptive Terms Frequency
Pizza Loversalways + beer + cheese + Crust + good + order + pepperoni + pizza + place + salad + sauce + slice + taste + thin 8,192
Night Life Appetizer + happy hour + beer + bar + great + half + night + roll + price + special 6,055
French Foodback + bread + cheese + chicken + delicious + French + line + long + lunch + minute + night + order 19,853
Chinese Food beef + chicken + Chinese + dis + egg + food + fry + good + lunch + noodle + pork + portion 24,855
Method Content-based filtering
Collaborative filtering
Root Mean Square Error 0.447 0.316
Mean Absolute Error 0.2 0.1
Fit Statistics
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2017
SAS Analytics Day
Shanmugavel [email protected](813) 810 5630https://www.linkedin.com/in/shan-g/
Ravi Shankar [email protected](405) 762 3625www.linkedin.com/in/ravi-shankar-subramanian-b088a079