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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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
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.
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.
Business Profile
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IDF values for various features
IDF Table
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
Top Five Suggestions Based On Rating (Collaborative)
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Cluster User Review
10
DJCrowd
Music
Club
LoudDance Rock
Concept Link
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
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