LARS copy.pptx - LARS A Location-Aware Recommender System...

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LARS A L ocation-A ware R ecommender S ystem GROUP MEMBER: 1. Md. Abul Kalam Azad 2. Abu Md. Sabbir Hassan Chowdhury 3. Uzzal Kar SUPERVISOR: SHM Md. Saddam Hossain Mukta Associate Professor, Computer Science and Engineering United International University CSE 6215 : Spatial Data Science (M)
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Recommender Systems – Basic Idea (1/2) Recommender Systems – Basic Idea (2/2) Analyze user behavior to recommend personalized and interesting things to do/read/see Users : provide opinions on items consumed/watched/listened to… The system : provides the user suggestions for new items Location Matters: Preference Locality Collaborative filtering process is the most commonly used one in Recommender Systems Movie preferences differ based on the user location
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Preference Locality Travel Locality LARS Main Idea Location-based Ratings LARS Solution Spatial User Ratings for Non-Spatial Items Non-Spatial User Ratings for Spatial Items Spatial User Ratings for Spatial Items Talk Outline Traditional Recommender Systems + Incorporating Users Locations User Item Rating Karim Restaurant X 4.5 Rohim Restaurant Y 2 User Location Uttara, Dhaka Motijheel, Dhaka Restaurant X Karim Karim located at Uttara, Dhaka rating a restaurant X located at Mirpur, Dhaka Restaurant Location Mirpur, Dhaka Motijheel, Dhaka
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Location-based Ratings Taxonomy LARS goes beyond the traditional rating triple (user, item, rating) to include the following taxonomy: Spatial User Rating for Non-spatial Items (user_location, user, item, rating) Example: A user with a certain location is rating a movie Recommendation: Recommend me a movie that users within the same area have liked Non-spatial User Rating for Spatial Items (user, item_location, item, rating) Example: A user with unknown location is rating a restaurant Recommendation: Recommend a nearby restaurant Spatial User Rating for Spatial Items (user_location, location, item_location, item, rating) Example: A user with a certain location is rating a restaurant
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Spatial User Ratings For Non-Spatial Items (1/3)
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Spatial User Ratings For Non- Spatial Items (2/3) Adaptive Pyramid Structure. Three main goals: Locality Scalability. Influence. Smaller cells more “localized” answers User Partitioning
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Merging : reduces the number of maintained cells 4-cell quadrant at level ( h +1) “merged” into parent at level h Queries at level ( h +1) now service at level h for merged region Merging decision made on trade-off between locality loss and scalability gain Splitting : increases number of cells Opposite operation as merging Splitting decision made on trade-off between locality gain and scalability loss Maintenance results in partial pyramid structure Spatial User Ratings For Non- Spatial Items (3/3)
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