Recommendation Movie - Computer Scienceark/654/team/5/presentation3.pdf · Introduction...
Transcript of Recommendation Movie - Computer Scienceark/654/team/5/presentation3.pdf · Introduction...
Movie Recommendation
Team Valak
Manpreet Kaur
Hao Su
Overview
● Introduction● Movie Recommender Phases
○ Locality Sensitive Hashing○ Intra Similarity○ Inter Similarity
● Sequential Algorithm● Parallel Algorithm● Demo● References
IntroductionRecommendation System: A technique which predicts what the user may like based on his history of interaction with the system. They are applied in fields like:
● Online Shopping● Browsing Portals● Streaming Portals
Techniques used to implement a recommendation system are:
● Content Based○ Only contents in the same categories will be recommended
● Collaborative Filtering○ Based on past behavior
Collaborative FilteringBased on the similarity in preferences, tastes and choices of users to analyses how similar users are and makes recommendation.
● Memory Based○ User - Item: users who are similar to you also liked …○ Item - Item: users who liked this also liked …
Movie Recommender
● Phase 1: Item Partitioning- Elements in the groups will be divided using minHash technique.
● Phase 2: Intra Similarity Phase - Compute the item similarity for every pair of items ti and t j belonging to the same group.
● Phase 3: Inter Similarity Phase - Compute the item similarity for every pair of items ti and t j belonging to the different group if these two items have received rating from same user.
● Phase 4: Find Recommendation - Find the similar items for the top rated items rated by the target user using similarity matrix constructed in above phases.
Phase1 :Locality Sensitive Hashing
1. Build an m×n matrix where every shingle that appears in a set is marked with a 1 otherwise 0.
2. Permute the rows of the matrix from step 2 and build a new p×n signature matrix where the number of the row of the first shingle to appear for a set is recorded for the permutation of the signature matrix.
3. Repeat permuting the rows of the input matrix MAX_PERMUTATIONS times and complete filling in the p×n signature matrix.
4. Choose a band size b for the number of rows for each item and compute hash value for each band.
Phase1: Locality Sensitive Hashing
Phase1: Locality Sensitive Hashing
Phase1: Locality Sensitive Hashing
Phase1: Locality Sensitive Hashing
Phase1: Locality Sensitive Hashing
Collaborative FilteringSimilarity matrix
- Metrics that measure similarity between two items.
- Pearson correlation
Sequential Algorithm
● Phase 1: Run Partitioning phase to divide the N items into K buckets.
● Phase 2: compute the similarity of every pair of items belonging to the same
bucket.
● Phase 3: Finally, we calculate the similarity of related item pair included in
different groups.
● Phase 4: Select the closest K neighbors for the top ratings given by the target
user.
● Recommend the top V movies by prediction.
Sequential Algorithm
Parallel Algorithm
● Phase 1: Run Partitioning phase to divide the N items into K buckets. Signature
matri will be computed in parallel.
● Phase 2: compute the similarity of every pair of items belonging to the same
bucket. Each bucket will be handled by separate threads.
● Phase 3: Finally, we calculate the similarity of related item pair included in
different groups. Each thread will handle different related pairs.
● Phase 4: Select the closest K neighbors for the top ratings given by the target
user. Each top rating of user will be handled by separate threads.
● Recommend the top V movies by prediction.
Phase1: Partitioning changes
Phase 2: Intra Similarity Phase Changes
Similarity Matrix
Phase3: Inter Similarity Computation Changes
Parallel Algorithm
Demo
References
- A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering By SongJie Gong
- Seunghee Kim, Hongyeon Kim and Jun-Ki Min, “An efficient parallel similarity matrix construction on MapReduce for collaborative filtering”, The Journal of Supercomputing, 06 February 2018, 1-19.
- https://medium.com/@james_aka_yale/the-4-recommendation-engines-that-can-predict-your-movie-tastes-bbec857b8223
- https://towardsdatascience.com/various-implementations-of-collaborative-filtering-100385c6dfe0
- https://www.learndatasci.com/tutorials/building-recommendation-engine-locality-sensitive-hashing-lsh-python/
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Any Question?