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?