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Community detection recommender system

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This presentation demonstrate a business case where community detection can be used to find movie recommendations.

Data & Analytics
1 of 34
Movie Recommendation Community Detection for Recommender System MH8351 - Web Analytics Group Project
Agenda Problem Statement Introduction Algorithms Evaluation Criteria Visualization Interesting Findings Community Detection Measure Key and Central Players Visualization Key Player Identification Applications Future Work Finally Data description Preprocessing Feature extraction Graph Construction Data
Introduction
This is story of Annie – A new Movieflix user She just finished watching Shrek and is thinking Why does Movieflix keep recommending animation movie. Just because I watched Shrek doesn't mean I like only animation. It would be nice to watch something else… umm… maybe a period movie.. Can Movieflix read her mind?
List Movies from community List prominent movies from each genre that the community has watched. Identify community Identify community with those movies. Select community with most maximum matching list basis. Input Movie List of movies Annie has watched so far (minimum 1) Mark key player Identify central, keyplayers and neighbor nodes in the community. Recommend To Annie - Movies from other genre preferred by community Probably – community detection Most of the content based recommender system suffer from “cold start” and “overspecialisation” problems. One way to deal with such problem is divide users into communities, identify the community of a new user and then recommend movies that other viewers in the same community have watched
Data Description
Raw Data – Two files – Movies and Ratings Ratings.csv Movies.csv Source: Extract from movie review site imdb (available publicly) – Movies mostly from last decade
Data profile All time classics like Forrest Gump and Shawshank Redemption have large number of review Dominating genre in the whole mix is Drama/Comedy/Thriller/Action Distribution of number of reviews per movie follows a exponential distribution Movies attracting most reviews Prominent Genre Distribution of reviews counts
Graph Generation – Nodes Edges and its meaning Each movie is a node. Only movies between 30 and 180 reviews were considered Node 01 Two movies are connected if a user reviewed both movies positively and gave a score of more then 3 on a scale 1-5 Edge02 Number of common positive reviews. Only weights above particular threshold were considered Edge Weight03 Undirected and weighted Nature of Graph04 The Mask / Comedy The Lion King / Animation 100 common positive reviews
Raw Graph – Nothing but a hairball Edge thickness denote weight Node Size Denote Degree Average degree = 7 Strongly connected Diameter= 5
Community Detection
First thing first – Identify type of network Degree Distribution of nodes indicates graph is similar to scale free network
The chart below compares the output from various community detection Algo. The algorithms are compared in terms of modularity of resulting community cluster and efficiency of execution (CPU times) Let’s get to work – Detect Communities Modularity based method like leading eigenvector and Louvain worked well Top Modularity Score 0.52
Resulting Communities Network has 3 Communities Node size represent Centrality
Let’s see what the viewers groups are like Drama King Nerd Clan Adventure Club
Insight 1 : Genre not a representation of viewer’s mindset Drama King Nerd Clan Adventure Club Each community has movies from variety of genre. Demonstrates that the approach to categorise a new user based on genre could be misleading.
Insight 1: Consider for example the movie Shrek Genre suggests it should be kids movie However PG rating suggests otherwise, a rating of PG usually means not suitable for kids Actually more popular among young adults. Movie review database tells us the true story
Insight 2: Effect of all time hit movies - Expectation Several reviews All time hit movies get high number of reviews. 01 High Degree Thus will have higher degree 03 Several shared reviews with other nodes As a result, these movies will share reviewers with large number of other movies02 Central nodes – Key players These high degree nodes will the central to a community and will uniquely characterize a community04
Insight 2: Effect of all time hit movies - Reality 1. All time hits only served to make community detection difficult 2. Network of all time hits had following characteristics: a. All nodes had high degree b. Poor modularity- 0.08 c. Well interconnected web- poor centrality distinction 3. When these nodes were integrated with overall network, the modularity of overall network dropped significantly. These movies were ultimately removed from the data mix. Possible explanation of such behaviour – Almost everyone have watched and liked all time hit movies, regardless of their movie preferences. As a result, data from these movies provide no information on viewer’s choice.
Key Player Identification
Drama King – Central Player Centrality Measure = Node Betweenness
Drama King – Central Player True lies have common fans with lot other prominent movies of the community
Drama King – Key Players Key Player Measure = Closeness Centrality Top 6
Nerd Clan– Central Player Centrality Measure = Node Betweenness
Nerd Clan– Central Player Fargo connects to all movies with considerable Centrality
Nerd Clan – Key Players The community probably consists of lot of science fiction lovers
Adventure Club – Central Movie
Adventure Club – Central Movie List of all movies Gladiator shares fans with. Genre ranges from thriller to animation to Action
Adventure Club – Key Players Notice how America History turn out to be a keyplayer even thought its degree is very low
Similarly Memento too is quite representative of the community American History shares fan only with Lord of the Rings and Memento Lord of Rings in turn shares fans with all prominent movies of the group Adventure Club – American History
Application and More
List Movies from community If “Shrek” is a keyplayer movie of the community, Jackpot! Choose all keyplayers(across genres). Otherwise, choose movies that are neighbors to “Shrek”. Identify community Identify communities has the movie “Shrek”. (Community Adventure Club) Input Movie Annie has just watched 1 movie – “Shrek” Mark key player Mark keyplayer and central movies of “Adventure Club”. Recommend To Annie – Recommend chosen movie. Recommended movies: Gladiator (Period drama) Lord of the Ring (Fantasy) Pirates of Caribbean (Fantasy) Back to Annie’s problem
Future Work 1. Create overlapping communities 2. Each node should have a probability associated with it of belonging to each of the community 3. Design accommodates hopping user better. If user preference change over time, they can easily follow a chain of movies into another community 4. Add more movies to network to broaden its scope
Thank You Community Detection for Recommender System

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Community detection recommender system

  • 1. Movie Recommendation Community Detection for Recommender System MH8351 - Web Analytics Group Project
  • 2. Agenda Problem Statement Introduction Algorithms Evaluation Criteria Visualization Interesting Findings Community Detection Measure Key and Central Players Visualization Key Player Identification Applications Future Work Finally Data description Preprocessing Feature extraction Graph Construction Data
  • 4. This is story of Annie – A new Movieflix user She just finished watching Shrek and is thinking Why does Movieflix keep recommending animation movie. Just because I watched Shrek doesn't mean I like only animation. It would be nice to watch something else… umm… maybe a period movie.. Can Movieflix read her mind?
  • 5. List Movies from community List prominent movies from each genre that the community has watched. Identify community Identify community with those movies. Select community with most maximum matching list basis. Input Movie List of movies Annie has watched so far (minimum 1) Mark key player Identify central, keyplayers and neighbor nodes in the community. Recommend To Annie - Movies from other genre preferred by community Probably – community detection Most of the content based recommender system suffer from “cold start” and “overspecialisation” problems. One way to deal with such problem is divide users into communities, identify the community of a new user and then recommend movies that other viewers in the same community have watched
  • 7. Raw Data – Two files – Movies and Ratings Ratings.csv Movies.csv Source: Extract from movie review site imdb (available publicly) – Movies mostly from last decade
  • 8. Data profile All time classics like Forrest Gump and Shawshank Redemption have large number of review Dominating genre in the whole mix is Drama/Comedy/Thriller/Action Distribution of number of reviews per movie follows a exponential distribution Movies attracting most reviews Prominent Genre Distribution of reviews counts
  • 9. Graph Generation – Nodes Edges and its meaning Each movie is a node. Only movies between 30 and 180 reviews were considered Node 01 Two movies are connected if a user reviewed both movies positively and gave a score of more then 3 on a scale 1-5 Edge02 Number of common positive reviews. Only weights above particular threshold were considered Edge Weight03 Undirected and weighted Nature of Graph04 The Mask / Comedy The Lion King / Animation 100 common positive reviews
  • 10. Raw Graph – Nothing but a hairball Edge thickness denote weight Node Size Denote Degree Average degree = 7 Strongly connected Diameter= 5
  • 12. First thing first – Identify type of network Degree Distribution of nodes indicates graph is similar to scale free network
  • 13. The chart below compares the output from various community detection Algo. The algorithms are compared in terms of modularity of resulting community cluster and efficiency of execution (CPU times) Let’s get to work – Detect Communities Modularity based method like leading eigenvector and Louvain worked well Top Modularity Score 0.52
  • 14. Resulting Communities Network has 3 Communities Node size represent Centrality
  • 15. Let’s see what the viewers groups are like Drama King Nerd Clan Adventure Club
  • 16. Insight 1 : Genre not a representation of viewer’s mindset Drama King Nerd Clan Adventure Club Each community has movies from variety of genre. Demonstrates that the approach to categorise a new user based on genre could be misleading.
  • 17. Insight 1: Consider for example the movie Shrek Genre suggests it should be kids movie However PG rating suggests otherwise, a rating of PG usually means not suitable for kids Actually more popular among young adults. Movie review database tells us the true story
  • 18. Insight 2: Effect of all time hit movies - Expectation Several reviews All time hit movies get high number of reviews. 01 High Degree Thus will have higher degree 03 Several shared reviews with other nodes As a result, these movies will share reviewers with large number of other movies02 Central nodes – Key players These high degree nodes will the central to a community and will uniquely characterize a community04
  • 19. Insight 2: Effect of all time hit movies - Reality 1. All time hits only served to make community detection difficult 2. Network of all time hits had following characteristics: a. All nodes had high degree b. Poor modularity- 0.08 c. Well interconnected web- poor centrality distinction 3. When these nodes were integrated with overall network, the modularity of overall network dropped significantly. These movies were ultimately removed from the data mix. Possible explanation of such behaviour – Almost everyone have watched and liked all time hit movies, regardless of their movie preferences. As a result, data from these movies provide no information on viewer’s choice.
  • 21. Drama King – Central Player Centrality Measure = Node Betweenness
  • 22. Drama King – Central Player True lies have common fans with lot other prominent movies of the community
  • 23. Drama King – Key Players Key Player Measure = Closeness Centrality Top 6
  • 24. Nerd Clan– Central Player Centrality Measure = Node Betweenness
  • 25. Nerd Clan– Central Player Fargo connects to all movies with considerable Centrality
  • 26. Nerd Clan – Key Players The community probably consists of lot of science fiction lovers
  • 27. Adventure Club – Central Movie
  • 28. Adventure Club – Central Movie List of all movies Gladiator shares fans with. Genre ranges from thriller to animation to Action
  • 29. Adventure Club – Key Players Notice how America History turn out to be a keyplayer even thought its degree is very low
  • 30. Similarly Memento too is quite representative of the community American History shares fan only with Lord of the Rings and Memento Lord of Rings in turn shares fans with all prominent movies of the group Adventure Club – American History
  • 32. List Movies from community If “Shrek” is a keyplayer movie of the community, Jackpot! Choose all keyplayers(across genres). Otherwise, choose movies that are neighbors to “Shrek”. Identify community Identify communities has the movie “Shrek”. (Community Adventure Club) Input Movie Annie has just watched 1 movie – “Shrek” Mark key player Mark keyplayer and central movies of “Adventure Club”. Recommend To Annie – Recommend chosen movie. Recommended movies: Gladiator (Period drama) Lord of the Ring (Fantasy) Pirates of Caribbean (Fantasy) Back to Annie’s problem
  • 33. Future Work 1. Create overlapping communities 2. Each node should have a probability associated with it of belonging to each of the community 3. Design accommodates hopping user better. If user preference change over time, they can easily follow a chain of movies into another community 4. Add more movies to network to broaden its scope
  • 34. Thank You Community Detection for Recommender System