Movie topics- Efficient features for movie recommendation systems

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User written movie reviews carry substantial amounts of movie related features such as description of location, time period, genres, characters, etc. Using natural language processing and topic modeling based techniques, it is possible to extract features from movie reviews and find movies with similar features.

Tecnologia

Efficient Features for
Movie Recommendation
Systems
Project presentation
Suvir Bhargav

Outline
● Motivation and Why movie reviews
● Problem statement
● How? or the overall system
● Text preprocessing approaches
● Postprocessing: movie topics from a reviews
corpus
● Similarity
● Experimental setup and results

Thanks to Sean Lind, source: http://www.silveroakcasino.com/blog/posts/netflix/what-to-watch-on-netflix.html
Motivation

Motivation
● movie genres are not enough.
● classify movies
○ keywords
○ moods
○ imdb ratings
○ micro genres

micro genres
source: http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/

Why movie reviews?
Source: a sample user written movie review from imdb

Problem statement
● Feature extraction from user reviews of
movies
● Use extracted features to find similar
movies.

The overall system
Movie reviews corpus
● preprocessing
○ tokenization, stopwords, lemmatized.
● post processing
○ topic modeling: Movie topics from a reviews corpus
● similarity measure
○ return movies with similar topics distribution

Text preprocessing
tokenization, stopwords, lemmatized.
Simple information extraction
Figure credit to nltk book.

Post processing
Document representation: Vector Space Model (VSM)
Picture credit: pyevolve

Post processing: generative model
source: David blei’s slide

Post processing: LDA
For each document in the collection, the words can be generated
in two stage process
1) Randomly choose a distribution over topics.
2) For each word in the document
a) Randomly choose a topic from the distribution over
topics in step 1.
b) Randomly choose a word from the corresponding
distribution over the vocabulary
Documents exhibit multiple topics

Similarity Measure
● Cosine Similarity
● KL divergence
● Hellinger distance

The overall system: implementation
Movie reviews corpus
● preprocessing
○ nltk and gensim’s simple preprocessing.
● post processing
○ gensim python wrapper to MALLET
○ index topic distribution of query movies, q and 1k
movies corpus, C.
● similarity measure
○ python numpy implementation
○ apply distance metric on indexed q and C.
○ sort and pick top 5 movies.

Experimental setup
Movie reviews corpus of 1k movies
reviews data source: imdb

Conclusion
● Movie topics as efficient features for RS
○ represents movies by underlying semantic patterns
○ useful for capturing movie genre and mood.
○ but not so well with plot.
○ user written movie reviews are useful movie meta-data.
● The developed prototype
○ easy to add more movie meta-data
○ python allows scalability.
○ Topics as an explanation needs further tuning.

Future directions
● Movie review preprocessing
○ bigram, trigrams.
○ create multi-word movie keywords or language
construction
● Building complex topic models
○ Hierarchical LDA
○ author-topic model
■ include authorship information.
■ similarity between authors

Thank You
Questions ?
Image src: http://www.brinvy.biz/177215/batman-catching-a-ride-on-supermans-back-funny-hd-wallpaper-x.html

Extra slides
List of extra slides and notes
● Original LDA paper
● introduction to probabilistic topic modeling
● and A. Huang’s Similarity measures for text document
clustering
● Another good LDA description
● Integrating out multinomial parameters in LDA
● language construction in micro genres

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Movie topics- Efficient features for movie recommendation systems

1. Efficient Features for Movie Recommendation Systems Project presentation Suvir Bhargav

2. Outline ● Motivation and Why movie reviews ● Problem statement ● How? or the overall system ● Text preprocessing approaches ● Postprocessing: movie topics from a reviews corpus ● Similarity ● Experimental setup and results

3. Thanks to Sean Lind, source: http://www.silveroakcasino.com/blog/posts/netflix/what-to-watch-on-netflix.html Motivation

4. Motivation ● movie genres are not enough. ● classify movies ○ keywords ○ moods ○ imdb ratings ○ micro genres

5. micro genres source: http://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/

6. Why movie reviews? Source: a sample user written movie review from imdb

7. Problem statement ● Feature extraction from user reviews of movies ● Use extracted features to find similar movies.

8. The overall system Movie reviews corpus ● preprocessing ○ tokenization, stopwords, lemmatized. ● post processing ○ topic modeling: Movie topics from a reviews corpus ● similarity measure ○ return movies with similar topics distribution

9. Text preprocessing tokenization, stopwords, lemmatized. Simple information extraction Figure credit to nltk book.

10. Post processing Document representation: Vector Space Model (VSM) Picture credit: pyevolve

11. Post processing: generative model source: David blei’s slide

12. Post processing: LDA For each document in the collection, the words can be generated in two stage process 1) Randomly choose a distribution over topics. 2) For each word in the document a) Randomly choose a topic from the distribution over topics in step 1. b) Randomly choose a word from the corresponding distribution over the vocabulary Documents exhibit multiple topics

13. Movie topics from a reviews corpus

14. Similarity Measure ● Cosine Similarity ● KL divergence ● Hellinger distance

15. Similarity Measure Cosine Similarity

16. Similarity Measure Hellinger Distance

17. The overall system: implementation Movie reviews corpus ● preprocessing ○ nltk and gensim’s simple preprocessing. ● post processing ○ gensim python wrapper to MALLET ○ index topic distribution of query movies, q and 1k movies corpus, C. ● similarity measure ○ python numpy implementation ○ apply distance metric on indexed q and C. ○ sort and pick top 5 movies.

18. Experimental setup Movie reviews corpus of 1k movies reviews data source: imdb

19. Experimental setup Evaluation criteria

20. Conclusion ● Movie topics as efficient features for RS ○ represents movies by underlying semantic patterns ○ useful for capturing movie genre and mood. ○ but not so well with plot. ○ user written movie reviews are useful movie meta-data. ● The developed prototype ○ easy to add more movie meta-data ○ python allows scalability. ○ Topics as an explanation needs further tuning.

21. Future directions ● Movie review preprocessing ○ bigram, trigrams. ○ create multi-word movie keywords or language construction ● Building complex topic models ○ Hierarchical LDA ○ author-topic model ■ include authorship information. ■ similarity between authors

22. Thank You Questions ? Image src: http://www.brinvy.biz/177215/batman-catching-a-ride-on-supermans-back-funny-hd-wallpaper-x.html

23. Extra slides List of extra slides and notes ● Original LDA paper ● introduction to probabilistic topic modeling ● and A. Huang’s Similarity measures for text document clustering ● Another good LDA description ● Integrating out multinomial parameters in LDA ● language construction in micro genres

24. LDA

Notas do Editor

movie similarity and then we finish with Conclusion and future directions.
made for a popular streaming service
user reviews are read not only to know how good or bad is the movie, but also to know what the movie is about. more than sentiment analysis. gives audience point of view.
Use extracted features but it could be used for other purposes as well.
System as we can implement each part as a module finish the one complete cycle and then repeat cycle if time .
preprocessing is general to data processing, here it is text text processing using NLTK toolkit start with small examples. chunking, named entity extraction. tokenization, stopwords, lemmatized.
before starting, lets understand document representation. DR is important part of information retrieval.
Main intuition is document exhibit multiple topics Simple intuition: Documents exhibit multiple topics, so does movie reviews about the movie, if preprocessing removes irrelevant words. Not all the topics of a review are important. Example Sentences 1 and 2: 100% Topic A Sentences 3 and 4: 100% Topic B Sentence 5: 60% Topic A, 40% Topic B Topic A: 30% broccoli, 15% bananas, 10% breakfast, 10% munching, … (at which point, you could interpret topic A to be about food) Topic B: 20% chinchillas, 20% kittens, 20% cute, 15% hamster, … (at which point, you could interpret topic B to be about cute animals) discover the hidden themes from the collection. annotate the documents according to those themes. use annotations to organize, summarize, search, form predictions
LDA is a statistical model of document collections that tries to capture this intuition
after training LDA model, we can look at the generated topics. notice each detail here
the Hellinger distance between P and Q is defined as It is important to note that for cosine similarity, higher value is better whereas for hellinger distance, smaller value represents more similarity.
start with: we developed the prototyping system Useful for capturing movie genre and mood information. the system to 10k movies with some effort. user written movie review contains information about movies.

Movie topics- Efficient features for movie recommendation systems

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Movie topics- Efficient features for movie recommendation systems

Notas do Editor