Code Paper

I built a recommender system combining the best of Collaborative Filtering (Matrix Factorization) and Content Analysis (Probabalistic Topic Modeling), and trained it on a dataset I crawled of IMDB movie ratings and scripts for those movies. After training, the system deomonstrated itself to make accurate recommendations on new movies based just on the script

Results

• Vanilla PMF –> Standard Probabilistic Matrix Factorization Recommendation Algorithm
• CTR-in-Matrix –> predictions for movies we have ratings for
• CTR-out-of-Matrix –> predictions for movies we have no ratings for and just use script.
• CTR-LDA-in-Matrix –> predictions for movies with ratings where we use LDA vector in place of learned ratings vector.

Learned Representaitons

The output of LDA (an unsupervised algorithm to learn topic representation) gives word distributions for each topic, and inspecting these topics can give us a sense of what the algorithm is actually learning. Here are a few of the topics clearly signifying some easily interpretable likely meaning as represented by their top 20 words.

• Education → teachers library hawk gentlemen science robinson teaching thank campus study education teach sir university student teacher students college class school

• Film → movies scene stuff feel picture started life camera work big real gonna came things wanted story lot didn kind film

• Christmas → turkey toy bells jingle toys presents mo marley lll vic walt la ho claus year poppy tree lm snow 0000 Music → hear doo stage piano gonna dance night songs guitar record hey playing singing rock play yeah band sing la do

• Law → believe attorney question prison justice murder jury truth state witness years evidence lawyer trial guilty honor didn law judge case

• Military → fight order stand ship soldiers gentlemen enemy aye orders officer soldier thank lieutenant sergeant army colonel general war men captain