APPLYING NEURAL NETWORKS TO MOVIE RECOMMENDATION · APPLYING NEURAL NETWORKS TO MOVIE...

APPLYING NEURAL NETWORKS TO MOVIE RECOMMENDATION UTKARSH KAJARIA PROBLEM MODEL TRAINING RESULTS Idea: Use deep learning to incorporate information from the metadata available for movielens dataset (such as tags, genre, average rating) and compare the results with the base model comprising of only userid, item id and ratings. Compare the results treating SVD as the benchmarking algorithm. The Dataset and Benchmark: This dataset contains 5-star rating and tagging activity from MovieLens. It contains 100004 ratings and 1296 user generated tags across 9125 movies. When evaluated using FunkSVD, a well known implementation of SVD, we get a mean absolute error (MAE) of 0.7155 Feature Construction: • userid: vectorized embedding of userid, (similar to word2vec) • itemId: vectorized embedding of itemId • avg_rating: Taken from the imdb website and normalized to be in [-1, 1] • tags: 582 unique tags taken as binary asymmetric features • genres: 20 genres taken as binary asymmetric features Problem Formulation: We model the problem as a multi-class classification problem with 5 possible output values for ratings 1 through 5 and using different combinations of the above features as input. (Shown in the diagram) Training the Network We train a 5 layer neural network consisting of one input layer, 3 hidden layers and one output layer. The diagram below is a conceptual representation. Our output layer has 5 cells. In addition we give dropouts between hidden layers. We train our model for 20, 50 and 100 epochs with: Crossentropy as the loss function, and Adam as the optimization algorithm Results In MAE (Mean Absolute Error) For 20 epochs ANALYSIS AND CONCLUSIONS • It’s clear that as we increase the dropout, we get better performance as overfitting is checked. • Our base model usr_itm outperforms the benchmark of FunkSVD showing that deep learning methods can give us improvements over traditional matrix factorization algorithms. • Secondly, the best model, usr_itm_avg gives a MAE of 0.6674, considerably outperforming our basemodel which shows that avg_rating is a useful factor in predicting movie ratings for individual users. • Third, this tells us that usr_itm_tags model does not perform much better than the base usr_itm suggesting that tags do not offer much value addition in predicting user rating. DATA SCIENCE COLLEGE OF SCIENCE & ENGINEERING avg_rating genres itemid userid tags user_itm user_itm_genre user_itm_avg user_itm_tags relu relu relu softmax Dropout usr_itm (m1) usr_itm_avg (m2) usr_itm_genre (m3) usr_itm_tags (m4) 0.0 0.830 0.801 0.782 0.834 0.1 0.755 0.731 0.725 0.755 0.2 0.726 0.715 0.702 0.718 0.3 0.690 0.699 0.694 0.704 0.4 0.699 0.676 0.685 0.703 0.5 0.6821 0.6674 0.6742 0.6880

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Transcript of APPLYING NEURAL NETWORKS TO MOVIE RECOMMENDATION · APPLYING NEURAL NETWORKS TO MOVIE...

APPLYINGNEURALNETWORKSTOMOVIERECOMMENDATION

UTKARSH KAJARIA

PROBLEM MODELTRAINING RESULTS

Idea:Use deep learning to incorporate information from the metadata availablefor movielens dataset (such as tags, genre, average rating) and comparethe results with the base model comprising of only userid, item id andratings. Compare the results treating SVD as the benchmarking algorithm.

TheDatasetandBenchmark:Thisdatasetcontains5-starratingandtaggingactivityfromMovieLens.Itcontains100004ratingsand1296usergeneratedtagsacross9125movies.WhenevaluatedusingFunkSVD,awellknownimplementationofSVD,wegetameanabsoluteerror(MAE)of0.7155

FeatureConstruction:• userid:vectorized embeddingofuserid,(similartoword2vec)• itemId:vectorized embeddingofitemId• avg_rating:Takenfromtheimdb websiteandnormalizedtobein[-1,1]• tags:582uniquetagstakenasbinaryasymmetricfeatures• genres:20genrestakenasbinaryasymmetricfeatures

ProblemFormulation:Wemodeltheproblemasamulti-classclassificationproblemwith5possibleoutputvaluesforratings1through5 andusingdifferentcombinationsoftheabovefeaturesasinput.(Showninthediagram)

Training the NetworkWe train a 5 layer neural network consisting of one input layer, 3 hiddenlayers and one output layer. The diagram below is a conceptualrepresentation. Our output layer has 5 cells. In addition we give dropoutsbetween hidden layers.

We train our model for 20, 50 and 100 epochs with:

Crossentropy as the loss function, andAdam as the optimization algorithm

ResultsInMAE(MeanAbsoluteError)For20epochs

ANALYSISANDCONCLUSIONS• It’s clear that as we increase the dropout, we get better performance as

overfitting is checked.• Our base model usr_itm outperforms the benchmark of FunkSVD

showing that deep learning methods can give us improvements overtraditional matrix factorization algorithms.

• Secondly, the best model, usr_itm_avg gives a MAE of 0.6674,considerably outperforming our basemodel which shows thatavg_rating is a useful factor in predicting movie ratings for individualusers.

• Third, this tells us that usr_itm_tags model does not perform muchbetter than the base usr_itm suggesting that tags do not offer muchvalue addition in predicting user rating.

DATA SCIENCECOLLEGE OF SCIENCE &ENGINEERING

avg_ratinggenres

itemiduserid

tags

user_itm

user_itm_genre user_itm_avg user_itm_tags

relu relurelu

softmax

Dropout usr_itm (m1)

usr_itm_avg(m2)

usr_itm_genre(m3)

usr_itm_tags(m4)