Recommender Systems III

Table Of Contents
  1. General
  2. Different Approaches of using DL in Recommender Systems
  3. Code Exercises

General

Where we focus

  • User-Item Recommendation
  • Item-Item Recommendation
    • Content based recommendation
    • Collaborative Filtering

Recall: Notes on Item-Item Recommendations

  • recommendation based on last item:“If you are interested in this, you probably also like …”
  • doesn’t need a user model
  • Item model needed→ How to model similarities between items?

Recall: Collaborative Filter

…

…

….

Content based Recommendation

  • we have more information about the film than just a number, we know the content of the film→ how to find similar items based on item properties and/or meta data?

Possible solutions:

  • Compute distance between movies for example (e.g. calculate sequences)→ possible with DL, but too expensive in most cases
  • use meta data→ Categorization, actors/directors, year, …
  • use simpler measure→ e.g. analyze movie poster

Recall: Benefits of DL

  • capacity of the learned mapping→ tensor input/output, like pictures, videos, tables, …

  • End-to-End Learning→ learn decision space and function in one optimization problem

    • Different Approaches of using DL in Recommender Systems

    Autoencoder

    • instead of a SVD a non-linear operation to compress data
    • consists of two parts:
      • encoder → compresses data in a latent space
      • Decoder →
    • we measure the distances in the compressed space
    • we can add additional evaluations to our loss function based on previous ratings

    Basic CNN Approach

    • Use CNNs for item (and user) content feature extraction
    • Examples:
      • Movie similarity by image similarity of posters
      • Clothing and fashion items, e.g. I like this kind heel, suggest me other shoes that have a similar heel
    • can directly combined with AE or SVD

    RNN based Recommender Systems

    • Using RNNs for text feature extraction:
    1. User features: reviews and comments by a user→ if somebody often insults, a negative rating doesn’t value much→ if somebody only writes positive things, a negative rating has more impact
    2. Item features: text description and reviews of item
    • s1, s2, … are words/ sequences of the text

    Hybrid Recommender Systems

    Combined Measurements

    • Film poster
    • Text (Meta data)
    • User Ratings

    Code Exercises

    (Links to Github)

    Autoencoder_with_Keras_week8.ipynb

    collaborative_filtering_with_Keras_week8.ipynb