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MelonRec-W2V

1. Competition Overview

Goal: To predict songs and tags that were not given when half or all of the songs and tags in the playlist are unseen

data

  • Playlist metadata (title, song, tag, number of likes, update time)
  • Song metadata (song title, album title, genre, title date)
  • Mel-spectrogram of the song

Number of participating teams: 786 teams

2. Issue

Challenge: all of the songs and tags in the playlist are unseen

Approach - Multi-Modal Retrieval - Query by Song, Tag, Title

Main Issue

  1. Make co-embedding space that contain song vector, tag vector, title vector
  2. Evaluate that the embedding space learns the semantic relationship.
  3. Cover all retrieval scenario
    • Given Song to Song retrieval (Playlist Continuous)
    • Given Playlist Tag to Tag retrieval (Playlist Auto-tagging)
    • Given Playlist tag to Song retrieval (Unseen Item retrieval)
    • Title to Tag and Song retrieval (Sentence to Item retrieval)

3. Idea

Approach

  • Make co-embedding space with Word2vec Method
  • Single Modal Retrieval
    • Voting each modality
  • Multi Modal Retrieval
    • Mean each modality
  • Cluster Based Retrieval

4. Approach

  1. Train Co-embedding Space (Word2Vec Embedding)

    • Input: Sentence (Title token, Tag, Genre, Song, Plylist id)
    • Ouput: Word, Item, Song, Plylist Vector
  2. Multi-Modality Retrieval (Inference)

  3. Evaluation with ndcg

    • tag wise, song wise

Dependencies

  • numpy 1.16.2
  • pandas 0.24.2
  • matplotlib 3.0.3
  • tqdm 4.31.1
  • gensim 3.8.3
  • sentencepiece 0.1.91
  • sklearn 0.20.3
  • khaiii
  • pytorch 1.5.1

4. Folder and Files

  • data download (link)
    • train.json, val.json, test.json, genre_gn_all.json, song_meta.json

Learning code (from scratch)

Best model hyper-parameter, window: 100, vector: 300, mincount: 10, iteration: 50, Skip-gram

$ train_embedding.py
$ embedding_most_similar.py

5. To-do

Measure Playlist-Song's Mean and Playlist's Vector Similarity

  • Mid-Evaluation of Embedding Space: KL Divergence
  • Training Method: Self-Supervised Approach

6. Reference