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ReciSys: Hybrid Recipe Recommendation with Attention & NLP

This project studies rating prediction on the Food.com dataset using modern recommender system techniques.

I explore two complementary predictive tasks:

  1. Hybrid Recommendation (Collaborative + Content-Based)

    • Predict user–recipe ratings using an attention-based neural recommender
    • Combines user embeddings, item embeddings, and rich recipe metadata
    • Model: SideFeatAttAE

  2. NLP-Based Rating Prediction

    • Predict ratings directly from review text
    • Models: Fine-tuned DeBERTa and TF-IDF + Ridge regression

Together, these tasks examine how structured interactions and natural language capture user preferences from different perspectives.

Dataset: Food.com (Majumder et al., EMNLP 2019)

  • 230K+ recipes
  • 1M+ user interactions
  • 18 years of data (2000–2018)

After filtering:

  • ~180K recipes
  • ~700K interactions

Original dataset goal: personalized recipe generation
This project focuses on rating prediction and representation learning.

Goal: Predict a user's rating for a recipe.

Inputs

  • User ID
  • Recipe ID
  • Recipe side features:
    • cooking time
    • number of steps
    • number of ingredients
    • nutrition vector
    • recipe tags

Model

  • User & item embeddings
  • Side-feature embedding via MLP
  • Multi-head attention over [user, item, side] tokens
  • MLP head + bias terms

Baselines

  • Global mean
  • User mean
  • Item mean
  • Matrix Factorization (SVD)

Metric

  • Mean Squared Error (MSE)

Goal: Predict rating from review text alone.

Models

  • Fine-tuned DeBERTa-v3 (regression)
  • TF-IDF + Ridge regression

Why NLP?

  • Captures sentiment, negation, and subtle phrasing
  • Complements collaborative signals

Metric

  • Mean Squared Error (MSE)
Model Test MSE
SideFeatAttAE 0.712
Matrix Factorization (SVD) 0.753
Global Mean 0.845
TF-IDF + Ridge (NLP) 0.378
DeBERTa (NLP) 0.286

  • Attention-based hybrid models outperform classical CF baselines

  • Rich side information improves robustness and cold-start behavior

  • NLP models can recover ratings extremely well from language alone

  • The architecture generalizes beyond Food.com to many recommendation domains

  • notebook/ReciSys.ipynb – full implementation and experiments

  • slides/ReciSys.pptx – presentation walkthrough

  • report/ReciSys_Report.pdf – written report

  • watch: video presentation

Dataset Citation

This project uses the Food.com dataset introduced in:

Bodhisattwa Prasad Majumder*, Shuyang Li*, Jianmo Ni, Julian McAuley.
Generating Personalized Recipes from Historical User Preferences.
EMNLP–IJCNLP, 2019.

If you use this dataset, please cite the paper above.