Training own recommendation model for diploma thesis

Here's a concise, actionable roadmap for training a recommendation model for your diploma thesis, focused on rigor and feasibility within academic constraints:

1. Define Scope & Problem (Critical First Step)

• ·Narrow the domain: Specify exactly what you're recommending (e.g., "movies to users based on implicit feedback," "academic papers to researchers"). Avoid overly broad goals.
• ·Formulate a clear research question: Examples:
  • ·"How does incorporating temporal dynamics improve accuracy in sequential session-based recommendations compared to static matrix factorization on [Dataset]?"
  • ·"Can a lightweight hybrid model (content + collaborative filtering) outperform pure collaborative filtering for cold-start items in [Domain]?"
• ·Justify thesis contribution: Is it novelty (new method), empirical validation (comparing existing methods on new data), or application (solving a specific problem)? This drives your entire methodology.

2. Data Acquisition & Preparation (Where Most Theses Succeed/Fail)

• ·Source: Use established, public benchmarks unless your thesis requires proprietary data (get IRB/ethics approval early if using user data):
  • ·General: MovieLens (100K/1M), Amazon Review datasets, Last.fm, DBLP.
  • ·Domain-specific: MIMIC-III (healthcare), ArXiv (academic), Steam (games).
• ·Non-negotiable steps:
  • ·Document everything: Source, size, timestamp, preprocessing code (keep raw data immutable).
  • ·Handle implicit vs. explicit feedback: Most real-world data is implicit (clicks, views); adjust loss functions accordingly (e.g., BPR loss).
  • ·Cold start analysis: Explicitly report % of new users/items in test set.
  • ·Avoid leakage: Split temporally if possible (train on past, predict future); never use future data in training.
• ·Size tip: For thesis work, 100K-1M interactions is often sufficient to demonstrate concepts without excessive compute. Start small, scale if needed.

3. Model Selection: Start Simple, Then Iterate

• ·Baseline is mandatory: Implement a strong, simple baseline first (e.g., Item-Item CF with cosine similarity, or SVD++). Your thesis must show your method beats this.
• ·Progression path (choose 1-2 based on scope):
  1. ·Memory-based CF (User-Item/Item-Item): Quick to implement, great for baselines/analysis.
  2. ·Matrix Factorization (SVD, SVD++, FunkSVD): Classic, interpretable, solid foundation.
  3. ·Factorization Machines (FM): Handles side features well (content + CF hybrid).
  4. ·Sequential Models (GRU4SAS, SASRec): If temporal dynamics are your focus.
  5. ·GNNs (LightGCN, PinSage): Only if you have rich graph data (social + item relations) and need SOTA; often overkill for thesis.
• ·Avoid: Jumping straight to complex deep learning without justification. Professors value clear analysis over black-box results.

4. Evaluation: Be Rigorous & Transparent

• ·Metrics:
  • ·Accuracy: Precision@K, Recall@K, NDCG@K (K=5,10,20 standard). Report multiple K.
  • ·Beyond accuracy: Diversity, novelty, serendipity (if relevant to your RQ).
  • ·Efficiency: Training/inference time (important for applicability).
• ·Validation:
  • ·Use hold-out testing (not just cross-validation unless justified for small data).
  • ·Statistical significance: Use t-tests or bootstrap to confirm improvements aren't random.
  • ·A/B test simulation: If possible, compare against baseline on same test set.
• ·Error analysis: Qualitatively inspect failures (e.g., "Does model fail on niche genres? Popularity bias?").

5. Thesis-Specific Deliverables

• ·Literature Review: Focus on why your approach addresses a gap (cite 10-15 key papers; avoid laundry lists).
• ·Methodology:
  • ·Pseudocode for key algorithms (or clear reference to implementation).
  • ·Diagram of your model architecture (even for CF).
  • ·Hyperparameter search space & range (show you didn't just pick defaults).
• ·Results:
  • ·Tables comparing baselines vs. your method (mean ± std over multiple runs if stochastic).
  • ·Ablation studies (e.g., "Removing component X drops Recall@10 by Y%").
  • ·Visualization: Learning curves, t-SNE of embeddings, case studies.
• ·Discussion:
  • ·Limitations honestly (data size, model assumptions, evaluation constraints).
  • ·Future work tied to thesis findings.
  • ·Ethics: Bias analysis (e.g., popularity bias), fairness notes if using sensitive data.

Key Pitfalls to Avoid

• ·❌ Vague problem statement: "Build a better recommender" → ❌ Thesis fails.
• ·✅ Specific: "Reduce popularity bias in long-tail movie recommendations via adversarial regularization on MovieLens 20M."
• ·❌ Ignoring baselines: Comparing only to random or naive averages → ❌ Weak contribution.
• ·❌ Overclaiming: "Our SOTA model beats all others" on a tiny, non-standard dataset → ❌ Unconvincing.
• ·✅ Credible: "Our hybrid FM improves Recall@10 by 3.2% over LightFM (p<0.01) on ML-1M, with 40% less training time than NGCF."
• ·❌ Poor reproducibility: Not sharing code/data links (or explaining restrictions) → ❌ Undermines validity.

Tools & Efficiency Tips

• ·Frameworks: Surprise (for CF baselines), TensorFlow/PyTorch (for deep learning), LibRec, LightFM.
• ·Reproducibility: Use git, requirements.txt/environment.yml, and seed everything (numpy.random.seed(42)).
• ·Compute: Start on CPU/laptop; use Colab Pro for short GPU bursts if needed. Avoid wasting weeks on tuning massive models unnecessarily.
• ·Timeline Suggestion (adjust to your schedule):
  • ·Weeks 1-2: Problem definition, lit review, data setup.
  • ·Weeks 3-5: Baselines, initial results, iterate on approach.
  • ·Weeks 6-8: Advanced model, ablation, error analysis.
  • ·Weeks 9-10: Writing, polishing, thesis formatting.

Final Advice: Your thesis value comes from thoughtful application and clear analysis, not just model accuracy. A well-executed Item-Item CF with insightful error analysis often scores higher than a messy SOTA attempt. Prioritize clarity, rigor, and connecting every result back to your research question. Good luck!