Wrap-Up: Research Roadmap & Resources

Where to Go From Here

What We Covered Today

A complete journey through modern recommendation systems

1. Foundations: Matrix Factorization & EASE
2. Sequential Models: Transformers & SASRec
3. LLM Integration: Zero-shot & metadata generation
4. Generative Systems: Page generation

Core Concepts Recap

Embeddings - Items and users in latent space - Foundation for all modern methods

Sequences - Temporal patterns matter - Self-attention captures dependencies

Generation - From ranking → creating experiences - Autoregressive paradigm

Skills You’ve Gained

✓ Implement classical CF methods (MF, EASE)

✓ Build transformer-based sequential models (SASRec)

✓ Apply LLMs to recommendation tasks

✓ Design generative recommendation systems

✓ Evaluate multi-objective recommendation quality

Research Roadmap

Where should you explore next?

Track 1: Classical Methods (Deepen)

Core Papers:

• Koren et al. (2009)
  • The definitive MF reference
• Steck (2019)
  • EASE: Simple but powerful
• He et al. (2017)
  • Neural extension of MF

Advanced Topics: - Factorization Machines - Bayesian Personalized Ranking (BPR)

Track 2: Sequential Recommendation

Transformer-Based:

• Kang & McAuley (2018)
  • SASRec - today’s focus
• Sun et al. (2019)
  • Bidirectional variant
• J. Li et al. (2020)
  • Temporal modeling

RNN-Based (Historical): - Hidasi et al. (2016)

Track 3: LLMs for Recommendation

Prompting & Zero-Shot:

• Hou et al. (2023)

• Geng et al. (2022)

  • Unified prompt-based framework

Collaborative LLMs:

• Zhang et al. (2023)

Conversational:

• Zhou et al. (2020)

Track 4: Generative Recommendation

Slate/List Generation:

• Ie et al. (2019)

• Zhan et al. (2021)

Layout Generation:

• Y. Li et al. (2020)
  • Inspiration for page generation

Your Research: - (Cite your own generative page papers here!)

Track 5: Evaluation & Deployment

Offline Metrics:

• Gunawardana & Shani (2009)

Online Evaluation:

• L. Li et al. (2010)

A/B Testing:

• Kohavi et al. (2007)

Foundation Papers (Must-Read)

Transformers: - Vaswani et al. (2017)

Embeddings: - Mikolov et al. (2013) - Word2Vec

Neural RecSys: - Covington et al. (2016)

Tools & Libraries

Classical RecSys: - Surprise - Python scikit for recommendation - LightFM - Hybrid recommendation

Neural RecSys: - RecBole - Unified deep learning framework - Transformers4Rec - NVIDIA’s library

LLM Tools: - LangChain - LLM application framework - LlamaIndex - Data framework for LLMs

Approximate Nearest Neighbors: - FAISS - Facebook’s vector search - Annoy - Spotify’s library

Datasets for Practice

MovieLens (used today) - 100K to 25M ratings - grouplens.org/datasets/movielens/

Others:

• Amazon Reviews - 233M reviews across categories
• Yelp Dataset - Business reviews & check-ins
• Last.fm - Music listening data
• Netflix Prize (historical) - Movie ratings
• Spotify Million Playlist - Music playlists

Advanced Notebooks (Take-Home)

Available in the workshop repository:

1. Bert4Rec (notebooks/advanced_01_bert4rec.qmd) - Bidirectional sequential modeling - Masked item prediction

2. ANN Retrieval (notebooks/advanced_02_ann_retrieval.qmd) - FAISS for large-scale retrieval - Two-tower models

3. Page Generation Prototype (notebooks/advanced_03_page_generation.qmd) - Tile sequence modeling - Multi-objective optimization

Community & Conferences

Conferences:

• RecSys - ACM Conference on Recommender Systems (annual)
• WSDM - Web Search and Data Mining
• KDD - Knowledge Discovery and Data Mining
• WWW - The Web Conference

Workshops:

• REVEAL (RecSys + Deep Learning)
• OHARS (Online and Adaptive Recommender Systems)

Online Communities:

• Papers With Code - Recommendation
• RecSys Subreddit
• Twitter/X RecSys community (#RecSys)

Open Research Questions

Where can you contribute?

1. Better offline→online alignment: Metrics that predict real engagement

2. Efficient transformers: Real-time sequential models for billion-user scale

3. LLM hallucination in RecSys: Ensuring factual recommendations

4. Fair & diverse generation: Avoiding filter bubbles in generative systems

5. Multimodal recommendation: Text + images + video + audio

6. Explainable generative systems: Why was this page generated?

Practical Next Steps

This Week:

1. Complete the advanced notebooks
2. Read 2-3 cornerstone papers
3. Experiment with your own dataset

This Month:

1. Implement a hybrid system (CF + LLM)
2. Benchmark on public datasets
3. Attend a RecSys meetup/webinar

This Year:

1. Publish a paper or blog post
2. Contribute to open-source RecSys libraries
3. Deploy a recommendation system in production

Thank You!

Questions?

Stay in Touch:

• Workshop materials: github.com/jankislinger/recsys-genai
• Email: Contact via GitHub issues
• Questions? Open an issue or discussion

Remember: The future of recommendation is generative!

From ranking items → generating experiences 🚀

Bibliography (Selected)

Classic: - Resnick et al. (1994) - Sarwar et al. (2001) - Koren et al. (2009)

Neural: - Covington et al. (2016) - He et al. (2017)

Sequential: - Kang & McAuley (2018) - Sun et al. (2019)

LLM: - Geng et al. (2022) - Hou et al. (2023)

Full bibliography available on the References page

Final Thought

“The best recommendation system is one that feels like magic to the user.”

Generative systems get us closer to that magic. ✨

Now go build! 🛠️

Covington, P., Adams, J., & Sargin, E. (2016). Deep neural networks for YouTube recommendations. Proceedings of the 10th ACM Conference on Recommender Systems, 191–198. https://doi.org/10.1145/2959100.2959190

Geng, S., Liu, S., Fu, Z., Ge, Y., & Zhang, Y. (2022). Recommendation as language processing (P5): A unified pretrain, personalized prompt & predict paradigm. Proceedings of the 16th ACM Conference on Recommender Systems, 299–315. https://doi.org/10.1145/3523227.3546767

Gunawardana, A., & Shani, G. (2009). A survey of accuracy evaluation metrics of recommendation tasks. Journal of Machine Learning Research, 10(12), 2935–2962. http://www.jmlr.org/papers/v10/gunawardana09a.html

He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T.-S. (2017). Neural collaborative filtering. Proceedings of the 26th International Conference on World Wide Web, 173–182. https://doi.org/10.1145/3038912.3052569

Hidasi, B., Karatzoglou, A., Baltrunas, L., & Tikk, D. (2016). Session-based recommendations with recurrent neural networks. International Conference on Learning Representations. https://arxiv.org/abs/1511.06939

Hou, Y., Zhang, J., Lin, Z., Lu, H., Xie, R., McAuley, J., & Zhao, W. X. (2023). Large language models are zero-shot rankers for recommender systems. arXiv Preprint arXiv:2305.08845. https://arxiv.org/abs/2305.08845

Ie, E., Hsu, C., Mladenov, M., Jain, V., Narvekar, S., Cheng, J., Choi, H.-T., & Boutilier, C. (2019). Reinforcement learning for slate-based recommender systems: A tractable decomposition and practical methodology. arXiv Preprint arXiv:1905.12767. https://arxiv.org/abs/1905.12767

Kang, W.-C., & McAuley, J. (2018). Self-attentive sequential recommendation. 2018 IEEE International Conference on Data Mining (ICDM), 197–206. https://doi.org/10.1109/ICDM.2018.00035

Kohavi, R., Longbotham, R., Sommerfield, D., & Henne, R. M. (2007). Controlled experiments on the web: Survey and practical guide. Data Mining and Knowledge Discovery, 18(1), 140–181. https://doi.org/10.1007/s10618-008-0114-1

Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30–37. https://doi.org/10.1109/MC.2009.263

Li, J., Wang, Y., & McAuley, J. (2020). Time interval aware self-attention for sequential recommendation. Proceedings of the 13th International Conference on Web Search and Data Mining, 322–330. https://doi.org/10.1145/3336191.3371786

Li, L., Kim, J. Y., & Zitouni, I. (2010). Offline evaluation to make decisions about PlaylistDJ recommendation algorithms. Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, 420–429.

Li, Y., Qian, Y., Yu, Y., Qin, X., Zhang, C., Liu, Y., Yao, K., Han, J., Liu, J., & Ding, E. (2020). LayoutLM: Pre-training of text and layout for document understanding. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 1192–1200. https://doi.org/10.1145/3394486.3403172

Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems, 26. https://proceedings.neurips.cc/paper/2013/hash/9aa42b31882ec039965f3c4923ce901b-Abstract.html

Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994). GroupLens: An open architecture for collaborative filtering of netnews. Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, 175–186. https://doi.org/10.1145/192844.192905

Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. Proceedings of the 10th International Conference on World Wide Web, 285–295. https://doi.org/10.1145/371920.372071

Steck, H. (2019). Embarrassingly shallow autoencoders for sparse data. The World Wide Web Conference, 3251–3257. https://doi.org/10.1145/3308558.3313710

Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., & Jiang, P. (2019). BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 1441–1450. https://doi.org/10.1145/3357384.3357895

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30. https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html

Zhan, R., Xie, R., Zhao, X., et al. (2021). Towards content provider aware recommender systems: A simulation study on the interplay between user and provider utilities. Proceedings of the Web Conference 2021, 3872–3883. https://doi.org/10.1145/3442381.3449889

Zhang, Y., Hou, Y., Zhao, W. X., et al. (2023). Collaborative large language model for recommender systems. arXiv Preprint arXiv:2311.01343. https://arxiv.org/abs/2311.01343

Zhou, K., Zhou, Y., Zhao, W. X., Wang, X., & Wen, J.-R. (2020). A survey on conversational recommender systems. ACM Computing Surveys, 54(4), 1–36. https://doi.org/10.1145/3453154