To prepare for the onsite interview with XXXX, I read bunches of papers about recommendation system these days. And some of them are really nice, I will keep track of it and add a really briefly comment for that paper, descriptive or detailed.

  1. Breese, J. S., Heckerman, D., & Kadie, C. (1998, July). Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence (pp. 43-52). Morgan Kaufmann Publishers Inc..

    • From Microsoft Research. It is an introduction to collaborative filtering, and evaluated the data set we use.

  2. Herlocker, J. L., Konstan, J. A., Borchers, A., & Riedl, J. (1999, August). An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval (pp. 230-237). ACM.

    • Proposes a framework for collaborative filtering. We will implement specific algorithms following this framework.

  3. Jeh, G., & Widom, J. (2002, July). SimRank: a measure of structural-context similarity. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 538-543). ACM.

    • Proposes a specific similarity measure.

  4. Schein, A. I., Popescul, A., Ungar, L. H., & Pennock, D. M. (2002, August). Methods and metrics for cold-start recommendations. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval(pp. 253-260). ACM.

    • FYI on cold-start problems.

  5. Su, X., & Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques. Advances in artificial intelligence, 2009, 4.

    • A review paper of collaborative filtering.

Notes those five papers above are the reading materials of Applied Data Science Course reading materials listed by Prof. Zheng.

  1. Building industrial-scale Real-World recommender systems. 2012, by Xavier from Netflix

    • It’s talked about the concerns for RS except for the RMSE and netflix solution in really really high-level, no so much detail, this two-page paper can give you the intuition

  2. Deep Nerual Networks for YouTube recommendations, 2016

    • Nice paper, worthy of reading and taking notes of it, as this is the first paper I read about applying DL to RS. The content can be hard to understand if no fundamental understanding of NN, embedding.

  3. Large scale distributed deep networks

    • remarkable paper.

  4. Collaborative Filtering for Implicit Feedback Datasets

    • implicit feedback, which is a really common scenario for RS systems, like youtube platform.

  5. A Reinforcement Learning Framework for Explainable Recommendation

  6. Neural collaborative filtering

  7. RippleNet- Propagating User Preferences on the Knowledge Graph for Recommender Systems

  8. RN- A Deep Reinforcement Learning Framework for News Recommendation

  9. xDeepFM- Combining Explicit and Implicit Feature Interactions for Recommender Systems

I will add more as I continues to prepare for the interview and to know more about the state-of-art techs in this field as much as I can.