Google Scholar makes a better job at keeping this list up to date. You can also find me on ACM DL, and DBLP.

Journal Papers

  1. A Short History of the RecSys Challenge. A. Said. 2016. AI Magazine, Vol.36, NO.4, 2016.
  2. RiVal - A New Benchmarking Toolkit For Recommender Systems. A. Said, A. Bellogín. 2014. ERCIM News, Vol.99, 2014.
  3. Movie Recommendation in Context. A. Said, S. Berkovsky, E. W. De Luca. 2013. ACM Trans. Intell. Syst. Technol. (TIST), Vol.4, NO.1, 2013

Conference Publications

  1. User Nutrition Modelling and Recommendation - Balancing Simplicity and Complexity. H. Schaefer, M. Elahi, D. Elsweiler, G. Groh, M. Harvey, B. Ludwig, F. Ricci, A. Said. 2017. ACM UMAP
  2. Towards Health (Aware) Recommender Systems. H. Schaefer, S. Hors-Fraile, R. Karumur, A. Calero Valdez, and A. Said, H. Torkamaan, T. Ulmer, and C. Trattner. 2017. ACM Digital Health
  3. Predicting Cyber Vulnerability Exploits with Machine Learning. M. Edkrantz, and A. Said. 2015. SCAI 2015: 48-57
  4. Predicting Vulnerability Exploits in the Wild. M. Edkrantz, S. Truvé, and A. Said. 2015. CSCloud 2015: 513-514
  5. Comparative Recommender System Evaluation: Benchmarking Recommendation Frameworks. A. Said, A. Bellogín. 2014. ACM RecSys 2014: 129-136
  6. RiVal – A Toolkit to Foster Reproducibility in Recommender System Evaluation. A. Said, A. Bellogín. 2014. RecSys 2014.
  7. `Free Lunch’ Enhancement for Collaborative Filtering with Factorization Machines. B. Loni, M. Larson, A. Said, Alan Hanjalic. 2014. ACM RecSys 2014: 281-284.
  8. WrapRec: An Easy Extension of Recommender System Libraries. B. Loni, A. Said. 2014. RecSys 2014.
  9. An Extended Data Model Format for Composite Recommendation. A. Said, B. Loni, R. Turrin, A. Lommatzsch. 2014. RecSys 2014.
  10. Comparative evaluation of recommender systems for digital media. D. Tikk, R. Turrin, M. Larson, D. Zibriczky, D. Malagolo, A. Said, A. Lommatzsch, V. Gál, S. Székely. 2014. IBC 2014.
  11. The Magic Barrier of Recommender Systems - No Magic, Just Ratings. A. Bellogín, A. Said, A. P. De Vries. 2014. UMAP 2014
  12. Do Recommendations Matter? – News Recommendation IRL. A. Said, A. Bellogín, J. Lin, A. de Vries. 2014. CSCW 2014.
  13. Information Retrieval and User-Centric Recommender System Evaluation. A. Said, A. Bellogín, A. de Vries, B. Kille.
  14. User-Centric Evaluation of a K-Furthest Neighbor Collaborative Filtering Recommender Algorithm. A. Said, B. Fields, B. J. Jain. 2013. ACM CSCW 2013.
  15. Correlating Perception-Oriented Aspects in User-Centric Recommender System Evaluation. A. Said, B. J. Jain, A. Lommatzsch, S. Albayrak. 2012. ACM IIiX 2012. Best Poster Nomination
  16. A 3D Approach to Recommender System Evaluation. A. Said, B. J. Jain. 2013. CSCW 2013.
  17. Users and Noise: The Magic Barrier of Recommender Systems. A. Said, B. J. Jain, S. Narr, T. Plumbaum. 2012. UMAP 2012. Springer Best Paper Award
  18. Estimating the Magic Barrier of Recommender Systems: A User Study. A. Said, B. J. Jain, S. Narr, T. Plumbaum, S. Albayrak, Christian Scheel. 2012. ACM SIGIR 2012.
  19. Analyzing Weighting Schemes in Collaborative Filtering: Cold Start, Post Cold Start and Power Users. A. Said, B. J. Jain. 2012. ACM SAC 2012.
  20. KMulE: A Framework for User-based Comparison of Recommender Algorithms. A. Said, E. W. De Luca, B. Kille, B. J. Jain, I. Micus, S. Albayrak. 2012. ACM IUI 2012.
  21. Identifying and Utilizing Contextual Data in Hybrid Recommender Systems. A. Said. 2010. RecSys 2010.
  22. A Comparison of How Demographic Data Affects Recommendation. A. Said, T. Plumbaum, E. W. De Luca, S. Albayrak. 2011. UMAP 2011.

Workshop Publications

  1. A Billiard Metaphor for Exploring Complex Graphs. E. Ventocilla, J. Bae, M. Riveiro, A. Said. 2017. Supporting Complex Search Tasks 2017.
  2. Recommending Recipes for Balanced Nutrition. D. Elsweiler, M. Harvey, A. Said, B. Ludwig. 2015. Beyond Health 2015.
  3. Bringing the “healthy” into Foor Recommenders D. Elsweiler, M. Harvey, B. Ludwig, A. Said. 2015. DMRS 2015: 33-36
  4. You Are What You Eat! Tracking Health Through Recipe Interactions. A. Said, A. Bellogín. 2014. RSWeb 2014.
  5. A Top-N Recommender System Evaluation Protocol Inspired by Deployed Systems. A. Said, A. Bellogín, A. P. De Vries. 2013. LSRS 2013. pdf
  6. Activating the Crowd: Exploiting User-Item Reciprocity for Recommendation. M. Larson, A. Said, Y. Shi, P. Cremonesi, D. Tikk, A. Karatzoglou. 2013. CrowdRec 2013.
  7. A Month in the Life of a Production News Recommender System. A. Said, J. Lin, A. Bellogín, A. P. De Vries. 2013. LivingLab 2013.
  8. Recommender Systems Evaluation: A 3D Benchmark. A. Said, D. Tikk, K. Stumpf, Y. Shi, M. Larson, P. Cremonesi. 2012. RUE 2012.
  9. Semantic Preference Retrieval for Querying Knowledge Bases. C. Scheel, A. Said, S. Albayrak. 2012. JIWES 2012.
  10. Increasing Diversity Through Furthest Neighbor-Based Recommendation. A. Said, B. J. Jain, B. Kile, S. Albayrak. 2012. DDR 2012.
  11. Group Recommendation in Context. A. Said, S. Berkovsky, E. W. De Luca. 2011. CAMRa 2011.
  12. Personalizing Tags: A Folksonomy-like Approach for Recommending Movies. A. Said, E. W. De Luca. 2011. HetRec 2011.
  13. Inferring Contextual User Profiles - Improving Recommender Performance. A. Said, E. W. De Luca. 2011. CARS 2011.
  14. Using Social and Pseudo Social Networks for Improved Recommendation Quality. A. Said, E. W. De Luca, S. Albayrak. 2011. ITWP 2011.
  15. Putting things in context. A. Said, S. Berkovsky, E. W. De Luca. 2010. CAMRa 2010.
  16. Exploiting hierarchical tags for context-awareness. A. Said, J. Kunegis, E. W. De Luca. 2010. ESAIR 2010.
  17. How social relationships affect user similarities. A. Said, E. W. De Luca. 2010. SRS 2010.
  18. Understanding the user: Personomy translation for tag-recommendation. R. Wetzker, A. Said, C. Zimmermann. 2009. ECML-PKDD DC 2009.
  19. A Hybrid PLSA Approach for Warmer Cold Start in Folksonomy Recommendation. A. Said, R. Wetzker, W. Umbrath, L. Hennig. 2009. RSWeb 2009.
  20. A hybrid approach to item recommendation in folksonomies. R. Wetzker, W. Umbrath, A. Said. 2009. ESAIR 2009.

Edited Volumes

  1. Proceedings of the ACM RecSys Challenge 2014. A. Said, S. Dooms, B. Loni, D. Tikk. 2014. ACM RecSys.
  2. Proceedings of the 4th Workshop on Context-awareness in Retrieval and Recommendation. A. Said, E. W. De Luca, D. Quercia, M. Böhmer. 2014. ECIR.
  3. Proceedings of the Workshop on reproducibility and replication in recommender systems. A. Bellogín, P. Castells, A. Said, D. Tikk. 2013. ACM RecSys.
  4. Proceedings of the 3rd Workshop on Context-awareness in Retrieval and Recommendation. M. Böhmer, E. W. De Luca, A. Said, J. Teevan. 2013. ACM WSDM.
  5. Proceedings of the 2nd Workshop on Context-awareness in Retrieval and Recommendation. E. W. De Luca, M. Böhmer, A. Said, E. Chi. 2012. ACM IUI.
  6. Proceedings of the 2011 Challenge on Context-aware Movie Recommendation. A. Said, E. W. De Luca, S. Berkovsky, J. Hermanns. 2011. ACM RecSys.
  7. Proceedings of the Workshop on Context-awareness in Retrieval and Recommendation. E. W. De Luca, M. Böhmer, A. Said, F. Michahelles. 2011. ACM IUI.
  8. Proceedings of the 2010 Challenge on Context-aware Movie Recommendation. A. Said, E. W. De Luca, S. Berkovsky, J. Hermanns. 2011. ACM RecSys.

Books, book chapters, and theses

  1. Benchmarking: A Methodology for Ensuring the Relative Quality of a Recommendation System for Software Engineering. A. Said, D. Tikk, P. Cremonesi. Book chapter: Recommendation Systems in Software Engineering, eds. M.P. Robillard, W. Maalej, R.J. Walker, T. Zimmerman. Springer. 2014, Berlin, Germany
  2. Evaluating the Accuracy and Utility of Recommender Systems. A. Said. Thesis for the degree Doktor der Ingenieurwissenschaften. TU Berlin, 2013, Berlin, Germany

Abstracts

  1. Tutorial on Replicable Evaluation of Recommender Systems. A. Said, A. Bellogín. 2015. ACM RecSys.
  2. Tutorial on The Challenge of Recommender Systems Challenges. A. Said, D. Tikk, A. Hotho. 2012. ACM RecSys

Other Publications

  1. The Not-so-Magic Magic Barrier of Recommender Systems. A. Bellogín, and A. Said. 2015. TinyToCS 3.
  2. Non-transparent recommender system evaluation leads to misleading results. A. Said, and A. Bellogín. 2015. TinyToCS 3