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P. Adamopoulos, A. Bellogin, P. Castells, P. Cremonesi, and H. Steck. REDD 2014 – International Workshop on Recommender Systems Evaluation: Dimensions and Design. Held in conjunction with ACM Conference on Recommender systems, 2014.
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P. Campos, F. Diez, and I. Cantador. Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols. User Modeling and User-Adapted Interaction, 24(1–2), pp. 67–119, 2014. CrossRef
O. Celma and P. Herrera. A new approach to evaluating novel recommendations. ACM Conference on Recommender Systems, pp. 179–186, 2008.
T. Chai and R. Draxler. Root mean square error (RMSE) or mean absolute error (MAE)?– Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), pp. 1247–1250, 2004., CrossRef
P. Chirita, W. Nejdl, and C. Zamfir. Preventing shilling attacks in online recommender systems. ACM International Workshop on Web Information and Data Management, pp. 67–74, 2005.
H. Cramer, V. Evers, S. Ramlal, M. Someren, L. Rutledge, N. Stash, L. Aroyo, and B. Wielinga. The effects of transparency on trust in and acceptance of a content-based art recommender. User Modeling and User-Adapted Interaction, 18(5), pp. 455–496, 2008.
P. Cremonesi, Y. Koren, and R. Turrin. Performance of recommender algorithms on top- n recommendation tasks. RecSys, pp. 39–46, 2010.
A. Das, M. Datar, A. Garg, and S. Rajaram. Google news personalization: scalable online collaborative filtering. World Wide Web Conference, pp. 271–280, 2007.
M. Deshpande and G. Karypis. Item-based top- n recommendation algorithms. ACM Transactions on Information Systems (TOIS), 22(1), pp. 143–177, 2004. CrossRef
R. Devooght, N. Kourtellis, and A. Mantrach. Dynamic matrix factorization with priors on unknown values. ACM KDD Conference, 2015.
T. Fawcett. ROC Graphs: Notes and Practical Considerations for Researchers. Technical Report HPL-2003-4, Palo Alto, CA, HP Laboratories, 2003.
D. M. Fleder and K. Hosanagar. Recommender systems and their impact on sales diversity. ACM Conference on Electronic Commerce, pp. 192–199, 2007.
M. Ge, C. Delgado-Battenfeld, and D. Jannach. Beyond accuracy: evaluating recommender systems by coverage and serendipity. ACM Conference on Recommender Systems, pp. 257–260, 2010.
J. Herlocker, J. Konstan, L. Terveen, and J. Riedl. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), 22(1), pp. 5–53, 2004. CrossRef
J. Herlocker, J. Konstan, and J. Riedl. Explaining collaborative filtering recommendations. ACM Conference on Computer Supported Cooperative work, pp. 241–250, 2000.
D. Jannach, M. Zanker, A. Felfernig, and G. Friedrich. An introduction to recommender systems, Cambridge University Press, 2011.
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J. Konstan, S. McNee, C. Ziegler, R. Torres, N. Kapoor, and J. Riedl. Lessons on applying automated recommender systems to information-seeking tasks. AAAI Conference, pp. 1630–1633, 2006.
Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. ACM KDD Conference, pp. 426–434, 2008. Extended version of this paper appears as: “Y. Koren. Factor in the neighbors: Scalable and accurate collaborative filtering. ACM Transactions on Knowledge Discovery from Data (TKDD), 4(1), 1, 2010.”
Y. Koren. The Bellkor solution to the Netflix grand prize. Netflix prize documentation, 81, 2009. http://www.netflixprize.com/assets/GrandPrize2009_BPC_BellKor.pdf
V. Krishnan, P. Narayanashetty, M. Nathan, R. Davies, and J. Konstan. Who predicts better? Results from an online study comparing humans and an online recommender system. ACM Conference on Recommender Systems, pp. 211–218, 2008.
S. Lam and J. Riedl. Shilling recommender systems for fun and profit. World Wide Web Conference, pp. 393–402, 2004.
N. Lathia, S. Hailes, L. Capra, and X. Amatriain. Temporal diversity in recommender systems. ACM SIGIR Conference, pp. 210–217, 2010.
B.-H. Lee, H. Kim, J. Jung, and G.-S. Jo. Location-based service with context data for a restaurant recommendation. Database and Expert Systems Applications, pp. 430–438, 2006.
L. Li, W. Chu, J. Langford, and X. Wang. Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms. International Conference on Web Search and Data Mining, pp. 297–306, 2011.
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Z. Ma, G. Pant, and O. Sheng. Interest-based personalized search. ACM Transactions on Information Systems, 25(1), 2007.
T. Mahmood and F. Ricci. Learning and adaptivity in interactive recommender systems. International Conference on Electronic Commerce, pp. 75–84, 2007.
T. Mahmood and F. Ricci. Improving recommender systems with adaptive conversational strategies. ACM Conference on Hypertext and Hypermedia, pp. 73–82, 2009.
M. O’Mahony, N. Hurley, N. Kushmerick, and G. Silvestre. Collaborative recommendation: A robustness analysis. ACM Transactions on Internet Technology, 4(4), pp. 344–377, 2004. CrossRef
B. Marlin and R. Zemel. Collaborative prediction and ranking with non-random missing data. ACM Conference on Recommender Systems, pp. 5–12, 2009.
S. McNee, J. Riedl, and J. Konstan. Being accurate is not enough: how accuracy metrics have hurt recommender systems. SIGCHI Conference, pp. 1097–1101, 2006.
S. Middleton, N. Shadbolt, and D. de Roure. Ontological user profiling in recommender systems. ACM Transactions on Information Systems, 22(1), pp. 54–88, 2004. CrossRef
B. Mobasher, R. Burke, R. Bhaumik, and C. Williams. Toward trustworthy recommender systems: an analysis of attack models and algorithm robustness. ACM Transactions on Internet Technology (TOIT), 7(4), 23, 2007.
T. Murakami, K. Mori, and R. Orihara. Metrics for evaluating the serendipity of recommendation lists. New Frontiers in Artificial Intelligence, pp. 40–46, 2008.
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B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Incremental singular value decomposition algorithms for highly scalable recommender systems. International Conference on Computer and Information Science, pp. 27–28, 2002.
B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Recommender systems for large-scale e-commerce: Scalable neighborhood formation using clustering. International Conference on Computer and Information Technology, 2002.
A. Schein, A. Popescul, L. Ungar, and D. Pennock. Methods and metrics for cold-start recommendations. ACM SIGIR Conference, 2002.
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G. Shani, M. Chickering, and C. Meek. Mining recommendations from the Web. ACM Conference on Recommender Systems, pp. 35–42, 2008.
J. Sill, G. Takacs, L. Mackey, and D. Lin. Feature-weighted linear stacking. arXiv preprint, arXiv:0911.0460, 2009. http://arxiv.org/pdf/0911.0460.pdf
B. Smyth and P. McClave. Similarity vs. diversity. Case-Based Reasoning Research and Development, pp. 347–361, 2001.
H. Steck. Item popularity and recommendation accuracy. ACM Conference on Recommender Systems, pp. 125–132, 2011.
H. Steck. Training and testing of recommender systems on data missing not at random. ACM KDD Conference, pp. 713–722, 2010.
H. Steck. Evaluation of recommendations: rating-prediction and ranking. ACM Conference on Recommender Systems, pp. 213–220, 2013.
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N. Taghipour, A. Kardan, and S. Ghidary. Usage-based web recommendations: a reinforcement learning approach. ACM Conference on Recommender Systems, pp. 113–120, 2007.
G. Takacs, I. Pilaszy, B. Nemeth, and D. Tikk. Scalable collaborative filtering approaches for large recommender systems. Journal of Machine Learning Research, 10, pp. 623–656, 2009.
C. Willmott and K. Matsuura. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79, 2005.
Y. Zhang, J. Callan, and T. Minka. Novelty and redundancy detection in adaptive filtering. ACM SIGIR Conference, pp. 81–88, 2002.
C. Ziegler, S. McNee, J. Konstan, and G. Lausen. Improving recommendation lists through topic diversification. World Wide Web Conference, pp. 22–32, 2005.
- Evaluating Recommender Systems
Charu C. Aggarwal
- Chapter 7
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