Summary of “Evaluation Collaborative Filtering Recommender Systems”

The following is a short summary of:
J. Herlocker, J. Konstan, L. Terveen, and J. Riedl. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), 22(1):5--53, 2004.

Commonly observed user tasks from recommender systems are as follow:
-. Annotation in context : Uses a recommender in an existing context. E.g., Overlay prediction information on top of existing links
-. Find good items : Video recommender, Item recommender, etc
-. Find All Good Items : purpose is to lower false negative rate
-. Recommend sequence : can be interesting
-. Find credible recommender : making the system more credible

Ratings can be explicit (users rate directly) or implicit (inferred from users behavior or preferences)

Commonly used evaluation metrics are:
1. Predictive accuracy
-. MAE (Mean Absolute Error) = (\sum (p_i – r_i))/N
-. MSE (Mean Square Error) = (\sum (p_i – r_i)^2)/N
-. RMSE (Root Mean Square Error) = \sqrt (MSE)
2. Classification accuracy
-. Precision : the ratio of relevant items selected to number of items selected
-. Recall : the ratio of relevant items selected to total number of relevant items available (Note: Precision and recall are inversely related.)
-. MAP(Mean Average Precision, aka F1) = 2PR/(P+R)