is t o f 2.1 A simple model for a recommen der system 3.1 Basic system with a single user and a single agent(shown split into a collection and a selection phase) 3. 2 The liRa interface 3.3 First five entries from a sample top-ten list produced by a students 3.4 Results of an experiment where only music-rel ated pages where rated highly 3.5 Com parison of the LIRA system against random and human-selected “cool”) pages 64 3.6 Variation of ndpm value after 95 iterations of the recommender system with differing numbers of words used from each document, for I-pref users. Error bars show 95% confiden ce intervals 3.7 Gradient des cent on test set (test of optimal learning), in each case averaged among all possible users with the same preference structure or 500 randomly generated users, whichever is the lesser 3.8 Comparison of ndpm dist ances using different numbers of steps of the gradient descent algorithm, after different numbers of iterations of the recommender system, for 1-pref users 95% confidence intervals shown. 69List of Figures 2.1 A simple model for a recommender system. . . . . . . . . . . . . . . . 38 3.1 Basic system with a single user and a single agent (shown split into a collection and a selection phase). .................... 46 3.2 The LIRA interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.3 First ve entries from a sample top-ten list produced by a student's program. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 3.4 Results of an experiment where only music-related pages where rated highly. ................................... 61 3.5 Comparison of the LIRA system against random and human-selected (\cool") pages. .............................. 64 3.6 Variation of ndpm value after 95 iterations of the recommender system with diering numbers of words used from each document, for 1-pref users. Error bars show 95% condence intervals. . . . . . . . . . . . . 65 3.7 Gradient descent on test set (test of optimal learning), in each case averaged among all possible users with the same preference structure, or 500 randomly generated users, whichever is the lesser. ....... 67 3.8 Comparison of ndpm distances using dierent numbers of steps of the gradient descent algorithm, after dierent numbers of iterations of the recommender system, for 1-pref users. 95% condence intervals shown. 69 xv