3.9 Learning curves for l-pref users comparing a single iter ation of gradient descent to relevance feedback 70 5.1 The Fab interface 5.2 Hypot hetical system design: single user, many collection agents 5.3 Hypot hetical system design: many users, single collect ion agent 5.4 Model underlying system design: many-to-many mapping between users and topics 5.5 Selection and collection agents 5.6 Select ion agents and collection agents lin ked via central router 5.7 Select ion agents and collection agents showing explicit collabor ation links ts of implemented Fab archit 5.9 Dist ance between actual and predicted ran kings, aver aged at each eval u ation doint 110 5.10 For each source, dist ance bet ween user rankings and its ideal ranking averaged over all users at each evaluat ion point 111 6.1 The three positions of the exploration/exploit ation select or, as seen in 119 6.2 Exploit ation vs. explor ation do cument selection strategies for 1-pref users. Graph shows ndpm values averaged over all 10 possible users, 1 measured at test iterations(every fift h iteration 6.3 Composition of documents in recommen ded sets for 1-pref 100% exploit at ion and 100% explor ation strategies. Recor ded for every train ing step, and averaged over all 10 possible user3.9 Learning curves for 1-pref users comparing a single iteration of gradient descent to relevance feedback. ...................... 70 5.1 The Fab interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 5.2 Hypothetical system design: single user, many collection agents ... 83 5.3 Hypothetical system design: many users, single collection agent ... 84 5.4 Model underlying system design: many-to-many mapping between users and topics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 5.5 Selection and collection agents . . . . . . . . . . . . . . . . . . . . . . 86 5.6 Selection agents and collection agents linked via central router . . . . 87 5.7 Selection agents and collection agents showing explicit collaboration links .................................... 96 5.8 Components of implemented Fab architecture ............. 100 5.9 Distance between actual and predicted rankings, averaged at each eval￾uation point. ............................... 110 5.10 For each source, distance between user rankings and its ideal ranking, averaged over all users at each evaluation point. . . . . . . . . . . . . 111 6.1 The three positions of the exploration/exploitation selector, as seen in the user interface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 6.2 Exploitation vs. exploration document selection strategies for 1-pref users. Graph shows ndpm values averaged over all 10 possible users, measured at test iterations (every fth iteration). ........... 121 6.3 Composition of documents in recommended sets for 1-pref users: 100% exploitation and 100% exploration strategies. Recorded for every train￾ing step, and averaged over all 10 possible users. . . . . . . . . . . . . 122 xvi