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Online Learning with Implicit User Preferences
Published on Jan 24, 20124570 Views
Many systems, ranging from search engines to smart homes, aim to continually improve the utility they are providing to their users. While clearly a machine learning problem, it is less clear what the
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Chapter list
Online Learning with Implicit User Preferences00:00
Today‟s Knowledge-Based Systems00:46
Learning for Search Engines01:51
Overview02:56
The Most Basic Learning Problem04:57
Approaches to Implicit Utility Elicitation05:41
How does User Behavior Reflect Retrieval Quality? -106:57
How does User Behavior Reflect Retrieval Quality? -208:14
Absolute Metrics: Metrics09:09
Absolute Metrics: Results - 110:13
Absolute Metrics: Results - 211:45
Approaches to Utility Elicitation12:05
Paired Comparisons: What to Measure?12:46
Paired Comparisons: Balanced Interleaving13:20
Balanced Interleaving: Results -115:32
Balanced Interleaving: Results -218:30
Problem: Learning on Operational System18:42
Regret for the Dueling Bandits Problem21:01
First Thought: Tournament22:44
Algorithm: Interleaved Filter 223:51
Assumptions -125:46
Assumptions -226:15
Lower Bound26:58
Dueling Bandits: Infinite K -127:02
Dueling Bandits: Infinite K -227:41
Experiment: Web Search28:05
Overview28:34
Who does the exploring? -129:45
Who does the exploring? -230:34
Who does the exploring? -330:49
Strategy for Within-Ranking Feedback31:09
User Study31:45
Strategy for Within-Ranking Feedback32:40
Strategy for Between-Ranking Feedback -133:44
Strategy for Between-Ranking Feedback -233:53
Does Accuracy Depend on Quality?34:48
Preference Online Learning Model34:51
Preference Perceptron Algorithm37:04
Preference Perceptron Regret Bound38:55
Experiment: Web Search41:17
Conclusions42:20