Katja Hofmann

Learning to InteractTowards “Self-learning” Search Solutions Presenting work by various authors,

and own work in collaboration with colleagues at Microsoft and the University of Amsterdam

@katjahofmann

Motivation

Example task:

Find best news articles based on user context; optimize click-through rate

Example task:

Tune ad display parameters (e.g., mainline reserve) to optimize revenue

Example task:

Improve ranking of QAC to optimize suggestion usage

Typical approach: lots of offline tuning + AB testing.

AB Testing= controlled experiment (often at

large scale) with (at least) 2 conditions

[Kohavi et al. ’09, ‘12]

Example: which search interface results in higher revenue?

Limitations of AB testingHigh manual effortNeed to carefully design / tune each treatment

Few tested alternativesTypically compare 2-5 options

Large required sample sizeDepending on effect size and variance, thousands to millions of impressions required to detect statistically significant differences

Result: slow development cycles (e.g., weeks)

Can any of this be automated to speed up innovation?

Towards “Self-learning” Search SolutionsContextual Bandits

Counterfactual Reasoning

Online Learning to Rank

Image adapted from: https://www.flickr.com/photos/prayitnophotography/4464000634

Contextual bandits

Why bandits?Interactive systems only observe user feedback (reward) on the items (actions) they present to their users.

Exploration – exploitation trade-off

Formalized as (contextual) bandit problem

submit query, interact with result lists

generate resultsinterpret feedback

Bandits

Address key challenge: how to balance exploration and exploitation – explore to learn, exploit to benefit from what has been learned.

= Reinforcement learning problem where actions do not affect future states

BanditsExample

Successes so far:100 50 10Arm pulls so far: ?? ?? ??

A B C

BanditsExample

Successes so far:100 50 10Arm pulls so far:1000 100 20

A B C

BanditsExample

Successes so far:100 50 10Arm pulls so far:1000 100 20

both arms are promising,higher uncertainty for C

A B C

Bandit approaches balance exploration and exploitation based on expected payoff and uncertainty.

Adding contextGoal: take the best action based on context information (e.g., topics in user history)Contextual ε-greedyIdea 1:

Use simple exploration approach (here: ε-greedy)

Idea 2:

Explore efficiently in a small action space, but use machine learning to optimize over a context space.

[Li et al. ‘12]

Contextual banditsExample application: news recommendation.

[Li et al. ‘12]

Li et al. propose to learn generalized linear models using contextual ε-greedy.

Models:

Example results:

Balancing exploration and exploitation is crucial for good results.

Summary: Contextual BanditsKey ideas

1) Balance exploration and exploitation, to ensure continued learning while applying what has been learned

2) Explore in a small action space, but learn in a large contextual space

Illustrated Sutra of Cause and Effect"E innga kyo" by Unknown - Woodblock reproduction, published in 1941 by Sinbi-Shoin Co., Tokyo. Licensed under Public domain via Wikimedia Commons - http://commons.wikimedia.org/wiki/File:E_innga_kyo.jpg#mediaviewer/File:E_innga_kyo.jpg

Counterfactual Reasoning

Example: ad placement

Problem: estimate effects of mainline reserve changes.

[Bottou et. al ‘13]

Counterfactual analysis

[Bottou et. al ‘13]

controlled experiment

counterfactual reasoning

Answering “what-if” questions

Key idea: estimate what would have happened if a different system (distribution over parameter values) had been used, using importance sampling.

Step 1: factorize based on known causal graph

This works because:

[Bottou et. al ‘13]

𝑃 ′ (𝜔 )=𝑃 (𝑢 ,𝑣 )∗…∗𝑃 ′ (𝑞|𝑥 ,𝑎 )∗…

𝑃 (𝜔 )=𝑃 (𝑢 ,𝑣 )∗…∗𝑃 (𝑞|𝑥 ,𝑎 )∗…

Step 2: compute estimates using importance sampling

𝑌 ′= 1𝑛∑𝑖=1

𝑛

𝑦 𝑖

𝑃 ′ (𝑞|𝑥 ,𝑎 )𝑃 (𝑞|𝑥 ,𝑎 )

= =

Example distributions:

𝑃 (𝑞)𝑃 ′(𝑞 )

𝑞

[Precup et. al ‘00]

Example result

[Bottou et. al ‘13]

Counterfactual reasoning allows analysis over a continuous range.

Summary: Counterfactual ReasoningKey ideas

1) Leverage known causal structure and importance sampling to reason about “alternative realities”

2) Bound estimator error to distinguish between uncertainty due to low sample size and exploration coverage

Online Learning to Rank

Compare two rankings:1) Generate interleaved (combined) ranking

2) Observe user clicks

3) Credit clicks to original rankers to infer outcome

document 1document 2document 3document 4

document 2document 3document 4document 1

document 1document 2document 3document 4

Interleaved Comparison Methods

[Joachims et al. ’05, Chapelle et al. ‘12, Hofmann et al. ‘13a]

Example: optimize QAC ranking

Learning from relative feedback

Dueling bandit gradient descent (DBGD) optimizes a weight vector for weighted-linear combinations of ranking features.

current best weight vector

sample unit sphere to generate candidate ranker

randomly generated candidate

featu

re

1

feature 2

Relative listwise feedback is obtained using interleaving

Learning approach

[Yue & Joachims ‘09]

Improving sample efficiency

Idea 1: Generate several candidate rankers, and select the best one by running a tournament on historical data

Idea 2: Use probabilistic interleave and importance sampling for ranker comparisons during the tournament

Estimate comparison outcomes using probabilistic interleave + importance sampling:

generate many candidates and select the most promising one

featu

re

1

feature 2

[Hofmann et al. ’13c]

Approach: candidate pre-selection (CPS)

Analysis: Speed of Learning

informational click model

[Hofmann et al. ’13b, Hofmann et al. ’13c]

From earlier work: learning from relative listwise feedback is robust to noise. Here: adding structure further dramatically improves performance.

Summary: Online Learning to RankKey ideas

1) Avoid combinatorial action space by exploring in parameter space

2) Reduce variance using relative feedback

3) Leverage known structures for sample-efficient learning

SummaryOptimizing interactive systemsSlow with manually designed alternatives and AB testing – how can we automate?

Contextual banditsSystematic approach to balancing exploration and exploitation; contextual bandits explore in small action space but optimize in large context space.

Counterfactual reasoningLeverages causal structure and importance sampling for “what if” analyses.

Online learning to rankAvoids combinatorial explosion by exploring and learning in parameter space; uses known ranking structure for sample-efficient learning.

What’s next?ResearchMeasuring reward, low-risk and low-variance exploration schemes, new learning mechanisms

ApplicationsAssess action and solution spaces in a given application, collect and learn from exploration data, increase experimental agilityTry this (at home)Try open-source code samples; Living labs challenge allows experimentation with online learning and evaluation methods

Challenge: http://living-labs.net/challenge/

Code: https://bitbucket.org/ilps/lerot

References and further readingA/B testing[Kohavi et al. ‘09] R. Kohavi, R. Longbotham, D. Sommerfield, R. M. Henne: Controlled experiments on the web:

survey and practical guide (Data Mining and Knowledge Discovery 18, 2009).

[Kohavi et al. ‘12] R. Kohavi, A. Deng, B. Frasca, R. Longbotham, T. Walker, Y. Xu: Trustworthy online controlled experiments: five puzzling outcomes explained (KDD 2012).

Contextual bandits[Li et al. ‘11] L. Li, W. Chu, J. Langford, X. Wang: Unbiased Offline Evaluation of Contextual-bandit-based News Article

Recommendation Algorithms (WWW, 2014).

[Li et al. ‘12] L. Li, W. Chu, J. Langford, T. Moon, X. Wang: An Unbiased Offline Evaluation of Contextual Bandit Algorithms based on Generalized Linear Models, ICML-2011 Workshop on Online Trading of Exploration and Exploitation.

Counterfactual reasoning[Bottou et. al ‘13] L. Bottou, J. Peters, J. Quiñonero-Candela, D.X. Charles, D.M. Chickering, E. Portugaly, D. Ray, P.

Simard, E. Snelson: Counterfactual reasoning and learning systems: the example of computational advertising (Journal of Machine Learning Research 14 (1), 2013).

[Precup et al. ‘00] D. Precup, R. S. Sutton, S. Singh: Eligibility Traces for Off-Policy Policy Evaluation (ICML 2000).

Interleaving[Chapelle et al. ‘12] O. Chapelle, T. Joachims, F. Radlinski, Y. Yue: Large Scale Validation and Analysis of Interleaved

Search Evaluation (ACM Transactions on Information Systems 30(1): 6, 2012).

[Hofmann et al. ’13a] K. Hofmann, S. Whiteson, M. de Rijke: Fidelity, Soundness, and Efficiency of Interleaved Comparison Methods (ACM Transactions on Information Systems 31(4): 17, 2013).

[Radlinski et al. ‘08] F. Radlinski, M. Kurup, and T. Joachims: How does clickthrough data reflect retrieval quality? (CIKM 2008).

Online learning to rank[Yue & Joachims ‘09] Y. Yue, T. Joachims: Interactively optimizing information retrieval system as a dueling bandits

problem (ICML 2009).

[Hofmann et al. ’13b] K. Hofmann, A. Schuth, S. Whiteson, M. de Rijke: Reusing Historical Interaction Data for Faster Online Learning to Rank for IR (WSDM 2013).

[Hofmann et al. ’13c] K. Hofmann, S. Whiteson, M. de Rijke: Balancing exploration and exploitation in listwise and pairwise online learning to rank for information retrieval (Information Retrieval 16, 2013).

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