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Intro

Build independent LinUCB for each user? . Cold start challenge • Users are not independent

Connected users are assumed to share similar model parameters • Graph Laplacan based regularization upon ridge regression to model dependency

Graph Laplacian based regularization upon ridge regression to model dependency • Encode graph Laplaclan in context formulate as a di dimensional LIUCB

Social influence among users. content and opinion sharing in social network W • Reward: weighted average of expected reward among friends

Adaptively cluster users into groups by keep removing edges

item clustering • Each item cluster is associated with its own user clustering

Context-dependent clustering . For current user i, find neighboring user set /for every candidate item X. . Then aggregate the history rewards/ predictions within the user cluster.

Particle Thompson Sampling (PTS) [KBKTC15] • Probabilistic Matrix Factorization framework • Particle filtering for online Bayesian parameter estimation • Thompson Sampling for exploration

Alternating Least Squares for optimization • Exploration considers uncertainty from two factors

Leverage historical data to warm start model, reduce the need of exploration

What is the problem-related (structure-related) regret lower bound . Eg, user dependency structure, low rank, offline data • Did current algorithms fully utilize the information in problem structure?

Description:

Explore the second part of a conference talk from KDD 2020 focusing on learning by exploration. Delve into advanced concepts such as graph Laplacian regularization, social influence modeling, adaptive user clustering, and Particle Thompson Sampling. Examine techniques for addressing cold start challenges, encoding user dependencies, and leveraging historical data for model warm-starting. Investigate context-dependent clustering methods, probabilistic matrix factorization, and online Bayesian parameter estimation. Consider the problem-related regret lower bounds and evaluate the effectiveness of current algorithms in utilizing problem structure information.

KDD 2020- Learning by Exploration-Part 2

Association for Computing Machinery (ACM)

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#Computer Science #Machine Learning #Reinforcement Learning #Recommendation Systems

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