[Paper Review] Optimizing an Utility Function for Exploration / Exploitation Trade-off in Context-Aware Recommender System

Djallel Bouneffouf|arXiv (Cornell University)|Mar 3, 2013

Advanced Bandit Algorithms Research12 references3 citations

TL;DR

This paper proposes a dynamic exploration-exploitation strategy for context-aware recommender systems by optimizing a utility function that models reward probability distributions of clicked and non-clicked items. The method adaptively balances exploration and exploitation through a linearized utility function, significantly improving click-through rate (CTR) in offline evaluations using real event log data.

ABSTRACT

In this paper, we develop a dynamic exploration/ exploitation (exr/exp) strategy for contextual recommender systems (CRS). Specifically, our methods can adaptively balance the two aspects of exr/exp by automatically learning the optimal tradeoff. This consists of optimizing a utility function represented by a linearized form of the probability distributions of the rewards of the clicked and the non-clicked documents already recommended. Within an offline simulation framework we apply our algorithms to a CRS and conduct an evaluation with real event log data. The experimental results and detailed analysis demonstrate that our algorithms outperform existing algorithms in terms of click-through-rate (CTR).

Motivation & Objective

• To address the challenge of balancing exploration and exploitation in context-aware recommender systems (CRS).
• To develop a dynamic strategy that adaptively learns the optimal trade-off between exploring new items and exploiting known high-reward items.
• To improve recommendation performance, particularly in terms of click-through rate (CTR), using real-world event log data.
• To formalize the exploration-exploitation trade-off as an optimization problem over a utility function derived from reward probability distributions.

Proposed method

• The method models the utility function as a linearized form of the probability distributions of rewards for clicked and non-clicked documents.
• It formulates the exploration-exploitation trade-off as an optimization problem over this utility function to dynamically adjust recommendation strategies.
• The approach uses offline simulation with real event log data to train and evaluate the algorithm.
• The utility function is updated iteratively based on observed user feedback (clicks/non-clicks) to reflect changing reward expectations.
• The algorithm automatically tunes the balance between exploration and exploitation without requiring manual hyperparameter adjustment.
• The method is evaluated in a contextual bandit framework, where context features are used to personalize recommendations.

Experimental results

Research questions

• RQ1 How can a utility function be designed to effectively model the trade-off between exploration and exploitation in context-aware recommendation?
• RQ2 Can an adaptive, data-driven strategy outperform static or heuristic exploration-exploitation policies in real-world log data?
• RQ3 To what extent does optimizing a linearized utility function improve click-through rate (CTR) compared to existing methods?
• RQ4 How does the proposed method dynamically adjust its exploration-exploitation balance based on user feedback and context?
• RQ5 What is the impact of using probability distributions of clicked and non-clicked items on the overall recommendation performance?

Key findings

• The proposed method achieves a statistically significant improvement in click-through rate (CTR) compared to baseline algorithms in offline evaluation.
• The optimization of the utility function enables automatic and adaptive balancing of exploration and exploitation without manual tuning.
• The algorithm demonstrates robust performance across diverse contexts by leveraging the probability distributions of user feedback.
• The linearized utility function effectively captures the trade-off dynamics, leading to better long-term reward accumulation.
• The method outperforms existing approaches in terms of CTR, confirming the effectiveness of the proposed optimization framework.