Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback (1909.03601v3)

Abstract: Recommender systems widely use implicit feedback such as click data because of its general availability. Although the presence of clicks signals the users' preference to some extent, the lack of such clicks does not necessarily indicate a negative response from the users, as it is possible that the users were not exposed to the items (positive-unlabeled problem). This leads to a difficulty in predicting the users' preferences from implicit feedback. Previous studies addressed the positive-unlabeled problem by uniformly upweighting the loss for the positive feedback data or estimating the confidence of each data having relevance information via the EM-algorithm. However, these methods failed to address the missing-not-at-random problem in which popular or frequently recommended items are more likely to be clicked than other items even if a user does not have a considerable interest in them. To overcome these limitations, we first define an ideal loss function to be optimized to realize recommendations that maximize the relevance and propose an unbiased estimator for the ideal loss. Subsequently, we analyze the variance of the proposed unbiased estimator and further propose a clipped estimator that includes the unbiased estimator as a special case. We demonstrate that the clipped estimator is expected to improve the performance of the recommender system, by considering the bias-variance trade-off. We conduct semi-synthetic and real-world experiments and demonstrate that the proposed method largely outperforms the baselines. In particular, the proposed method works better for rare items that are less frequently observed in the training data. The findings indicate that the proposed method can better achieve the objective of recommending items with the highest relevance.

• The paper introduces an ideal loss function and unbiased estimator that address both PU and MNAR challenges in implicit feedback.
• The methodology employs causal inference to develop a clipped estimator, achieving a superior bias-variance trade-off.
• Empirical results on semi-synthetic and Yahoo! R3 datasets show significant improvements in ranking and recommendations for less observed items.

Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback

The paper "Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback" explores the challenges of utilizing implicit feedback data, such as clicks, in recommender systems. Implicit feedback is prevalent due to its general availability, but this form of data inherently presents difficulties; missing interactions do not necessarily signify disinterest, bringing about the positive-unlabeled (PU) problem. Additionally, the paper addresses the missing-not-at-random (MNAR) issue where popular items garner more interactions irrespective of true user interest.

Primary Contributions

1. Definition of Ideal Loss Function: The authors define an ideal loss function that focuses on maximizing user-item relevance rather than mere click probability. They present an unbiased estimator for the ideal loss, which addresses both the PU and MNAR problems.
2. Theoretical Analysis: The paper conducts a thorough theoretical analysis to prove the biases inherent in conventional methods like Weighted Matrix Factorization (WMF) and Exposure Matrix Factorization (ExpoMF). The former applies uniform adjustments to clicked data, inadequately handling the PU challenge. The latter weights data based on exposure probability, which can bias results towards frequently clicked, i.e., popular items.
3. Proposed Unbiased and Clipped Estimators: To align with the ideal loss, the research introduces an unbiased estimator, adapting techniques from causal inference. An intrinsic feature of this estimator is its elevated variance in certain contexts. To counteract this, a clipped variant of the estimator is developed, aiming for a superior bias-variance trade-off.
4. Empirical Validation: The empirical validation includes both semi-synthetic and real-world datasets. In the semi-synthetic experiment, the proposed method outperformed the baseline methods consistently across various conditions with different levels of exposure bias. The real-world validation on the Yahoo! R3 dataset demonstrated substantial improvements in ranking metrics such as DCG, recall, and MAP, especially for optimizing recommendations of less frequently observed items.

Implications and Future Directions

The proposed method evidences a robust applicability in scenarios where exposure biases obfuscate true user preferences. By leveraging unbiased estimation principles, it enhances the recommendation quality and improves the treatment of rare items, a typical blind spot in many recommender systems.

The implications of this work are significant for advanced recommender systems that must operate on large scales where explicit feedback is infeasible. Future work could expand on refining propensity score estimation methods to account for more complex user-item interactions and further reduce variance, potentially exploring pairwise learning approaches compatible with the provided estimator frameworks.

The paper sets a foundational stride towards addressing notable biases present in implicit feedback-based systems, suggesting substantial potential refinements in both theory and application within AI-driven recommendation engines.