Modeling User Exposure in Recommendation (1510.07025v2)

Abstract: Collaborative filtering analyzes user preferences for items (e.g., books, movies, restaurants, academic papers) by exploiting the similarity patterns across users. In implicit feedback settings, all the items, including the ones that a user did not consume, are taken into consideration. But this assumption does not accord with the common sense understanding that users have a limited scope and awareness of items. For example, a user might not have heard of a certain paper, or might live too far away from a restaurant to experience it. In the language of causal analysis, the assignment mechanism (i.e., the items that a user is exposed to) is a latent variable that may change for various user/item combinations. In this paper, we propose a new probabilistic approach that directly incorporates user exposure to items into collaborative filtering. The exposure is modeled as a latent variable and the model infers its value from data. In doing so, we recover one of the most successful state-of-the-art approaches as a special case of our model, and provide a plug-in method for conditioning exposure on various forms of exposure covariates (e.g., topics in text, venue locations). We show that our scalable inference algorithm outperforms existing benchmarks in four different domains both with and without exposure covariates.

• The paper introduces ExpoMF, a probabilistic model that separates exposure from interaction to overcome implicit feedback limitations.
• It employs Gaussian matrix factorization with an EM-based inference procedure and integrates exposure covariates for scalable, adaptable recommendations.
• Empirical results demonstrate that ExpoMF outperforms WMF in key metrics like Recall and NDCG, confirming its effective causal inference approach.

Modeling User Exposure in Recommendation: An Essay

This paper presents a novel probabilistic modeling approach that integrates user exposure to items into the collaborative filtering framework, a cornerstone technique in recommender systems. By considering exposure as a latent variable, the researchers aim to rigorously address the limitation of existing implicit feedback models that inadequately assume all unclicked items are disliked by users.

Theoretical Framework

The primary contribution is the introduction of the Exposure Matrix Factorization (ExpoMF) model, which proposes a latent representation of user exposure. This model computationally separates two events: exposure to an item and the actual interaction with it. The premise is that a user's interaction history (e.g., clicks, views) only partially represents their preferences due to limited exposure. The ExpoMF framework is positioned as a more general case where existing methods like Weighted Matrix Factorization (WMF) are seen as special instances when exposure is treated uniformly across all user-item pairs.

Notably, ExpoMF integrates ideas from causal inference, specifically using the potential outcomes framework to decouple the exposure mechanism from user preferences. This approach reflects an advanced understanding of the implicit data's causal structure, recognizing that non-consumption can result from non-exposure rather than disapproval.

Methodology

The model leverages a Gaussian probabilistic matrix factorization structure, delineating user preferences and item profiles via latent factors. Exposure is characterized as a binary latent variable inferred from the interaction data. The researchers also introduce a scalable inference procedure based on Expectation-Maximization (EM), allowing for efficient parameter estimation even in large-scale datasets.

An innovative aspect of the approach is the model's flexibility to incorporate exposure covariates, such as geographical location or textual content, through logistic regression. This capability significantly enhances the model's adaptability to real-world contexts where additional metadata can influence exposure.

Empirical Results

The authors validate the efficacy of ExpoMF across multiple domains, including music listening, academic paper recommendation, bookmarking activities, and venue check-ins. Their empirical analysis demonstrates that ExpoMF, with or without the inclusion of exposure covariates, consistently outperforms the state-of-the-art WMF across different datasets on key recommendation metrics like Recall and NDCG.

Implications and Future Insights

The implications of this work are both practical and theoretical. Practically, the model offers recommender systems a robust methodology to account for exposure, leading to more accurate consumer behavior predictions. Theoretically, it encourages further exploration into causally explicit models in recommendation systems, advancing beyond heuristic-adjusted inference methods.

Future research could expand on the model's capacity to dynamically adjust exposure probabilities over time or leverage additional causal data sources, such as user browsing patterns. Furthermore, deploying ExpoMF in online environments with real-time user feedback would provide valuable insights into its operational impact and any potential need for model refinement.

In conclusion, this paper enriches the collaborative filtering landscape by addressing a fundamental limitation in modeling implicit feedback, enabling more refined user preference predictions through the thoughtful integration of exposure as a latent factor.