Causal Inference in Recommender System: Manipulating Popularity Bias

The widespread use of recommender systems across various platforms has introduced a pervasive challenge - popularity bias. This bias overshadows personalized selections and limits the diversity of recommendation systems, as they tend to prioritize widely popular items, hindering the discovery of unpopular content that may better align with individual user preferences.

Introduction

Popularity bias serves as a confounding factor in item and user interactions. Traditional algorithms often lean towards promoting popular items for profit, or overlook the significance of popularity, resulting in an imbalanced promotion of trending items. As a result, the content recommended to users often reflects prevailing trends more than their interests. This biased prioritization fosters a self-reinforcing loop, where popular items continue to be promoted.

To address popularity bias, we use the causal inference approach to de-bias user history data. We implemented two models, the Popularity-bias De-confounding and Adjusted model (PDA), and the Disentangling Interest and Conformity with causal Embeddings model (DICE) to produce more personalized recommendations. The two models introduce different logic behind manipulating the popularity bias, and we will discuss the details in the Methodology section.

We found that instead of completely removing the popularity bias, incorporating the bias in the algorithms performs better results, ie. achieving higher recall rates.

Conventional Recommender Systems

Recommender Systems are widely utilized in various online platforms such as personalized shopping recommendations, streaming platforms, and social media advertisements. These platforms employ the systems to match users to their potential preferences. Existing recommendation systems are divided into two categories, collaborative filtering(CF) and content-based recommendation(CTR). CF extracts the user preference based on users’ historical behaviors in which algorithms conduct similarities between users and items from historical interactions. CTR prediction takes advantage of high-order features of users, items, or context, to feed into neural networks. These traditional popular systems, although they involve high-level deep learning techniques, focus on correlations in data rather than causality, which is not induced by the former.

Here, our project borrowed ideas from ZHANG and ZHENG’s papers to integrate causal inference into recommendation systems, which enhance the accuracy and interpretability of personalized suggestions. These two proposed models, rooted in causal reasoning, provide a valuable framework for other recommender algorithms.

Causal Inference

Let’s take a look at the popularity bias under causal relationships.

In Figure 1(a), the traditional recommendation methods take user information (U node) and item information (I node) to account for the interaction, namely click (C node). This relationship form, I -> C and U -> C, is called the collider effect, that U and I are independent of each other, but both contribute to C.

However, in reality, a third factor, popularity (Z node) affects the interaction (C node) and the item information (I node). This Z variable is called a confounder, shown in Figure 1(b). To be specific, people have a conformity mentality that tends to follow the majority to choose purchase (Z -> C). So the more popular the items are, the more likely people will interact with them. On the other hand, recommender models tend to inherit the bias in the data to expose popular items more frequently (Z -> I -> C), which further exacerbates the popularity bias. The popularity (Z) that existed in the second path is a bias amplification and we want to remove or control the strength of it.

Figure 1: Causal Relationship Between Popularity and User Click

Data Processing

Two datasets, Douban and Netflix, are utilized for model comparison. Following the data cleaning phase, the Douban dataset retained 7,174,218 interactions involving 47,890 users and 26,047 items. The Netflix dataset comprises 122,958 unique users and 1,343 distinct items.

The PDA model involves temporal splitting to calculate popularity drift. The DICE model takes rating-5 entries as positive samples and artificially draws negative samples.

Methodology

PDA

- Causal inference offers a solution to de-bias user behavior data and removes the influence of popularity, allowing recommendation systems to provide more equitable and unbiased suggestions. The predictive model is \( P(Z \vert \text{do}(U, I)) \), where "do" means intervention.

- In the PD model, we completely remove popularity bias. We use \( ELU(f(u,i)) \), a user-item matching score, to indicate \( P(Z \vert \text{do}(U, I)) \), the probability of user behavior given user and item information.

- In the PDA model, we control the strength of popularity bias, which is a parameter \( \gamma \). We use \( ELU(f(u,i))*m_i^{\gamma} \) to estimate \( P(C \vert \text{do}(U, I), \text{do}(Z)) \), the probability of user behavior by intervening user & item and the popularity bias. Here \( m_i \) is the popularity value of \( Z=z \).

- Then we optimize the BPR loss function to train the model.

Figure 2: PDA model task flow

DICE

- The DICE model separates user interest (same idea as U in PDA) and conformity (same idea as Z in PDA) embeddings, from which popularity bias is considered as a cause of user behavior.

- The DICE methodology is a framework for disentangling user and item embeddings, which can be incorporated into other recommender models.

- We separate dataset \( O \) to two cause-specific datasets. \( O_1 \) is conformity-caused data, and \( O_2 \) is interest-caused data.

- We learn user and item embeddings separately in \( O_1 \) and \( O_2 \). Then we concatenate two embeddings to estimate clicks, ie. user behavior.

- Adopting multi-task curriculum learning, the final loss function is \[ L = L_{\text{click}}^{O_1 + O_2} + \alpha \cdot (L_{\text{interest}}^{O_2} + L_{\text{conformity}}^{O_1 + O_2}) + \beta \cdot L_{\text{discrepancy}} \]

Figure 3: DICE model task flow

Result

Our analysis of the PDA and DICE recommender system models shows distinct performances on Netflix and Douban, suggesting the influence of different user engagement patterns.

On Netflix, known for its herd behavior where users often gravitate towards popular content, DICE excels (recall@20: 0.1906) by effectively differentiating between a user’s individual preferences and what’s trending. This distinction allows for popular recommendations without neglecting personal tastes.

Douban, with its focus on thorough reviews and user reflection, particularly for books, sees the PDA model perform better (recall@20: 0.0565). Here, the PDA model prevents the amplification of popularity bias during training and judiciously incorporates popularity during inference, catering to Douban's distinct user interaction style, which favors more personalized engagement.

The outcomes from both datasets underline the importance of tailoring recommender systems to match user behaviors and platform nuances. While DICE suits platforms where shared cultural trends heavily sway user choices, like Netflix, PDA is ideal for platforms that prioritize deep, individual engagement, like Douban. This nuanced approach is essential for creating recommender systems that offer both popular and individualized content recommendations.

Figure 4: Recall@20 model comparison

Application

Mitigating popularity bias in recommender systems is essential for creating more personalized and diverse digital environments. This approach not only elevates user satisfaction by aligning recommendations more closely with individual preferences but also promotes a broader spectrum of content and products. It benefits various platforms, including e-commerce, content streaming, social media, and online advertising, by fostering equitable exposure for emerging talents and niche products.

Key Benefits:

- Personalization: Recommendations reflect genuine user interests beyond mainstream popularity.

- Diversity: Expands the range of content, supporting a vibrant ecosystem of creators and vendors.

- Engagement: Enhances user engagement by providing relevant and intriguing content discoveries.

Reference

- Zhang, Yang, Fuli Feng, Xiangnan He, Tianxin Wei, Chonggang Song, Guohui Ling, and Yongdong Zhang.2021. “Causal intervention for leveraging popularity bias in recommendation.” In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval.

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- Yu Zheng, Chen Gao, Xiang Li, Xiangnan He, Depeng Jin, Yong Li. 2021. Disentangling User Interest and Conformity for Recommendation with Causal Embedding. In Proceedings of the Web Conference 2021.

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