AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations

Collaborative filtering methods based on graph neural networks (GNNs) have witnessed significant success in recommender systems (RS), capitalizing on their ability to capture collaborative signals within intricate user-item relationships via message-passing mechanisms. However, these GNN-based RS inadvertently introduce excess linear correlation between user and item embeddings, contradicting the goal of providing personalized recommendations. While existing research predominantly ascribes this flaw to the over-smoothing problem, this paper underscores the critical, often overlooked role of the over-correlation issue in diminishing the effectiveness of GNN representations and subsequent recommendation performance. Up to now, the over-correlation issue remains unexplored in RS. Meanwhile, how to mitigate the impact of over-correlation while preserving collaborative filtering signals is a significant challenge. To this end, this paper aims to address the aforementioned gap by undertaking a comprehensive study of the over-correlation issue in graph collaborative filtering models. Firstly, we present empirical evidence to demonstrate the widespread prevalence of over-correlation in these models. Subsequently, we dive into a theoretical analysis which establishes a pivotal connection between the over-correlation and over-smoothing issues. Leveraging these insights, we introduce the Adaptive Feature De-correlation Graph Collaborative Filtering (AFDGCF) framework, which dynamically applies correlation penalties to the feature dimensions of the representation matrix, effectively alleviating both over-correlation and over-smoothing issues. The efficacy of the proposed framework is corroborated through extensive experiments conducted with four representative graph collaborative filtering models across four publicly available datasets.

Understanding the Ranking Loss for Recommendation with Sparse User Feedback

Click-through rate (CTR) prediction holds significant importance in the realm of online advertising. While many existing approaches treat it as a binary classification problem and utilize binary cross entropy (BCE) as the optimization objective, recent advancements have indicated that combining BCE loss with ranking loss yields substantial performance improvements. However, the full efficacy of this combination loss remains incompletely understood. In this paper, we uncover a new challenge associated with BCE loss in scenarios with sparse positive feedback, such as CTR prediction: the gradient vanishing for negative samples. Subsequently, we introduce a novel perspective on the effectiveness of ranking loss in CTR prediction, highlighting its ability to generate larger gradients on negative samples, thereby mitigating their optimization issues and resulting in improved classification ability. Our perspective is supported by extensive theoretical analysis and empirical evaluation conducted on publicly available datasets. Furthermore, we successfully deployed the ranking loss in Tencent's online advertising system, achieving notable lifts of 0.70% and 1.26% in Gross Merchandise Value (GMV) for two main scenarios. The code for our approach is openly accessible at the following GitHub repository: https://github.com/SkylerLinn/Understanding-the-Ranking-Loss.

Recommendation Algorithm Based on Recommendation Sessions

The enormous development of the Internet, both in the geographical scale and in the area of using its possibilities in everyday life, determines the creation and collection of huge amounts of data. Due to the scale, it is not possible to analyse them using traditional methods, therefore it makes a necessary to use modern methods and techniques. Such methods are provided, among others, by the area of recommendations. The aim of this study is to present a new algorithm in the area of recommendation systems, the algorithm based on data from various sets of information, both static (categories of objects, features of objects) and dynamic (user behaviour).

Retention Induced Biases in a Recommendation System with Heterogeneous Users

I examine a conceptual model of a recommendation system (RS) with user inflow and churn dynamics. When inflow and churn balance out, the user distribution reaches a steady state. Changing the recommendation algorithm alters the steady state and creates a transition period. During this period, the RS behaves differently from its new steady state. In particular, A/B experiment metrics obtained in transition periods are biased indicators of the RS's long term performance. Scholars and practitioners, however, often conduct A/B tests shortly after introducing new algorithms to validate their effectiveness. This A/B experiment paradigm, widely regarded as the gold standard for assessing RS improvements, may consequently yield false conclusions. I also briefly discuss the data bias caused by the user retention dynamics.

Ad Recommendation in a Collapsed and Entangled World

In this paper, we present an industry ad recommendation system, paying attention to the challenges and practices of learning appropriate representations. Our study begins by showcasing our approaches to preserving priors when encoding features of diverse types into embedding representations. Specifically, we address sequence features, numeric features, pre-trained embedding features, as well as sparse ID features. Moreover, we delve into two pivotal challenges associated with feature representation: the dimensional collapse of embeddings and the interest entanglement across various tasks or scenarios. Subsequently, we propose several practical approaches to effectively tackle these two challenges. We then explore several training techniques to facilitate model optimization, reduce bias, and enhance exploration. Furthermore, we introduce three analysis tools that enable us to comprehensively study feature correlation, dimensional collapse, and interest entanglement. This work builds upon the continuous efforts of Tencent's ads recommendation team in the last decade. It not only summarizes general design principles but also presents a series of off-the-shelf solutions and analysis tools. The reported performance is based on our online advertising platform, which handles hundreds of billions of requests daily, serving millions of ads to billions of users.

BMLP: Behavior-aware MLP for Heterogeneous Sequential Recommendation

In real recommendation scenarios, users often have different types of behaviors, such as clicking and buying. Existing research methods show that it is possible to capture the heterogeneous interests of users through different types of behaviors. However, most multi-behavior approaches have limitations in learning the relationship between different behaviors. In this paper, we propose a novel multilayer perceptron (MLP)-based heterogeneous sequential recommendation method, namely behavior-aware multilayer perceptron (BMLP). Specifically, it has two main modules, including a heterogeneous interest perception (HIP) module, which models behaviors at multiple granularities through behavior types and transition relationships, and a purchase intent perception (PIP) module, which adaptively fuses subsequences of auxiliary behaviors to capture users' purchase intent. Compared with mainstream sequence models, MLP is competitive in terms of accuracy and has unique advantages in simplicity and efficiency. Extensive experiments show that BMLP achieves significant improvement over state-of-the-art algorithms on four public datasets. In addition, its pure MLP architecture leads to a linear time complexity.

Self-supervised Contrastive Learning for Implicit Collaborative Filtering

Contrastive learning-based recommendation algorithms have significantly advanced the field of self-supervised recommendation, particularly with BPR as a representative ranking prediction task that dominates implicit collaborative filtering. However, the presence of false-positive and false-negative examples in recommendation systems hampers accurate preference learning. In this study, we propose a simple self-supervised contrastive learning framework that leverages positive feature augmentation and negative label augmentation to improve the self-supervisory signal. Theoretical analysis demonstrates that our learning method is equivalent to maximizing the likelihood estimation with latent variables representing user interest centers. Additionally, we establish an efficient negative label augmentation technique that samples unlabeled examples with a probability linearly dependent on their relative ranking positions, enabling efficient augmentation in constant time complexity. Through validation on multiple datasets, we illustrate the significant improvements our method achieves over the widely used BPR optimization objective while maintaining comparable runtime.

Debiasing Recommendation with Personal Popularity

Global popularity (GP) bias is the phenomenon that popular items are recommended much more frequently than they should be, which goes against the goal of providing personalized recommendations and harms user experience and recommendation accuracy. Many methods have been proposed to reduce GP bias but they fail to notice the fundamental problem of GP, i.e., it considers popularity from a \textit{global} perspective of \textit{all users} and uses a single set of popular items, and thus cannot capture the interests of individual users. As such, we propose a user-aware version of item popularity named \textit{personal popularity} (PP), which identifies different popular items for each user by considering the users that share similar interests. As PP models the preferences of individual users, it naturally helps to produce personalized recommendations and mitigate GP bias. To integrate PP into recommendation, we design a general \textit{personal popularity aware counterfactual} (PPAC) framework, which adapts easily to existing recommendation models. In particular, PPAC recognizes that PP and GP have both direct and indirect effects on recommendations and controls direct effects with counterfactual inference techniques for unbiased recommendations. All codes and datasets are available at \url{https://github.com/Stevenn9981/PPAC}.

Understanding the Role of Cross-Entropy Loss in Fairly Evaluating Large Language Model-based Recommendation

Large language models (LLMs) have gained much attention in the recommendation community; some studies have observed that LLMs, fine-tuned by the cross-entropy loss with a full softmax, could achieve state-of-the-art performance already. However, these claims are drawn from unobjective and unfair comparisons. In view of the substantial quantity of items in reality, conventional recommenders typically adopt a pointwise/pairwise loss function instead for training. This substitute however causes severe performance degradation, leading to under-estimation of conventional methods and over-confidence in the ranking capability of LLMs. In this work, we theoretically justify the superiority of cross-entropy, and showcase that it can be adequately replaced by some elementary approximations with certain necessary modifications. The remarkable results across three public datasets corroborate that even in a practical sense, existing LLM-based methods are not as effective as claimed for next-item recommendation. We hope that these theoretical understandings in conjunction with the empirical results will facilitate an objective evaluation of LLM-based recommendation in the future.

Are ID Embeddings Necessary? Whitening Pre-trained Text Embeddings for Effective Sequential Recommendation

Recent sequential recommendation models have combined pre-trained text embeddings of items with item ID embeddings to achieve superior recommendation performance. Despite their effectiveness, the expressive power of text features in these models remains largely unexplored. While most existing models emphasize the importance of ID embeddings in recommendations, our study takes a step further by studying sequential recommendation models that only rely on text features and do not necessitate ID embeddings. Upon examining pretrained text embeddings experimentally, we discover that they reside in an anisotropic semantic space, with an average cosine similarity of over 0.8 between items. We also demonstrate that this anisotropic nature hinders recommendation models from effectively differentiating between item representations and leads to degenerated performance. To address this issue, we propose to employ a pre-processing step known as whitening transformation, which transforms the anisotropic text feature distribution into an isotropic Gaussian distribution. Our experiments show that whitening pre-trained text embeddings in the sequential model can significantly improve recommendation performance. However, the full whitening operation might break the potential manifold of items with similar text semantics. To preserve the original semantics while benefiting from the isotropy of the whitened text features, we introduce WhitenRec+, an ensemble approach that leverages both fully whitened and relaxed whitened item representations for effective recommendations. We further discuss and analyze the benefits of our design through experiments and proofs. Experimental results on three public benchmark datasets demonstrate that WhitenRec+ outperforms state-of-the-art methods for sequential recommendation.