Comprehending Knowledge Graphs with Large Language Models for Recommender Systems

Recently, the introduction of knowledge graphs (KGs) has significantly advanced recommender systems by facilitating the discovery of potential associations between items. However, existing methods still face several limitations. First, most KGs suffer from missing facts or limited scopes. This can lead to biased knowledge representations, thereby constraining the model's performance. Second, existing methods typically convert textual information into IDs, resulting in the loss of natural semantic connections between different items. Third, existing methods struggle to capture high-order relationships in global KGs due to their inefficient layer-by-layer information propagation mechanisms, which are prone to introducing significant noise. To address these limitations, we propose a novel method called CoLaKG, which leverages large language models (LLMs) for knowledge-aware recommendation. The extensive world knowledge and remarkable reasoning capabilities of LLMs enable them to supplement KGs. Additionally, the strong text comprehension abilities of LLMs allow for a better understanding of semantic information. Based on this, we first extract subgraphs centered on each item from the KG and convert them into textual inputs for the LLM. The LLM then outputs its comprehension of these item-centered subgraphs, which are subsequently transformed into semantic embeddings. Furthermore, to utilize the global information of the KG, we construct an item-item graph using these semantic embeddings, which can directly capture higher-order associations between items. Both the semantic embeddings and the structural information from the item-item graph are effectively integrated into the recommendation model through our designed representation alignment and neighbor augmentation modules. Extensive experiments on four real-world datasets demonstrate the superiority of our method.

Rankability-enhanced Revenue Uplift Modeling Framework for Online Marketing

Uplift modeling has been widely employed in online marketing by predicting the response difference between the treatment and control groups, so as to identify the sensitive individuals toward interventions like coupons or discounts. Compared with traditional \textit{conversion uplift modeling}, \textit{revenue uplift modeling} exhibits higher potential due to its direct connection with the corporate income. However, previous works can hardly handle the continuous long-tail response distribution in revenue uplift modeling. Moreover, they have neglected to optimize the uplift ranking among different individuals, which is actually the core of uplift modeling. To address such issues, in this paper, we first utilize the zero-inflated lognormal (ZILN) loss to regress the responses and customize the corresponding modeling network, which can be adapted to different existing uplift models. Then, we study the ranking-related uplift modeling error from the theoretical perspective and propose two tighter error bounds as the additional loss terms to the conventional response regression loss. Finally, we directly model the uplift ranking error for the entire population with a listwise uplift ranking loss. The experiment results on offline public and industrial datasets validate the effectiveness of our method for revenue uplift modeling. Furthermore, we conduct large-scale experiments on a prominent online fintech marketing platform, Tencent FiT, which further demonstrates the superiority of our method in practical applications.

Diffusion-based Contrastive Learning for Sequential Recommendation

Contrastive learning has been effectively applied to alleviate the data sparsity issue and enhance recommendation performance.The majority of existing methods employ random augmentation to generate augmented views of original sequences. The learning objective then aims to minimize the distance between representations of different views for the same user. However, these random augmentation strategies (e.g., mask or substitution) neglect the semantic consistency of different augmented views for the same user, leading to semantically inconsistent sequences with similar representations. Furthermore, most augmentation methods fail to utilize context information, which is critical for understanding sequence semantics. To address these limitations, we introduce a diffusion-based contrastive learning approach for sequential recommendation. Specifically, given a user sequence, we first select some positions and then leverage context information to guide the generation of alternative items via a guided diffusion model. By repeating this approach, we can get semantically consistent augmented views for the same user, which are used to improve the effectiveness of contrastive learning. To maintain cohesion between the representation spaces of both the diffusion model and the recommendation model, we train the entire framework in an end-to-end fashion with shared item embeddings. Extensive experiments on five benchmark datasets demonstrate the superiority of our proposed method.

Treatment-Aware Hyperbolic Representation Learning for Causal Effect Estimation with Social Networks

Estimating the individual treatment effect (ITE) from observational data is a crucial research topic that holds significant value across multiple domains. How to identify hidden confounders poses a key challenge in ITE estimation. Recent studies have incorporated the structural information of social networks to tackle this challenge, achieving notable advancements. However, these methods utilize graph neural networks to learn the representation of hidden confounders in Euclidean space, disregarding two critical issues: (1) the social networks often exhibit a scalefree structure, while Euclidean embeddings suffer from high distortion when used to embed such graphs, and (2) each ego-centric network within a social network manifests a treatment-related characteristic, implying significant patterns of hidden confounders. To address these issues, we propose a novel method called Treatment-Aware Hyperbolic Representation Learning (TAHyper). Firstly, TAHyper employs the hyperbolic space to encode the social networks, thereby effectively reducing the distortion of confounder representation caused by Euclidean embeddings. Secondly, we design a treatment-aware relationship identification module that enhances the representation of hidden confounders by identifying whether an individual and her neighbors receive the same treatment. Extensive experiments on two benchmark datasets are conducted to demonstrate the superiority of our method.