E-commerce recommendation platforms often operate across multiple domains, giving rise to Multi-Domain Recommendation (MDR) methods. These methods leverage auxiliary information from other domains to capture comprehensive user preferences and enhance recommendation accuracy. However, cross-domain knowledge transfer often causes domains to capture similar preferences. Additionally, most MDR models optimize for next-item prediction, which promotes greedy strategies focused on immediate gains within individual domains while neglecting the platform-wide profit. These two factors result in vicious competition, as our empirical analysis reveals: Domains recommend overlapping item lists, competing for the same pool of user attention and platform traffic, reducing the overall orders. To address this, we propose the Direct Preference Optimization from Manual Rules (DPOMR) training paradigm for MDR. Inspired by Reinforcement Learning from Human Feedback (RLHF), DPOMR learns user preferences from pairwise preference data generated through manual rules rooted in business expertise. Furthermore, we design a variant of Direct Preference Optimization (DPO) to bypass expensive RL training. Extensive experiments demonstrate the advantages of DPOMR, including: (1) achieving SOTA performance in boosting overall orders(2) showing compatibility with various backbones, and (3) offering robust extendibility to integrate diverse manual rules tailored to specific business needs.

In modern recommendation systems, leveraging multiple types of user-item interaction behaviors (e.g., click, add-to-cart, and purchase) presents both advantages and challenges. Recent studies organize multi-behavior data into heterogeneous bipartite graphs and used graph neural networks to learn latent representations. However, these methods struggle to model higher-order interactions and capture complex dependencies across various behaviors. In this paper, we propose a novel graph construction method that converts multi-behavior interactions into dual star-expansion hypergraphs by introducing a new type of node called hypernode. Subsequently, we develop StarRec, a hypergraph-based framework for multi-behavior recommendation. StarRec utilizes a spatial-based two-stage intra-behavior message passing strategy and a cross-behavior propagation layer to accurately and efficiently propagate information, modeling both high-order and cross-behavior relationships through the hypergraphs. This approach yields comprehensive representations that enhance recommendation performance. Experimental results on two real-world datasets demonstrate the superiority of StarRec. Our extensive experiments show that StarRec significantly outperforms state-of-the-art methods while maintaining competitive model scalability.

To address the business needs of industrial recommendation systems, an increasing number of Multi-Domain Recommendation (MDR) methods are designed to improve recommendation performance on multiple domains simultaneously. Most MDR methods follow a multi-task learning paradigm, suffering from poor deployability and negative transfer. Due to the great success of large pre-trained models, the pre-train & fine-tune paradigm is attracting increasing attention. The latest methods introduce parameter-efficient fine-tuning techniques like prompt-tuning, showcasing high efficiency and effectiveness. However, these methods neglect the fundamental differences between recommendation and NLP tasks. The inadequate capacity of recommendation models restricts the effectiveness of prompts and adapters. Worse still, traditional natural domain division may group non-identically distributed samples into the same domain, violating the assumption of independent and identically distributed (i.i.d.) data. In this paper, we propose MultiLoRA, a Multi-directional Low Rank Adaptation paradigm for multi-domain recommendation. First we pre-train a universal model using all data samples. Then we conduct multiple domain divisions on the sample space. Under each division, we fine-tune the pre-trained model to obtain a set of domain-specific LoRAs. Finally, we learn a LoRA fusion module to integrate domain-specific preference patterns across multiple divisions. Experimental results on real-world datasets demonstrate notable advantages of MultiLoRA: (1) achieving SOTA performance, (2) showcasing remarkable compatibility, and (3) proving highly efficient, featuring only 2% trainable parameters compared to the backbone.

Cross-Domain Recommendation (CDR) is a promising technique to alleviate data sparsity by transferring knowledge across domains. However, the negative transfer issue in the presence of numerous domains has received limited attention. Most existing methods transfer all information from source domains to the target domain without distinction. This introduces harmful noise and irrelevant features, resulting in suboptimal performance. Although some methods decompose user features into domain-specific and domain-shared components, they fail to consider other causes of negative transfer. Worse still, we argue that simple feature decomposition is insufficient for multi-domain scenarios. To bridge this gap, we propose TrineCDR, the TRIple-level kNowledge transferability Enhanced model for multi-target CDR. Unlike previous methods, TrineCDR captures single domain and targeted cross-domain embeddings to serve multi-domain recommendation. For the latter, we identify three fundamental causes of negative transfer, ranging from micro to macro perspectives, and correspondingly enhance knowledge transferability at three different levels: the feature level, the interaction level, and the domain level. Through these efforts, TrineCDR effectively filters out noise and irrelevant information from source domains, leading to more comprehensive and accurate representations in the target domain. We extensively evaluate the proposed model on real-world datasets, sampled from Amazon and Douban, under both dual-target and multi-target scenarios. The experimental results demonstrate the superiority of TrineCDR over state-of-the-art cross-domain recommendation methods.

Click-through rate (CTR) prediction, whose purpose is to predict the probability of a user clicking on an item, plays a pivotal role in recommender systems. Capturing users’ accurate preferences from their historical interactions (e.g., clicks) is an essential step for handling this task and has aroused wide concern in both academia and industry. However, most of the previous methods focus on the users with abundant clicks and ill-serve the users who rarely click or purchase items. Though the ratio of these long-tailed users may be small on popular platforms, such as Amazon and Taobao, they are the majority on the newborn e-commerce company like Lazada. To extract the interests of long-tailed users, several works attempt to integrate the side information, such as demographic features. Nevertheless, these features are usually not available and may even lead to privacy concerns. Therefore, how to utilize the noisy and limited clicks becomes the key challenge.In this paper, we propose a novel model called Hierarchical Interest Modeling (HIM). It hierarchically utilizes long-tailed users’ limited behaviors and captures their preferences from both personalized and group-wise perspectives. HIM consists of two main components, including User Behavior Pyramid (UBP) and User Behavior Clustering (UBC). The UBP module utilizes additional negative feedback to reduce the noises in positive feedback, thus obtaining reliable user personalized representations. Then, the UBC module automatically discovers latent user groups with self-supervised reconstruction loss and learns another interest representation for each user in a group-wise aspect. Extensive experiments on both public and industrial datasets verify the superiority of HIM compared with the state-of-the-art baselines. Moreover, HIM has already been deployed on Lazada recommendation scenario and gains 3.38% on CTR prediction on average on the online A/B test. Our codes are available in https://github.com/xiaojin-nj/HIM.