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.

The item cold-start problem is crucial for online recommender systems, as the success of the cold-start phase determines whether items can transition into popular ones. Prompt learning, a powerful technique used in natural language processing (NLP) to address zero- or few-shot problems, has been adapted for recommender systems to tackle similar challenges. However, existing methods typically rely on content-based properties or text descriptions for prompting, which we argue may be suboptimal for cold-start recommendations due to 1) semantic gaps with recommender tasks, 2) model bias caused by warm-up items contribute most of the positive feedback to the model, which is the core of the cold-start problem that hinders the recommender quality on cold-start items. We propose to leverage high-value positive feedback, termed pinnacle feedback as prompt information, to simultaneously resolve the above two problems. We experimentally prove that compared to the content description proposed in existing works, the positive feedback is more suitable to serve as prompt information by bridging the semantic gaps. Besides, we propose item-wise personalized prompt networks to encode pinnaclce feedback to relieve the model bias by the positive feedback dominance problem. Extensive experiments on four real-world datasets demonstrate the superiority of our model over state-of-the-art methods. Moreover, PROMO has been successfully deployed on a popular short-video sharing platform, a billion-user scale commercial short-video application, achieving remarkable performance gains across various commercial metrics within cold-start scenarios.

Short-form video feed has become one of the most popular ways for billions of users to interact with content, where users watch short-form videos of a few seconds one-by-one in a session. The common solution to improve the quality of experience (QoE) for short-form video feed is to treat it as a common sequential item recommendation problem and maximize its click-through rate prediction. However, the QoE of short-form video streaming under dynamic network conditions is jointly determined by both recommendation accuracy and streaming efficiency, and thus merely considering recommendation will lead to the degradation of the QoE of the streaming system for the audience. In this paper, we propose SSR, namely the short-form video streaming and recommendation system, which consists of a Transformer-based recommendation module and a reinforcement learning (RL) based bitrate adaptation streaming module. Specifically, we use Transformer to encode the session into a representation vector and recommend proper short-form videos based on the user's recent interest and the timeliness characteristics of short-form video contents. Then, the RL module combines the representation of session and other observations within the playback, and yields the appropriate bitrate allocation for the next short-form video to optimize a given QoE objective. Trace-driven emulations verify the efficiency of SSR compared to several state-of-the-art recommender systems and streaming strategies with at least 10%-15% QoE improvement under various QoE objectives.