Christopher Leung

Hanjia Lyu, Song Jiang, Hanqing Zeng et al.

Text-based recommendation holds a wide range of practical applications due to its versatility, as textual descriptions can represent nearly any type of item. However, directly employing the original item descriptions may not yield optimal recommendation performance due to the lack of comprehensive information to align with user preferences. Recent advances in large language models (LLMs) have showcased their remarkable ability to harness commonsense knowledge and reasoning. In this study, we introduce a novel approach, coined LLM-Rec, which incorporates four distinct prompting strategies of text enrichment for improving personalized text-based recommendations. Our empirical experiments reveal that using LLM-augmented text significantly enhances recommendation quality. Even basic MLP (Multi-Layer Perceptron) models achieve comparable or even better results than complex content-based methods. Notably, the success of LLM-Rec lies in its prompting strategies, which effectively tap into the language model's comprehension of both general and specific item characteristics. This highlights the importance of employing diverse prompts and input augmentation techniques to boost the recommendation effectiveness of LLMs.

Juhee Hong, Meng Liu, Shengzhi Wang et al.

Large-scale recommendations commonly adopt a multi-stage cascading ranking system paradigm to balance effectiveness and efficiency. Early Stage Ranking (ESR) systems utilize the "user-item decoupling" approach, where independently learned user and item representations are only combined at the final layer. While efficient, this design is limited in effectiveness, as it struggles to capture fine-grained user-item affinities and cross-signals. To address these, we propose the Generative Early Stage Ranking (GESR) paradigm, introducing the Mixture of Attention (MoA) module which leverages diverse attention mechanisms to bridge the effectiveness gap: the Hard Matching Attention (HMA) module encodes explicit cross-signals by computing raw match counts between user and item features; the Target-Aware Self Attention module generates target-aware user representations conditioned on the item, enabling more personalized learning; and the Cross Attention modules facilitate early and more enriched interactions between user-item features. MoA's specialized attention encodings are further refined in the final layer through a Multi-Logit Parameterized Gating (MLPG) module, which integrates the newly learned embeddings via gating and produces secondary logits that are fused with the primary logit. To address the efficiency and latency challenges, we have introduced a comprehensive suite of optimization techniques. These span from custom kernels that maximize the capabilities of the latest hardware to efficient serving solutions powered by caching mechanisms. The proposed GESR paradigm has shown substantial improvements in topline metrics, engagement, and consumption tasks, as validated by both offline and online experiments. To the best of our knowledge, this marks the first successful deployment of full target-aware attention sequence modeling within an ESR stage at such a scale.

Gangda Deng, Hongkuan Zhou, Hanqing Zeng et al.

Recently, Temporal Graph Neural Networks (TGNNs) have demonstrated state-of-the-art performance in various high-impact applications, including fraud detection and content recommendation. Despite the success of TGNNs, they are prone to the prevalent noise found in real-world dynamic graphs like time-deprecated links and skewed interaction distribution. The noise causes two critical issues that significantly compromise the accuracy of TGNNs: (1) models are supervised by inferior interactions, and (2) noisy input induces high variance in the aggregated messages. However, current TGNN denoising techniques do not consider the diverse and dynamic noise pattern of each node. In addition, they also suffer from the excessive mini-batch generation overheads caused by traversing more neighbors. We believe the remedy for fast and accurate TGNNs lies in temporal adaptive sampling. In this work, we propose TASER, the first adaptive sampling method for TGNNs optimized for accuracy, efficiency, and scalability. TASER adapts its mini-batch selection based on training dynamics and temporal neighbor selection based on the contextual, structural, and temporal properties of past interactions. To alleviate the bottleneck in mini-batch generation, TASER implements a pure GPU-based temporal neighbor finder and a dedicated GPU feature cache. We evaluate the performance of TASER using two state-of-the-art backbone TGNNs. On five popular datasets, TASER outperforms the corresponding baselines by an average of 2.3% in Mean Reciprocal Rank (MRR) while achieving an average of 5.1x speedup in training time.

Seung Gyu Hyun, Christopher Leung

Real-world applications that involve missing values are often constrained by the cost to obtain data. Test-cost sensitive, or costly feature, methods additionally consider the cost of acquiring features. Such methods have been extensively studied in the problem of classification. In this paper, we study a related problem of test-cost sensitive methods to identify nearby points from a large set, given a new point with some unknown feature values. We present two models, one based on a tree and another based on Deep Reinforcement Learning. In our simulations, we show that the models outperform random agents on a set of five real-world data sets.