Sequential recommender systems aim to predict users' next interested item given their historical interactions. However, a long-standing issue is how to distinguish between users' long/short-term interests, which may be heterogeneous and contribute differently to the next recommendation. Existing approaches usually set pre-defined short-term interest length by exhaustive search or empirical experience, which is either highly inefficient or yields subpar results. The recent advanced transformer-based models can achieve state-of-the-art performances despite the aforementioned issue, but they have a quadratic computational complexity to the length of the input sequence. To this end, this paper proposes a novel sequential recommender system, AutoMLP, aiming for better modeling users' long/short-term interests from their historical interactions. In addition, we design an automated and adaptive search algorithm for preferable short-term interest length via end-to-end optimization. Through extensive experiments, we show that AutoMLP has competitive performance against state-of-the-art methods, while maintaining linear computational complexity.
Successful sequential recommendation systems rely on accurately capturing the user's short-term and long-term interest. Although Transformer-based models achieved state-of-the-art performance in the sequential recommendation task, they generally require quadratic memory and time complexity to the sequence length, making it difficult to extract the long-term interest of users. On the other hand, Multi-Layer Perceptrons (MLP)-based models, renowned for their linear memory and time complexity, have recently shown competitive results compared to Transformer in various tasks. Given the availability…
TriMLP: Revenge of a MLP-like Architecture in Sequential Recommendation
Jilin University（吉林大学）；University of Macau（澳门大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Hong Kong University of Science and Technology（香港科技大学）
Recently, deep learning models have been widely spread in the industrial recommender systems and boosted the recommendation quality. Though having achieved remarkable success, the design of task-aware recommender systems usually requires manual feature engineering and architecture engineering from domain experts. To relieve those human efforts, we explore the potential of neural architecture search (NAS) and introduce AMEIR for Automatic behavior Modeling, interaction Exploration and multi-layer perceptron (MLP) Investigation in the Recommender system. The core contributions of AMEIR are the t…
Personalized recommendations are the backbone machine learning (ML) algorithm that powers several important application domains (e.g., ads, e-commerce, etc) serviced from cloud datacenters. Sparse embedding layers are a crucial building block in designing recommendations yet little attention has been paid in properly accelerating this important ML algorithm. This paper first provides a detailed workload characterization on personalized recommendations and identifies two significant performance limiters: memory-intensive embedding layers and compute-intensive multi-layer perceptron (MLP) layers…
The use of GPUs has proliferated for machine learning workflows and is now considered mainstream for many deep learning models. Meanwhile, when training state-of-the-art personal recommendation models, which consume the highest number of compute cycles at our large-scale datacenters, the use of GPUs came with various challenges due to having both compute-intensive and memory-intensive components. GPU performance and efficiency of these recommendation models are largely affected by model architecture configurations such as dense and sparse features, MLP dimensions. Furthermore, these models oft…
Renmin University of China（中国人民大学）；University of Chinese Academy of Sciences（中国科学院大学）；Chinese Academy of Sciences（中国科学院）
Recently, deep neural networks such as RNN, CNN and Transformer have been applied in the task of sequential recommendation, which aims to capture the dynamic preference characteristics from logged user behavior data for accurate recommendation. However, in online platforms, logged user behavior data is inevitable to contain noise, and deep recommendation models are easy to overfit on these logged data. To tackle this problem, we borrow the idea of filtering algorithms from signal processing that attenuates the noise in the frequency domain. In our empirical experiments, we find that filtering …
A Survey on Deep Neural Networks in Collaborative Filtering Recommendation Systems
Center for Artificial Intelligence and Technology（人工智能与技术中心）；Faculty of Information Science and Technology（信息科学与技术学院）；The National University of Malaysia（马来西亚国立大学）
BMLP: Behavior-aware MLP for Heterogeneous Sequential Recommendation
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））
Self-attention models have achieved state-of-the-art performance in sequential recommender systems by capturing the sequential dependencies among user-item interactions. However, they rely on positional embeddings to retain the sequential information, which may break the semantics of item embeddings. In addition, most existing works assume that such sequential dependencies exist solely in the item embeddings, but neglect their existence among the item features. In this work, we propose a novel sequential recommender system (MLP4Rec) based on the recent advances of MLP-based architectures, whic…
University of Cambridge（剑桥大学）；University of Alberta（阿尔伯塔大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；University College London（伦敦大学学院）；University of Amsterdam（阿姆斯特丹大学）
The linearised Laplace method for estimating model uncertainty has received renewed attention in the Bayesian deep learning community. The method provides reliable error bars and admits a closed-form expression for the model evidence, allowing for scalable selection of model hyperparameters. In this work, we examine the assumptions behind this method, particularly in conjunction with model selection. We show that these interact poorly with some now-standard tools of deep learning--stochastic approximation methods and normalisation layers--and make recommendations for how to better adapt this c…
Alibaba Search\&Recommendation Group Beijing\&Hangzhou China；Alibaba Search\&Recommendation Group
Deep learning based methods have been widely used in industrial recommendation systems (RSs). Previous works adopt an Embedding&MLP paradigm: raw features are embedded into low-dimensional vectors, which are then fed on to MLP for final recommendations. However, most of these works just concatenate different features, ignoring the sequential nature of users' behaviors. In this paper, we propose to use the powerful Transformer model to capture the sequential signals underlying users' behavior sequences for recommendation in Alibaba. Experimental results demonstrate the superiority of the propos…
In this talk, we introduce Merlin HugeCTR. Merlin HugeCTR is an open source, GPU-accelerated integration framework for click-through rate estimation. It optimizes both training and inference, whilst enabling model training at scale with model-parallel embeddings and data-parallel neural networks. In particular, Merlin HugeCTR combines a high-performance GPU embedding cache with an hierarchical storage architecture, to realize low-latency retrieval of embeddings for online model inference tasks. In the MLPerf v1.0 DLRM model training benchmark, Merlin HugeCTR achieves a speedup of up to 24.6x o…
Who Benefits From Sinus Surgery? Comparing Generative AI and Supervised Machine Learning for Predicting Surgical Outcomes in Chronic Rhinosinusitis
Department of Computer Science, Purdue University（计算机科学系，普渡大学）；Department of Computer Science, Indiana University Indianapolis（计算机科学系，印第安纳大学印第安纳波利斯分校）；Department of Otolaryngology—Head and Neck Surgery, Indiana University School of Medicine（耳鼻喉科—头颈外科，印第安纳大学医学院）
There are rich formats of information in the network, such as rating, text, image, and so on, which represent different aspects of user preferences. In the field of recommendation, how to use those data effectively has become a difficult subject. With the rapid development of neural network, researching on multi-modal method for recommendation has become one of the major directions. In the existing recommender systems, numerical rating, item description and review are main information to be considered by researchers. However, the characteristics of the item may affect the user's preferences, w…
Optimized Three Deep Learning Models Based-PSO Hyperparameters for Beijing PM2.5 Prediction
College of Computer and Information, Hohai University（河海大学计算机与信息学院）；Universitas Ahmad Dahlan（艾哈迈德·达兰大学）；Faculty of Engineering, Universitas Negeri Malang（马朗国立大学工程学院）
GLINT-RU: Gated Lightweight Intelligent Recurrent Units for Sequential Recommender Systems
City University of Hong Kong（香港城市大学）；High Magnetic Field Laboratory, Chinese Academy of Sciences（中国科学院强磁场科学中心）；Southern University of Science and Technology（南方科技大学）；Jinan University（暨南大学）；University of Science and Technology of China（中国科学技术大学）
Yonsei University（延世大学）；Chungnam National University（忠南国立大学）
In recent years, many recommender systems using network embedding (NE) such as graph neural networks (GNNs) have been extensively studied in the sense of improving recommendation accuracy. However, such attempts have focused mostly on utilizing only the information of positive user-item interactions with high ratings. Thus, there is a challenge on how to make use of low rating scores for representing users' preferences since low ratings can be still informative in designing NE-based recommender systems. In this study, we present SiReN, a new sign-aware recommender system based on GNN models. S…
ZBW-Leibniz Information Centre for Economics（莱布尼茨经济信息中心）；Indian Institute of Technology Patna（印度理工学院巴特那分校）
Traditional Collaborative Filtering (CF) based methods are applied to understand the personal preferences of users/customers for items or products from the rating matrix. Usually, the rating matrix is sparse in nature. So there are some improved variants of the CF method that apply the increasing amount of side information to handle the sparsity problem. Only linear kernel or only non-linear kernel is applied in most of the available recommendation-related work to understand user-item latent feature embeddings from data. Only linear kernel or only non-linear kernel is not sufficient to learn c…
Sequential recommendation (SR) plays an important role in personalized recommender systems because it captures dynamic and diverse preferences from users' real-time increasing behaviors. Unlike the standard autoregressive training strategy, future data (also available during training) has been used to facilitate model training as it provides richer signals about user's current interests and can be used to improve the recommendation quality. However, these methods suffer from a severe training-inference gap, i.e., both past and future contexts are modeled by the same encoder when training, whil…
Cross domain recommendation (CDR) is one popular research topic in recommender systems. This paper focuses on a popular scenario for CDR where different domains share the same set of users but no overlapping items. The majority of recent methods have explored the shared-user representation to transfer knowledge across domains. However, the idea of shared-user representation resorts to learn the overlapped features of user preferences and suppresses the domain-specific features. Other works try to capture the domain-specific features by an MLP mapping but require heuristic human knowledge of ch…
Internet is changing the world, adapting to the trend of internet sales will bring revenue to traditional insurance companies. Online insurance is still in its early stages of development, where cold start problem (prospective customer) is one of the greatest challenges. In traditional e-commerce field, several cross-domain recommendation (CDR) methods have been studied to infer preferences of cold start users based on their preferences in other domains. However, these CDR methods could not be applied to insurance domain directly due to the domain specific properties. In this paper, we propose…
PHKT:Personalized Dynamic Hypergraph-enhanced KAN-Transformer for Multi-behavior Sequential Recommendation
Hangzhou Dianzi University（杭州电子科技大学）；School of Computer Science and Technology, Hangzhou Dianzi University（杭州电子科技大学计算机科学与技术学院）；Sanya Traditional Chinese Medicine Hospital（三亚市中医医院）；Combat Support College, Rocket Force University of Engineering（火箭军工程大学勤务学院）；Fuxi AI Lab, NetEase Games, NetEase Inc.（网易伏羲实验室，网易游戏，网易公司）
NAVER CLOVA；NAVER AI LAB（Naver AI实验室）；NAVER R&D Center（Naver研发中心）
General-purpose representation learning through large-scale pre-training has shown promising results in the various machine learning fields. For an e-commerce domain, the objective of general-purpose, i.e., one for all, representations would be efficient applications for extensive downstream tasks such as user profiling, targeting, and recommendation tasks. In this paper, we systematically compare the generalizability of two learning strategies, i.e., transfer learning through the proposed model, ShopperBERT, vs. learning from scratch. ShopperBERT learns nine pretext tasks with 79.2M parameter…
