RNE: A Scalable Network Embedding for Billion-scale Recommendation

• URL: http://arxiv.org/abs/2003.07158v2
• Date: Thu, 9 Apr 2020 06:47:20 GMT
• Title: RNE: A Scalable Network Embedding for Billion-scale Recommendation
• Authors: Jianbin Lin, Daixin Wang, Lu Guan, Yin Zhao, Binqiang Zhao, Jun Zhou, Xiaolong Li, and Yuan Qi
• Abstract summary: We propose RNE, a data-efficient Recommendation-based Network Embedding method, to give personalized and diverse items to users. On the one hand, the method is able to preserve the local structure between the users and items while modeling the diversity and dynamic property of the user interest to boost the recommendation quality. We deploy RNE on a recommendation scenario of Taobao, the largest E-commerce platform in China, and train it on a billion-scale user-item graph.
• Score: 21.6366085346674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nowadays designing a real recommendation system has been a critical problem for both academic and industry. However, due to the huge number of users and items, the diversity and dynamic property of the user interest, how to design a scalable recommendation system, which is able to efficiently produce effective and diverse recommendation results on billion-scale scenarios, is still a challenging and open problem for existing methods. In this paper, given the user-item interaction graph, we propose RNE, a data-efficient Recommendation-based Network Embedding method, to give personalized and diverse items to users. Specifically, we propose a diversity- and dynamics-aware neighbor sampling method for network embedding. On the one hand, the method is able to preserve the local structure between the users and items while modeling the diversity and dynamic property of the user interest to boost the recommendation quality. On the other hand the sampling method can reduce the complexity of the whole method theoretically to make it possible for billion-scale recommendation. We also implement the designed algorithm in a distributed way to further improves its scalability. Experimentally, we deploy RNE on a recommendation scenario of Taobao, the largest E-commerce platform in China, and train it on a billion-scale user-item graph. As is shown on several online metrics on A/B testing, RNE is able to achieve both high-quality and diverse results compared with CF-based methods. We also conduct the offline experiments on Pinterest dataset comparing with several state-of-the-art recommendation methods and network embedding methods. The results demonstrate that our method is able to produce a good result while runs much faster than the baseline methods.

Related papers

• Unleashing the Potential of Multi-Channel Fusion in Retrieval for Personalized Recommendations [33.79863762538225]
  A key challenge in Recommender systems (RS) is efficiently processing vast item pools to deliver highly personalized recommendations under strict latency constraints. In this paper, we explore advanced channel fusion strategies by assigning systematically optimized weights to each channel. Our methods enhance both personalization and flexibility, achieving significant performance improvements across multiple datasets and yielding substantial gains in real-world deployments.
  arXiv  Detail & Related papers  (2024-10-21T14:58:38Z)
• Evaluating Ensemble Methods for News Recommender Systems [50.90330146667386]
  This paper demonstrates how ensemble methods can be used to combine many diverse state-of-the-art algorithms to achieve superior results on the Microsoft News dataset (MIND) Our findings demonstrate that a combination of NRS algorithms can outperform individual algorithms, provided that the base learners are sufficiently diverse.
  arXiv  Detail & Related papers  (2024-06-23T13:40:50Z)
• Mirror Gradient: Towards Robust Multimodal Recommender Systems via Exploring Flat Local Minima [54.06000767038741]
  We analyze multimodal recommender systems from the novel perspective of flat local minima. We propose a concise yet effective gradient strategy called Mirror Gradient (MG) We find that the proposed MG can complement existing robust training methods and be easily extended to diverse advanced recommendation models.
  arXiv  Detail & Related papers  (2024-02-17T12:27:30Z)
• Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Methods [52.0617030129699]
  We introduce a novel theoretical framework for analyzing the effectiveness of DeepMatching Networks and Reinforcement Learning methods. Our main contribution holds for a broad class of problems including Max-and Min-Cut, Max-$k$-Bipartite-Bi, Maximum-Weight-Bipartite-Bi, and Traveling Salesman Problem. As a byproduct of our analysis we introduce a novel regularization process over vanilla descent and provide theoretical and experimental evidence that it helps address vanishing-gradient issues and escape bad stationary points.
  arXiv  Detail & Related papers  (2023-10-08T23:39:38Z)
• Interactive Graph Convolutional Filtering [79.34979767405979]
  Interactive Recommender Systems (IRS) have been increasingly used in various domains, including personalized article recommendation, social media, and online advertising. These problems are exacerbated by the cold start problem and data sparsity problem. Existing Multi-Armed Bandit methods, despite their carefully designed exploration strategies, often struggle to provide satisfactory results in the early stages. Our proposed method extends interactive collaborative filtering into the graph model to enhance the performance of collaborative filtering between users and items.
  arXiv  Detail & Related papers  (2023-09-04T09:02:31Z)
• Towards Universal Sequence Representation Learning for Recommender Systems [98.02154164251846]
  We present a novel universal sequence representation learning approach, named UniSRec. The proposed approach utilizes the associated description text of items to learn transferable representations across different recommendation scenarios. Our approach can be effectively transferred to new recommendation domains or platforms in a parameter-efficient way.
  arXiv  Detail & Related papers  (2022-06-13T07:21:56Z)
• GHRS: Graph-based Hybrid Recommendation System with Application to Movie Recommendation [0.0]
  We propose a recommender system method using a graph-based model associated with the similarity of users' ratings. By utilizing the advantages of Autoencoder feature extraction, we extract new features based on all combined attributes. The experimental results on the MovieLens dataset show that the proposed algorithm outperforms many existing recommendation algorithms on recommendation accuracy.
  arXiv  Detail & Related papers  (2021-11-06T10:47:45Z)
• Sample-Rank: Weak Multi-Objective Recommendations Using Rejection Sampling [0.5156484100374059]
  We introduce a method involving multi-goal sampling followed by ranking for user-relevance (Sample-Rank) to nudge recommendations towards multi-objective goals of the marketplace. The proposed method's novelty is that it reduces the MO recommendation problem to sampling from a desired multi-goal distribution then using it to build a production-friendly learning-to-rank model.
  arXiv  Detail & Related papers  (2020-08-24T09:17:18Z)
• PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest [54.56236567783225]
  PinnerSage is an end-to-end recommender system that represents each user via multi-modal embeddings. We conduct several offline and online A/B experiments to show that our method significantly outperforms single embedding methods.
  arXiv  Detail & Related papers  (2020-07-07T17:13:20Z)
• Recommendation system using a deep learning and graph analysis approach [1.2183405753834562]
  We propose a novel recommendation method based on Matrix Factorization and graph analysis methods. In addition, we leverage deep Autoencoders to initialize users and items latent factors, and deep embedding method gathers users' latent factors from the user trust graph.
  arXiv  Detail & Related papers  (2020-04-17T08:05:33Z)
• Deep Multi-View Enhancement Hashing for Image Retrieval [40.974719473643724]
  This paper proposes a supervised multi-view hash model which can enhance the multi-view information through neural networks. The proposed method is systematically evaluated on the CIFAR-10, NUS-WIDE and MS-COCO datasets.
  arXiv  Detail & Related papers  (2020-02-01T08:32:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.