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Leyu Lin

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8 papers
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8

AAAI Conference 2024 Conference Paper

Plug-In Diffusion Model for Sequential Recommendation

  • Haokai Ma
  • Ruobing Xie
  • Lei Meng
  • Xin Chen
  • Xu Zhang
  • Leyu Lin
  • Zhanhui Kang

Pioneering efforts have verified the effectiveness of the diffusion models in exploring the informative uncertainty for recommendation. Considering the difference between recommendation and image synthesis tasks, existing methods have undertaken tailored refinements to the diffusion and reverse process. However, these approaches typically use the highest-score item in corpus for user interest prediction, leading to the ignorance of the user's generalized preference contained within other items, thereby remaining constrained by the data sparsity issue. To address this issue, this paper presents a novel Plug-in Diffusion Model for Recommendation (PDRec) framework, which employs the diffusion model as a flexible plugin to jointly take full advantage of the diffusion-generating user preferences on all items. Specifically, PDRec first infers the users' dynamic preferences on all items via a time-interval diffusion model and proposes a Historical Behavior Reweighting (HBR) mechanism to identify the high-quality behaviors and suppress noisy behaviors. In addition to the observed items, PDRec proposes a Diffusion-based Positive Augmentation (DPA) strategy to leverage the top-ranked unobserved items as the potential positive samples, bringing in informative and diverse soft signals to alleviate data sparsity. To alleviate the false negative sampling issue, PDRec employs Noise-free Negative Sampling (NNS) to select stable negative samples for ensuring effective model optimization. Extensive experiments and analyses on four datasets have verified the superiority of the proposed PDRec over the state-of-the-art baselines and showcased the universality of PDRec as a flexible plugin for commonly-used sequential encoders in different recommendation scenarios. The code is available in https://github.com/hulkima/PDRec.

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AAAI Conference 2021 Conference Paper

Hierarchical Reinforcement Learning for Integrated Recommendation

  • Ruobing Xie
  • Shaoliang Zhang
  • Rui Wang
  • Feng Xia
  • Leyu Lin

Integrated recommendation aims to jointly recommend heterogeneous items in the main feed from different sources via multiple channels, which needs to capture user preferences on both item and channel levels. It has been widely used in practical systems by billions of users, while few works concentrate on the integrated recommendation systematically. In this work, we propose a novel Hierarchical reinforcement learning framework for integrated recommendation (HRL-Rec), which divides the integrated recommendation into two tasks to recommend channels and items sequentially. The low-level agent is a channel selector, which generates a personalized channel list. The high-level agent is an item recommender, which recommends specific items from heterogeneous channels under the channel constraints. We design various rewards for both recommendation accuracy and diversity, and propose four losses for fast and stable model convergence. We also conduct an online exploration for sufficient training. In experiments, we conduct extensive offline and online experiments on a billion-level real-world dataset to show the effectiveness of HRL-Rec. HRL-Rec has also been deployed on WeChat Top Stories, affecting millions of users. The source codes are released in https: //github. com/modriczhang/HRL-Rec.

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ECAI Conference 2020 Conference Paper

Capturing Attraction Distribution: Sequential Attentive Network for Dwell Time Prediction

  • Tianxin Wang
  • Jingwu Chen
  • Fuzhen Zhuang
  • Leyu Lin
  • Feng Xia 0006
  • Lihuan Du
  • Qing He 0003

In article recommendation, the dwell time is an important metric to measure user engagement on content and has been widely used as a proxy for user satisfaction. Therefore, predicting the dwell time is very helpful for making better recommendations and improving user experience. Modeling the interaction between user and content is the key for dwell time prediction. However, conventional methods usually model the content with document-level representation in a non-personalized way, which ignores the natural reading process of the reader and the reader attraction in sub-document level, this might lead to a bias for analyzing the user reading behavior. Since the attraction level of different parts is different for the user, the user attention changes dynamically while reading. The former content affects the reading for the latter content via the change of attraction level. Therefore, considering the attraction level of each part of the article, i. e. , attraction distribution, is quite necessary for content modeling. In this paper, we propose the Sequential Attentive Network (SAN) for dwell time prediction, which effectively models the attraction distribution of the article reading process. We collect the data from WeChat, a widely-used mobile app in China, for experiments. The results demonstrate the advantages of our model over several competitive baselines on dwell time prediction.

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IJCAI Conference 2020 Conference Paper

Deep Feedback Network for Recommendation

  • Ruobing Xie
  • Cheng Ling
  • Yalong Wang
  • Rui Wang
  • Feng Xia
  • Leyu Lin

Both explicit and implicit feedbacks can reflect user opinions on items, which are essential for learning user preferences in recommendation. However, most current recommendation algorithms merely focus on implicit positive feedbacks (e. g. , click), ignoring other informative user behaviors. In this paper, we aim to jointly consider explicit/implicit and positive/negative feedbacks to learn user unbiased preferences for recommendation. Specifically, we propose a novel Deep feedback network (DFN) modeling click, unclick and dislike behaviors. DFN has an internal feedback interaction component that captures fine-grained interactions between individual behaviors, and an external feedback interaction component that uses precise but relatively rare feedbacks (click/dislike) to extract useful information from rich but noisy feedbacks (unclick). In experiments, we conduct both offline and online evaluations on a real-world recommendation system WeChat Top Stories used by millions of users. The significant improvements verify the effectiveness and robustness of DFN. The source code is in https: //github. com/qqxiaochongqq/DFN.

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IJCAI Conference 2020 Conference Paper

Internal and Contextual Attention Network for Cold-start Multi-channel Matching in Recommendation

  • Ruobing Xie
  • Zhijie Qiu
  • Jun Rao
  • Yi Liu
  • Bo Zhang
  • Leyu Lin

Real-world integrated personalized recommendation systems usually deal with millions of heterogeneous items. It is extremely challenging to conduct full corpus retrieval with complicated models due to the tremendous computation costs. Hence, most large-scale recommendation systems consist of two modules: a multi-channel matching module to efficiently retrieve a small subset of candidates, and a ranking module for precise personalized recommendation. However, multi-channel matching usually suffers from cold-start problems when adding new channels or new data sources. To solve this issue, we propose a novel Internal and contextual attention network (ICAN), which highlights channel-specific contextual information and feature field interactions between multiple channels. In experiments, we conduct both offline and online evaluations with case studies on a real-world integrated recommendation system. The significant improvements confirm the effectiveness and robustness of ICAN, especially for cold-start channels. Currently, ICAN has been deployed on WeChat Top Stories used by millions of users. The source code can be obtained from https: //github. com/zhijieqiu/ICAN.

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AAAI Conference 2020 Conference Paper

Neural Snowball for Few-Shot Relation Learning

  • Tianyu Gao
  • Xu Han
  • Ruobing Xie
  • Zhiyuan Liu
  • Fen Lin
  • Leyu Lin
  • Maosong Sun

Knowledge graphs typically undergo open-ended growth of new relations. This cannot be well handled by relation extraction that focuses on pre-defined relations with sufficient training data. To address new relations with few-shot instances, we propose a novel bootstrapping approach, Neural Snowball, to learn new relations by transferring semantic knowledge about existing relations. More specifically, we use Relational Siamese Networks (RSN) to learn the metric of relational similarities between instances based on existing relations and their labeled data. Afterwards, given a new relation and its few-shot instances, we use RSN to accumulate reliable instances from unlabeled corpora; these instances are used to train a relation classifier, which can further identify new facts of the new relation. The process is conducted iteratively like a snowball. Experiments show that our model can gather high-quality instances for better fewshot relation learning and achieves significant improvement compared to baselines. Codes and datasets are released on https: //github. com/thunlp/Neural-Snowball.

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ICLR Conference 2020 Conference Paper

Towards Fast Adaptation of Neural Architectures with Meta Learning

  • Dongze Lian
  • Yin Zheng
  • Yintao Xu
  • Yanxiong Lu
  • Leyu Lin
  • Peilin Zhao
  • Junzhou Huang
  • Shenghua Gao

Recently, Neural Architecture Search (NAS) has been successfully applied to multiple artificial intelligence areas and shows better performance compared with hand-designed networks. However, the existing NAS methods only target a specific task. Most of them usually do well in searching an architecture for single task but are troublesome for multiple datasets or multiple tasks. Generally, the architecture for a new task is either searched from scratch, which is neither efficient nor flexible enough for practical application scenarios, or borrowed from the ones searched on other tasks, which might be not optimal. In order to tackle the transferability of NAS and conduct fast adaptation of neural architectures, we propose a novel Transferable Neural Architecture Search method based on meta-learning in this paper, which is termed as T-NAS. T-NAS learns a meta-architecture that is able to adapt to a new task quickly through a few gradient steps, which makes the transferred architecture suitable for the specific task. Extensive experiments show that T-NAS achieves state-of-the-art performance in few-shot learning and comparable performance in supervised learning but with 50x less searching cost, which demonstrates the effectiveness of our method.

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AAAI Conference 2018 Conference Paper

Does William Shakespeare REALLY Write Hamlet? Knowledge Representation Learning With Confidence

  • Ruobing Xie
  • Zhiyuan Liu
  • Fen Lin
  • Leyu Lin

Knowledge graphs (KGs), which could provide essential relational information between entities, have been widely utilized in various knowledge-driven applications. Since the overall human knowledge is innumerable that still grows explosively and changes frequently, knowledge construction and update inevitably involve automatic mechanisms with less human supervision, which usually bring in plenty of noises and conflicts to KGs. However, most conventional knowledge representation learning methods assume that all triple facts in existing KGs share the same significance without any noises. To address this problem, we propose a novel confidence-aware knowledge representation learning framework (CKRL), which detects possible noises in KGs while learning knowledge representations with confidence simultaneously. Specifically, we introduce the triple confidence to conventional translation-based methods for knowledge representation learning. To make triple confidence more flexible and universal, we only utilize the internal structural information in KGs, and propose three kinds of triple con- fidences considering both local and global structural information. In experiments, We evaluate our models on knowledge graph noise detection, knowledge graph completion and triple classification. Experimental results demonstrate that our confidence-aware models achieve significant and consistent improvements on all tasks, which confirms the capability of CKRL modeling confidence with structural information in both KG noise detection and knowledge representation learning.

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