【论文推荐】最新5篇推荐系统相关论文—文档向量矩阵分解、异构网络融合、树结构深度模型、深度强化学习、负二项矩阵分解

【导读】专知内容组整理了最近五篇推荐系统(Recommender System)相关文章,为大家进行介绍,欢迎查看!

1. ParVecMF: A Paragraph Vector-based Matrix Factorization Recommender System(ParVecMF:基于文档向量矩阵分解模型的推荐系统)



作者:Georgios Alexandridis,Georgios Siolas,Andreas Stafylopatis

摘要:Review-based recommender systems have gained noticeable ground in recent years. In addition to the rating scores, those systems are enriched with textual evaluations of items by the users. Neural language processing models, on the other hand, have already found application in recommender systems, mainly as a means of encoding user preference data, with the actual textual description of items serving only as side information. In this paper, a novel approach to incorporating the aforementioned models into the recommendation process is presented. Initially, a neural language processing model and more specifically the paragraph vector model is used to encode textual user reviews of variable length into feature vectors of fixed length. Subsequently this information is fused along with the rating scores in a probabilistic matrix factorization algorithm, based on maximum a-posteriori estimation. The resulting system, ParVecMF, is compared to a ratings' matrix factorization approach on a reference dataset. The obtained preliminary results on a set of two metrics are encouraging and may stimulate further research in this area.

期刊:arXiv, 2018年1月10日

网址

http://www.zhuanzhi.ai/document/931302eaf239f8c7d006bce0405b3682

2. Learning with Heterogeneous Side Information Fusion for Recommender Systems(基于异构网络融合模型的推荐系统)



作者:Huan Zhao,Quanming Yao,Yangqiu Song,James Kwok,Dik Lun Lee

摘要:Recommender System (RS) is a hot area where artificial intelligence (AI) techniques can be effectively applied to improve performance. Since the well-known Netflix Challenge, collaborative filtering (CF) has become the most popular and effective recommendation method. Despite their success in CF, various AI techniques still have to face the data sparsity and cold start problems. Previous works tried to solve these two problems by utilizing auxiliary information, such as social connections among users and meta-data of items. However, they process different types of information separately, leading to information loss. In this work, we propose to utilize Heterogeneous Information Network (HIN), which is a natural and general representation of different types of data, to enhance CF-based recommending methods. HIN-based recommender systems face two problems: how to represent high-level semantics for recommendation and how to fuse the heterogeneous information to recommend. To address these problems, we propose to applying meta-graph to HIN-based RS and solve the information fusion problem with a "matrix factorization (MF) + factorization machine (FM)" framework. For the "MF" part, we obtain user-item similarity matrices from each meta-graph and adopt low-rank matrix approximation to get latent features for both users and items. For the "FM" part, we propose to apply FM with Group lasso (FMG) on the obtained features to simultaneously predict missing ratings and select useful meta-graphs. Experimental results on two large real-world datasets, i.e., Amazon and Yelp, show that our proposed approach is better than that of the state-of-the-art FM and other HIN-based recommending methods.

期刊:arXiv, 2018年1月8日

网址

http://www.zhuanzhi.ai/document/4b7b3b4e73a07bb466ed2a3f7584f714

3. Learning Tree-based Deep Model for Recommender Systems(基于树结构深度模型的推荐系统)



作者:Han Zhu,Pengye Zhang,Guozheng Li,Jie He,Han Li,Kun Gai

摘要:We propose a novel recommendation method based on tree. With user behavior data, the tree based model can capture user interests from coarse to fine, by traversing nodes top down and make decisions whether to pick up each node to user. Compared to traditional model-based methods like matrix factorization (MF), our tree based model does not have to fetch and estimate each item in the entire set. Instead, candidates are drawn from subsets corresponding to user's high-level interests, which is defined by the tree structure. Meanwhile, finding candidates from the entire corpus brings more novelty than content-based approaches like item-based collaborative filtering.Moreover, in this paper, we show that the tree structure can also act to refine user interests distribution, to benefit both training and prediction. The experimental results in both open dataset and Taobao display advertising dataset indicate that the proposed method outperforms existing methods.

期刊:arXiv, 2018年1月8日

网址

http://www.zhuanzhi.ai/document/062f817e4155bf2a537ea5e97f7360e8

4. Deep Reinforcement Learning for List-wise Recommendations(基于深度强化学习的List-wise推荐)



作者:Xiangyu Zhao,Liang Zhang,Zhuoye Ding,Dawei Yin,Yihong Zhao,Jiliang Tang

摘要:Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process and make recommendations following a fixed strategy. In this paper, we propose a novel recommender system with the capability of continuously improving its strategies during the interactions with users. We model the sequential interactions between users and a recommender system as a Markov Decision Process (MDP) and leverage Reinforcement Learning (RL) to automatically learn the optimal strategies via recommending trial-and-error items and receiving reinforcements of these items from users' feedbacks. In particular, we introduce an online user-agent interacting environment simulator, which can pre-train and evaluate model parameters offline before applying the model online. Moreover, we validate the importance of list-wise recommendations during the interactions between users and agent, and develop a novel approach to incorporate them into the proposed framework LIRD for list-wide recommendations. The experimental results based on a real-world e-commerce dataset demonstrate the effectiveness of the proposed framework.

期刊:arXiv, 2018年1月5日

网址

http://www.zhuanzhi.ai/document/3e5f8162f0da62cc72a3ca0dde48598a

5. Negative Binomial Matrix Factorization for Recommender Systems(基于负二项矩阵分解模型的推荐系统)



作者:Olivier Gouvert,Thomas Oberlin,Cédric Févotte

摘要:We introduce negative binomial matrix factorization (NBMF), a matrix factorization technique specially designed for analyzing over-dispersed count data. It can be viewed as an extension of Poisson matrix factorization (PF) perturbed by a multiplicative term which models exposure. This term brings a degree of freedom for controlling the dispersion, making NBMF more robust to outliers. We show that NBMF allows to skip traditional pre-processing stages, such as binarization, which lead to loss of information. Two estimation approaches are presented: maximum likelihood and variational Bayes inference. We test our model with a recommendation task and show its ability to predict user tastes with better precision than PF.

期刊:arXiv, 2018年1月5日

网址

http://www.zhuanzhi.ai/document/dc54ec19b1018f6750db6f23d9d50f1b

原文发布于微信公众号 - 专知(Quan_Zhuanzhi)

原文发表时间:2018-01-28

本文参与腾讯云自媒体分享计划,欢迎正在阅读的你也加入,一起分享。

发表于

我来说两句

0 条评论
登录 后参与评论

相关文章

来自专栏CSDN技术头条

【基础】常用的机器学习&数据挖掘知识点

Basis(基础): MSE(Mean Square Error均方误差),LMS(LeastMean Square最小均方),LSM(Least Square...

32480
来自专栏专知

【论文推荐】最新八篇强化学习相关论文—残差网络、QMIX、元学习、动态速率分配、分层强化学习、抽象概况、快速物体检测、SOM

【导读】专知内容组整理了最近八篇强化学习(Reinforcement learning)相关文章,为大家进行介绍,欢迎查看! 1.BlockDrop: Dyna...

56550
来自专栏AI研习社

126篇殿堂级深度学习论文分类整理 从入门到应用(上)

█ 如果你有非常大的决心从事深度学习,又不想在这一行打酱油,那么研读大牛论文将是不可避免的一步。而作为新人,你的第一个问题或许是:“论文那么多,从哪一篇读起?”...

36880
来自专栏专知

【论文推荐】最新八篇机器翻译相关论文—自注意力残差解码器、条件序列生成式对抗网络、检索译文、域自适应、细粒度注意力机制

30140
来自专栏专知

【论文推荐】最新六篇图像描述生成相关论文—字符级推断、视觉解释、语义对齐、实体感知、确定性非自回归

25970
来自专栏专知

【论文推荐】最新六篇生成式对抗网络(GAN)相关论文—半监督学习、对偶、交互生成对抗网络、激活、纳什均衡、tempoGAN

【导读】专知内容组整理了最近六篇生成式对抗网络(GAN)相关文章,为大家进行介绍,欢迎查看! 1. Exploiting the potential of un...

50590
来自专栏专知

【论文推荐】最新十二篇情感分析相关论文—自然语言推理框架、网络事件、多任务学习、实时情感变化检测、多因素分析、深度语境词表示

25360
来自专栏AI研习社

126篇殿堂级深度学习论文分类整理 从入门到应用(下)

AI 研习社:本文接“126篇殿堂级深度学习论文分类整理 从入门到应用(上)”,是该整理的下半部分,即应用篇;按照各应用领域对论文进行分类。 3 应用 3.1 ...

32660
来自专栏数据科学学习手札

(数据科学学习手札20)主成分分析原理推导&Python自编函数实现

主成分分析(principal component analysis,简称PCA)是一种经典且简单的机器学习算法,其主要目的是用较少的变量去解释原来资料中的大部...

43970
来自专栏专知

【论文推荐】最新八篇目标跟踪相关论文—自适应相关滤波、因果关系图模型、TrackingNet、ClickBAIT、图像矩模型

【导读】专知内容组整理了最近八篇目标跟踪(Object Tracking)相关文章,为大家进行介绍,欢迎查看! 1. Adaptive Correlation ...

49880

扫码关注云+社区

领取腾讯云代金券