【论文推荐】最新七篇推荐系统相关论文—影响兴趣、知识Embeddings、 音乐推荐、非结构化、一致性、显式和隐式特征、知识图谱

【导读】专知内容组整理了最近七篇推荐系统(Recommender System)相关文章,为大家进行介绍,欢迎查看!

1.Learning Recommendations While Influencing Interests(在影响兴趣的同时学习推荐)


作者:Rahul Meshram,D. Manjunath,Nikhil Karamchandani

摘要:Personalized recommendation systems (RS) are extensively used in many services. Many of these are based on learning algorithms where the RS uses the recommendation history and the user response to learn an optimal strategy. Further, these algorithms are based on the assumption that the user interests are rigid. Specifically, they do not account for the effect of learning strategy on the evolution of the user interests. In this paper we develop influence models for a learning algorithm that is used to optimally recommend websites to web users. We adapt the model of \cite{Ioannidis10} to include an item-dependent reward to the RS from the suggestions that are accepted by the user. For this we first develop a static optimisation scheme when all the parameters are known. Next we develop a stochastic approximation based learning scheme for the RS to learn the optimal strategy when the user profiles are not known. Finally, we describe several user-influence models for the learning algorithm and analyze their effect on the steady user interests and on the steady state optimal strategy as compared to that when the users are not influenced.

期刊:arXiv, 2018年3月23日

网址

http://www.zhuanzhi.ai/document/513db2a850d4db1d1ce478b1ffd40353

2.Learning over Knowledge-Base Embeddings for Recommendation(在基于知识embeddings的基础上学习推荐)


作者:Yongfeng Zhang,Qingyao Ai,Xu Chen,Pengfei Wang

机构:Tsinghua University,Beijing University of Posts and Telecommunications

摘要:State-of-the-art recommendation algorithms -- especially the collaborative filtering (CF) based approaches with shallow or deep models -- usually work with various unstructured information sources for recommendation, such as textual reviews, visual images, and various implicit or explicit feedbacks. Though structured knowledge bases were considered in content-based approaches, they have been largely neglected recently due to the availability of vast amount of data, and the learning power of many complex models. However, structured knowledge bases exhibit unique advantages in personalized recommendation systems. When the explicit knowledge about users and items is considered for recommendation, the system could provide highly customized recommendations based on users' historical behaviors. A great challenge for using knowledge bases for recommendation is how to integrated large-scale structured and unstructured data, while taking advantage of collaborative filtering for highly accurate performance. Recent achievements on knowledge base embedding sheds light on this problem, which makes it possible to learn user and item representations while preserving the structure of their relationship with external knowledge. In this work, we propose to reason over knowledge base embeddings for personalized recommendation. Specifically, we propose a knowledge base representation learning approach to embed heterogeneous entities for recommendation. Experimental results on real-world dataset verified the superior performance of our approach compared with state-of-the-art baselines.

期刊:arXiv, 2018年3月23日

网址

http://www.zhuanzhi.ai/document/4a7f93d4541c08412376c92c7ac72f98

3.Current Challenges and Visions in Music Recommender Systems Research(音乐推荐系统研究中的当前挑战与展望)


作者:Markus Schedl,Hamed Zamani,Ching-Wei Chen,Yashar Deldjoo,Mehdi Elahi

机构:Johannes Kepler University,University of MassachuseŠs

摘要:Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field.

期刊:arXiv, 2018年3月22日

网址

http://www.zhuanzhi.ai/document/040f1775628bf7d35d98d8b04c817161

4.Product Characterisation towards Personalisation: Learning Attributes from Unstructured Data to Recommend Fashion Products(个性化的产品特性:从非结构化数据中学习属性来推荐时尚产品)


作者:Ângelo Cardoso,Fabio Daolio,Saúl Vargas

机构:Universidade de Lisboa

摘要:In this paper, we describe a solution to tackle a common set of challenges in e-commerce, which arise from the fact that new products are continually being added to the catalogue. The challenges involve properly personalising the customer experience, forecasting demand and planning the product range. We argue that the foundational piece to solve all of these problems is having consistent and detailed information about each product, information that is rarely available or consistent given the multitude of suppliers and types of products. We describe in detail the architecture and methodology implemented at ASOS, one of the world's largest fashion e-commerce retailers, to tackle this problem. We then show how this quantitative understanding of the products can be leveraged to improve recommendations in a hybrid recommender system approach.

期刊:arXiv, 2018年3月21日

网址

http://www.zhuanzhi.ai/document/e187e84da06667f00a4cefcde5667d32

5.Similar but Different: Exploiting Users' Congruity for Recommendation Systems(类似但不同:利用用户对推荐系统的一致性)


作者:Ghazaleh Beigi,Huan Liu

机构:Arizona State University

摘要:The pervasive use of social media provides massive data about individuals' online social activities and their social relations. The building block of most existing recommendation systems is the similarity between users with social relations, i.e., friends. While friendship ensures some homophily, the similarity of a user with her friends can vary as the number of friends increases. Research from sociology suggests that friends are more similar than strangers, but friends can have different interests. Exogenous information such as comments and ratings may help discern different degrees of agreement (i.e., congruity) among similar users. In this paper, we investigate if users' congruity can be incorporated into recommendation systems to improve it's performance. Experimental results demonstrate the effectiveness of embedding congruity related information into recommendation systems.

期刊:arXiv, 2018年3月16日

网址

http://www.zhuanzhi.ai/document/c79c87c517160b413d44dd40805ba0c9

6.xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems(xDeepFM: 结合显式和隐式特征交互的推荐系统)


作者:Jianxun Lian,Xiaohuan Zhou,Fuzheng Zhang,Zhongxia Chen,Xing Xie,Guangzhong Sun

机构:University of Science and Technology,Beijing University of Posts and Telecommunications

摘要:Combinatorial features are essential for the success of many commercial models. Manually crafting these features usually comes with high cost due to the variety, volume and velocity of raw data in web-scale systems. Factorization based models, which measure interactions in terms of vector product, can learn patterns of combinatorial features automatically and generalize to unseen features as well. With the great success of deep neural works (DNNs) in various fields, recently researchers have proposed several DNN-based factorization model to learn both low- and high-order feature interactions. Despite the powerful ability of learning an arbitrary function from data, plain DNNs generate feature interactions implicitly and at the bit-wise level. In this paper, we propose a novel Compressed Interaction Network (CIN), which aims to generate feature interactions in an explicit fashion and at the vector-wise level. We show that the CIN share some functionalities with convolutional neural networks (CNNs) and recurrent neural networks (RNNs). We further combine a CIN and a classical DNN into one unified model, and named this new model eXtreme Deep Factorization Machine (xDeepFM). On one hand, the xDeepFM is able to learn certain bounded-degree feature interactions explicitly; on the other hand, it can learn arbitrary low- and high-order feature interactions implicitly. We conduct comprehensive experiments on three real-world datasets. Our results demonstrate that xDeepFM outperforms state-of-the-art models. We have released the source code of xDeepFM at https://github.com/Leavingseason/xDeepFM.

期刊:arXiv, 2018年3月15日

网址

http://www.zhuanzhi.ai/document/771b19587b0b9da76ce1e836d06e59b6

7.Ripple Network: Propagating User Preferences on the Knowledge Graph for Recommender Systems(波纹网络:在推荐系统的知识图上传播用户偏好)


作者:Hongwei Wang,Fuzheng Zhang,Jialin Wang,Miao Zhao,Wenjie Li,Xing Xie,Minyi Guo

机构:Shanghai Jiao Tong University,The Hong Kong Polytechnic University

摘要:To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance. This paper considers the knowledge graph as the source of side information. To address the limitations of existing embedding-based and path-based methods for knowledge-graph-aware recommendation, we propose Ripple Network, an end-to-end framework that naturally incorporates the knowledge graph into recommender systems. Similar to actual ripples propagating on the surface of water, Ripple Network stimulates the propagation of user preferences over the set of knowledge entities by automatically and iteratively extending a user's potential interests along links in the knowledge graph. The multiple "ripples" activated by a user's historically clicked items are thus superposed to form the preference distribution of the user with respect to a candidate item, which could be used for predicting the final clicking probability. Through extensive experiments on real-world datasets, we demonstrate that Ripple Network achieves substantial gains in a variety of scenarios, including movie, book and news recommendation, over several state-of-the-art baselines.

期刊:arXiv, 2018年3月9日

网址:http://www.zhuanzhi.ai/document/ec777791a9d080123eb5e32299007615

原文发布于微信公众号 - 专知(Quan_Zhuanzhi)

原文发表时间:2018-03-28

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

来自专栏专知

【论文推荐】最新5篇语音识别(ASR)相关论文—音频对抗样本、对抗性语音识别系统、声学模型、序列到序列、口语可理解性矫正

【导读】专知内容组整理了最近五篇语音识别(Automatic Speech Recognition, ASR)相关文章,为大家进行介绍,欢迎查看! 1. Aud...

5894
来自专栏专知

【论文推荐】最新七篇聊天机器人相关论文—社交媒体、远程医疗、序列理解、食物推荐、知识学习引擎、终生交互知识学习、任务管理

【导读】专知内容组整理了最近七篇聊天机器人(Chatbot)相关文章,为大家进行介绍,欢迎查看! 1.Touch Your Heart: A Tone-awar...

41210
来自专栏专知

【论文推荐】最新八篇视频描述生成相关论文—在线视频理解、联合定位和描述事件、生成视频、跨模态注意力机制、联合事件检测和描述

【导读】专知内容组整理近期八篇视频描述生成(Video Captioning)相关文章,为大家进行介绍,欢迎查看!

1995
来自专栏生信小驿站

主成分分析①

principal() 含多种可选的方差旋转方法的主成分分析 fa() 可用主轴、最小残差、加权最小平方或最大似然法估计的因子分析 fa.paralle...

912
来自专栏AI科技评论

动态 | NIPS 2017 今天开启议程,谷歌科学家竟然组团去了450人,还都不是去玩的!

AI 科技评论按:据说,别人去NIPS 2017是这样的: ? 而谷歌去NIPS 2017是这样的: ? 今天,人工智能领域本年度最后一个学术盛会、机器学习领域...

2303
来自专栏专知

【论文推荐】最新七篇推荐系统相关论文—协同度量学习、SQL-Rank、用户行为与神经网络、隐私价格、贝叶斯、 IoT、序列感知

【导读】专知内容组整理了最近七篇推荐系统(Recommender System)相关文章,为大家进行介绍,欢迎查看! 1. Collaborative Metr...

5528
来自专栏一心无二用,本人只专注于基础图像算法的实现与优化。

关于Cewu Lu等的《Combining Sketch and Tone for Pencil Drawing Production》一文铅笔画算法的理解和笔录。

相关论文的链接:Combining Sketch and Tone for Pencil Drawing Production        第一次看《Com...

4139
来自专栏专知

【论文Slides分享】多媒体顶级会议ACM Multimedia 2017 China 论文宣讲会报告Slides资料分享

【导读】第25届ACM国际多媒体会议(ACM International Conference on Multimedia, 简称ACMMM)于2017年10月...

4016
来自专栏专知

【论文推荐】最新七篇推荐系统相关论文—正则化奇异值、用户视角、CTR预测、Top-k、人机交互、隐反馈

1722
来自专栏专知

【重磅】深度学习顶会ICLR2018评审结果出炉,一文快速了解评审分析简报和评分最高的十篇论文

【导读】ICLR,全称为「International Conference on Learning Representations」(国际学习表征会议),201...

3385

扫码关注云+社区

领取腾讯云代金券