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Towards Topic-Guided Conversational Recommender System [article]

Kun Zhou, Yuanhang Zhou, Wayne Xin Zhao, Xiaoke Wang, Ji-Rong Wen
2020 arXiv   pre-print
Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations.  ...  Based on TG-ReDial, we present the task of topic-guided conversational recommendation, and propose an effective approach to this task.  ...  I u (a chronologicallyordered sequence of items that u has interacted with).  ... 
arXiv:2010.04125v2 fatcat:hnak5mzgwjhgdeaaytxxb5k22u

DTCRSKG: A Deep Travel Conversational Recommender System Incorporating Knowledge Graph

Hui Fang, Chongcheng Chen, Yunfei Long, Ge Xu, Yongqiang Xiao
2022 Mathematics  
To address these gaps, in the first step of this study, we constructed two human-annotated datasets for the travel conversational recommender system.  ...  We provided two linked data sets, namely, interaction sequence and dialogue data sets.  ...  Acknowledgments: The authors express their acknowledgment to my tutor and my colleagues at Fuzhou University, Minjiang University, and Yunfei Long at the University of Essex for their valuable suggestions  ... 
doi:10.3390/math10091402 fatcat:npubeenmcfcwjozwl6t2yteii4

Advances and Challenges in Conversational Recommender Systems: A Survey [article]

Chongming Gao, Wenqiang Lei, Xiangnan He, Maarten de Rijke, Tat-Seng Chua
2021 arXiv   pre-print
The recent rise of conversational recommender systems (CRSs) changes this situation fundamentally.  ...  Recommender systems exploit interaction history to estimate user preference, having been heavily used in a wide range of industry applications.  ...  Acknowledgments This work is supported by the National Natural Science Foundation of China (U19A2079, 61972372) and the National Key Research and Development Program of China (2020AAA0106000).  ... 
arXiv:2101.09459v6 fatcat:j7djzhrv6fazpogmnj7r4e4f2y

Advances and challenges in conversational recommender systems: A survey

Chongming Gao, Wenqiang Lei, Xiangnan He, Maarten de Rijke, Tat-Seng Chua
2021 AI Open  
The recent rise of conversational recommender systems (CRSs) changes this situation fundamentally.  ...  A B S T R A C T Recommender systems exploit interaction history to estimate user preference, having been heavily used in a wide range of industry applications.  ...  Acknowledgments This work is supported by the National Natural Science Foundation of China (61972372, U19A2079) and the National Key Research and Development Program of China (2020YFB1406703, 2020AAA0106000  ... 
doi:10.1016/j.aiopen.2021.06.002 fatcat:4r26fmsuvjcyla5wycb2ax62ha

COOKIE: A Dataset for Conversational Recommendation over Knowledge Graphs in E-commerce [article]

Zuohui Fu, Yikun Xian, Yaxin Zhu, Yongfeng Zhang, Gerard de Melo
2020 arXiv   pre-print
Then we simulate conversations mirroring the human coarse-to-fine process of choosing preferred items.  ...  In this work, we present a new dataset for conversational recommendation over knowledge graphs in e-commerce platforms called COOKIE.  ...  Conversation Synthesis. The next step is to generate dialogue for the recommendation interactions.  ... 
arXiv:2008.09237v1 fatcat:agnch5bxxjcebcswpy3mkbltte

Session-Based Recommender System for Sustainable Digital Marketing

Hyunwoo Hwangbo, Yangsok Kim
2019 Sustainability  
This research aims to improve recommendation systems' performance by considering item session and attribute session information.  ...  We suggest the Item Session-Based Recommender (ISBR) and the Attribute Session-Based Recommenders (ASBRs) that use item and attribute session data independently, and then we suggest the Feature-Weighted  ...  Session-Based Recommender Systems SBRs explicitly consider the order of user interactions, namely sessions, when recommending items.  ... 
doi:10.3390/su11123336 fatcat:wt2mmb7razb55cqwuiwe54alxi

DGEM: A New Dual-modal Graph Embedding Method in Recommendation System [article]

Huimin Zhou and Qing Li and Yong Jiang and Rongwei Yang and Zhuyun Qi
2021 arXiv   pre-print
In addition, as the interaction between users and items increases and the relationship between items becomes more complicated, the embedding method proposed for sequence data is no longer suitable for  ...  In the current deep learning based recommendation system, the embedding method is generally employed to complete the conversion from the high-dimensional sparse feature vector to the low-dimensional dense  ...  If the sparseness of the recommendation system is measured by the proportion of interactions among items in all possible interactions, the sparseness of the Amazon electronic product data set used in this  ... 
arXiv:2108.04031v1 fatcat:negba5cctnf6zdhnosqcahnugy

Modeling impression discounting in large-scale recommender systems

Pei Lee, Laks V.S. Lakshmanan, Mitul Tiwari, Sam Shah
2014 Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '14  
User behavioral analysis and user feedback (both explicit and implicit) modeling are crucial for the improvement of any online recommender system.  ...  In this paper, we address modeling impression discounting of recommended items, that is, how to model user's no-action feedback on impressed recommended items.  ...  In most recommender systems, once the conversion is true, item will not be recommended to user again. So conversion = T rue only possibly occurs once on the tail of a sequence.  ... 
doi:10.1145/2623330.2623356 dblp:conf/kdd/LeeLTS14 fatcat:lwysoceijndvzgnufra57x4j5e

An Implicit Preference-Aware Sequential Recommendation Method Based on Knowledge Graph

Haiyan Wang, Kaiming Yao, Jian Luo, Yi Lin, Honghao Gao
2021 Wireless Communications and Mobile Computing  
In addition, most of the previous works mainly focus on exploiting relationships between items in the sequence and seldom consider quantifying the degree of preferences for items implied by user's different  ...  However, most of the sequential recommendation methods assume that user's preferences only depend on specific items in the current sequence and do not consider user's implicit interests.  ...  Acknowledgments This work is supported partly by the National Natural Science Foundation of China under Grant No. 61772285 and the Jiangsu Key Laboratory of Big Data Security and Intelligent Processing  ... 
doi:10.1155/2021/5206228 fatcat:dmn2323kyfh2nppavmyhse4dfe

Personal Interest Attention Graph Neural Networks for Session-Based Recommendation

Xiangde Zhang, Yuan Zhou, Jianping Wang, Xiaojun Lu
2021 Entropy  
Considering the diversity of items and users' interests, a personalized interest attention graph neural network (PIA-GNN) is proposed for session-based recommendation.  ...  Session-based recommendations aim to predict a user's next click based on the user's current and historical sessions, which can be applied to shopping websites and APPs.  ...  In addition, most existing recommendation systems use users' information and historical behavior records for personalized recommendation.  ... 
doi:10.3390/e23111500 pmid:34828197 pmcid:PMC8618736 fatcat:ms7keg7bn5am7e7g4nbxmgindy

Who to Watch Next: Two-side Interactive Networks for Live Broadcast Recommendation [article]

Jiarui Jin, Xianyu Chen, Yuanbo Chen, Weinan Zhang, Renting Rui, Zaifan Jiang, Zhewen Su, Yong Yu
2022 arXiv   pre-print
Different from classical item recommendation, live broadcast recommendation is to automatically recommend user anchors instead of items considering the interactions among triple-objects (i.e., users, anchors  ...  In this paper, we propose a novel TWo-side Interactive NetworkS (TWINS) for live broadcast recommendation.  ...  The Shanghai Jiao Tong University Team is supported by Shanghai Municipal Science and Technology Major Project (2021SHZDZX0102) and National Natural Science Foundation of China (62076161, 62177033).  ... 
arXiv:2202.04333v1 fatcat:2yw3frfs5zd7fpeeldlcobaruy

A Survey on Reinforcement Learning for Recommender Systems [article]

Yuanguo Lin, Yong Liu, Fan Lin, Lixin Zou, Pengcheng Wu, Wenhua Zeng, Huanhuan Chen, Chunyan Miao
2022 arXiv   pre-print
Finally, under discussion for open issues of RL and its limitations of recommender systems, we highlight some potential research directions in this field.  ...  To this end, we firstly provide a thorough overview, comparisons, and summarization of RL approaches applied in four typical recommendation scenarios, including interactive recommendation, conversational  ...  future preference and recommend the next item given a sequence of historical interactions.  ... 
arXiv:2109.10665v2 fatcat:wx5ghn66hzg7faxee54jf7gspq

How recommender systems can transform airline offer construction and retailing

Amine Dadoun, Michael Defoin-Platel, Thomas Fiig, Corinne Landra, Raphaël Troncy
2021 Journal of Revenue and Pricing Management  
We present six recommender system use cases that cover the entire traveler journey and we discuss the particular mind-set and needs of the customer for each of these use cases.  ...  This paper contains a systematic review of the different families of recommender system algorithms and discusses how the use cases can be implemented in practice by matching them with a recommender system  ...  It is therefore necessary to analyze users' live sequence of actions (for instance, their sequence of clicks) to identify patterns and generate recommendations (Linden et al. 2003) .  ... 
doi:10.1057/s41272-021-00313-2 fatcat:ukvazo4zhnagzdwkqu454wohku

Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation [article]

Yitong Pang, Lingfei Wu, Qi Shen, Yiming Zhang, Zhihua Wei, Fangli Xu, Ethan Chang, Bo Long, Jian Pei
2021 arXiv   pre-print
Predicting the next interaction of a short-term interaction session is a challenging task in session-based recommendation.  ...  Almost all existing works rely on item transition patterns, and neglect the impact of user historical sessions while modeling user preference, which often leads to non-personalized recommendation.  ...  ACKNOWLEDGMENTS The work is partially supported by the National Nature Science Foundation of China (No. 61976160, 61976158, 61906137) and Technology research plan project of Ministry of Public and Security  ... 
arXiv:2107.03813v3 fatcat:tf7b734ymzh47i3o27arpnqmry

A Bayesian Approach to Conversational Recommendation Systems [article]

Francesca Mangili and Denis Broggini and Alessandro Antonucci and Marco Alberti and Lorenzo Cimasoni
2020 arXiv   pre-print
We present a conversational recommendation system based on a Bayesian approach.  ...  Such prior information can be combined with historical data to discriminate items with different recommendation histories.  ...  Here we focus on such a newer class of recommendation systems, called here conversational, as we term conversation a sequence of dynamically customized interactions between the user and the system, before  ... 
arXiv:2002.05063v1 fatcat:bk7xfu5gt5exdpflr7iumrvdwm
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