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Towards Deep Conversational Recommendations [article]

Raymond Li, Samira Kahou, Hannes Schulz, Vincent Michalski, Laurent Charlin, Chris Pal
2019 arXiv   pre-print
Second, we use this dataset to explore multiple facets of conversational recommendations.  ...  There has been growing interest in using neural networks and deep learning techniques to create dialogue systems.  ...  It is well known that deep learning techniques require considerable amounts of data to be effective.  ... 
arXiv:1812.07617v2 fatcat:mypxbed5yrfvhpmoxmw7d65lxi

Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems

Wenqiang Lei, Xiangnan He, Yisong Miao, Qingyun Wu, Richang Hong, Min-Yen Kan, Tat-Seng Chua
2020 Proceedings of the 13th International Conference on Web Search and Data Mining  
A successful Conversational Recommender System (CRS) requires proper handling of interactions between conversation and recommendation.  ...  items, based on Estimation stage and conversation history; and (3) Reflection, which updates the recommender model when a user rejects the recommendations made by the Action stage.  ...  Towards the deep interaction between CC and RC, we propose a new solution named Estimation-Action-Reflection (EAR), which consists of three stages.  ... 
doi:10.1145/3336191.3371769 dblp:conf/wsdm/Lei0MWHKC20 fatcat:zwq72cr3ifh4jgtidobcvxnf4e

An Automatic Procedure for Generating Datasets for Conversational Recommender Systems

Alessandro Suglia, Claudio Greco, Pierpaolo Basile, Giovanni Semeraro, Annalina Caputo
2017 Conference and Labs of the Evaluation Forum  
Conversational Recommender Systems assist online users in their information-seeking and decision making tasks by supporting an interactive process with the aim of finding the most appealing items according  ...  Unfortunately, collecting dialogues data to train these systems can be labour-intensive, especially for data-hungry Deep Learning models.  ...  The source code of the automatic procedure for generating conversational recommender systems datasets will be released when the paper will be accepted.  ... 
dblp:conf/clef/Suglia0BSC17 fatcat:dq77som3grcsbj4yfk56esvema

Process Knowledge-Infused AI: Towards User-level Explainability, Interpretability, and Safety [article]

Amit Sheth, Manas Gaur, Kaushik Roy, Revathy Venkataraman, Vedant Khandelwal
2022 arXiv   pre-print
However, in high-value, sensitive, or safety-critical applications such as self-management for personalized health or food recommendation with a specific purpose (e.g., allergy-aware recipe recommendations  ...  ACKNOWLEDGMENT This work was supported in part by National Science Foundation (NSF) Award 2133842, "EAGER: Advancing Neurosymbolic AI with Deep Knowledge-infused Learning."  ...  ., shallow, semi-deep, and deep [3] ), process knowledge infusion develops a new and complementary set of methods, datasets, and evaluation methods under semi-deep and deep knowledge infusion.  ... 
arXiv:2206.13349v1 fatcat:tuqoxg76qzh2xm65silebta25i

Conversational Recommendation: Formulation, Methods, and Evaluation

Wenqiang Lei, Xiangnan He, Maarten de Rijke, Tat-Seng Chua
2020 Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval  
We identify four emerging directions: (1) exploration and exploitation trade-off in the cold-start recommendation setting; (2) attributecentric conversational recommendation; (3) strategy-focused conversational  ...  This prevents recommender systems from capturing dynamic and fine-grained preferences of users. Conversational recommender systems bring a revolution to existing recommender systems.  ...  recommendation 4.4 Dialogue understanding and generation V: Future directions (10 min) 2.1 Collaborative filtering 2.2 Deep learning approaches 2.3  ... 
doi:10.1145/3397271.3401419 dblp:conf/sigir/Lei0RC20 fatcat:hz3vszhqrfejbhmxkohe7ihtsq

Deep Bayesian Multi-Target Learning for Recommender Systems [article]

Qi Wang, Zhihui Ji, Huasheng Liu, Binqiang Zhao
2019 arXiv   pre-print
This work introduces a multi-target optimization framework with Bayesian modeling of the target events, called Deep Bayesian Multi-Target Learning (DBMTL).  ...  We applied the framework to Taobao live-streaming recommendation, to simultaneously optimize (and strike a balance) on targets including click-through rate, user stay time in live room, purchasing behaviors  ...  We thank members of the Alibaba Recommendation Algorithm Team for various fruitful discussions that contributed to the evolution of the Taobao live-streaming recommendation system.  ... 
arXiv:1902.09154v1 fatcat:nau6npbr7rafregqbg45dtlj7a

Adversarial learning for product recommendation [article]

Joel R. Bock, Akhilesh Maewal
2020 arXiv   pre-print
The results are shown comparable to published conversion rates aggregated across many industries and product types.  ...  Our results are preliminary, however they suggest that the recommendations produced by the model may provide utility for consumers and digital retailers.  ...  Comparison with deep recommenders. A direct comparison of the present results with deep recommender systems found in the literature is problematic.  ... 
arXiv:2007.07269v1 fatcat:tods5pqxvnde3hhoqxohfnngpa

Conversational Recommender System [article]

Yueming Sun, Yi Zhang
2018 arXiv   pre-print
In this work, we propose to integrate research in dialog systems and recommender systems into a novel and unified deep reinforcement learning framework to build a personalized conversational recommendation  ...  On the other hand, it is well known that sales conversion rate can be greatly improved based on recommender systems, which learn user preferences based on past purchasing behavior and optimize business  ...  Inspired by these works, we build a deep RL based conversational recommender system.  ... 
arXiv:1806.03277v1 fatcat:d3opu6le6nhk7a27wrvi3op3b4

Listening to Young Children: Toward Agency, Equity, and Deeper Learning

Martha M. Foote
2019 Journal of Education & Social Policy  
With the goal of deep learning in mind, a recognition that an authentic and deep level of listening to young children by teachers and adults is a readily available strategy and opportunity for schools  ...  opportunities to be taken seriously as they interact and express their thoughts, young children can be afforded experiences to better develop their agencies to think, create, express, connect, and be the kind of deep  ...  Cadwell also recommends careful consideration and planning for the composition of groups.  ... 
doi:10.30845/jesp.v6n4p3 fatcat:af44bd757fcjlafmq6mjeydota

Neural Approaches to Conversational AI

Jianfeng Gao, Michel Galley, Lihong Li
2018 Proceedings of ACL 2018, Tutorial Abstracts  
This tutorial surveys neural approaches to conversational AI that were developed in the last few years.  ...  We group conversational systems into three categories: (1) question answering agents, (2) taskoriented dialogue agents, and (3) social bots.  ...  breakthroughs in deep learning (DL) and reinforcement learning (RL) are applied to conversational AI.  ... 
doi:10.18653/v1/p18-5002 dblp:conf/acl/GaoGL18 fatcat:7llxwuntafh4fcjj4ia3tm642a

Page 28 of The New Princeton Review Vol. 10, Issue 1 [page]

1838 The New Princeton Review  
What more adapted to cover them with confusion and shame, than to hear a man of God manifesting a deep and tender interest in the salva- tion of their offspring, toward whom they were conscious that they  ...  hearts of the parents, which had never relented before, began to melt, and that very conversation was the means of bringing them to serious reflection; to deep conviction of sin; and, finally, as their  ... 

DTCRSKG: A Deep Travel Conversational Recommender System Incorporating Knowledge Graph

Hui Fang, Chongcheng Chen, Yunfei Long, Ge Xu, Yongqiang Xiao
2022 Mathematics  
Then, we proposed and evaluated BERT-based baseline models for the travel conversational recommender system and compared them with several representative non-conversational and conversational recommender  ...  Extensive experiments demonstrated the effectiveness and robustness of our approach regarding conversational recommendation tasks.  ...  To our knowledge, our work is the first to produce a deep travel conversational recommendation system.  ... 
doi:10.3390/math10091402 fatcat:npubeenmcfcwjozwl6t2yteii4

Page 661 of Contemporary Psychology Vol. 48, Issue 5 [page]

2003 Contemporary Psychology  
That the Bible was only the work of man and not to be taken as the last word is deep talk about their negative attitude toward Ellis's book” (see Haskell, 1999, pp. 54-55).  ...  conversation.  ... 

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.  ...  We summarize the key challenges of developing CRSs in five directions: (1) Question-based user preference elicitation. (2) Multi-turn conversational recommendation strategies. (3) Dialogue understanding  ...  Model-free frameworks such as deep Q-network (DQN) [214, 216, 231, 226] and deep deterministic policy gradient (DDPG) [65] are used in interactive recommendation scenarios.  ... 
arXiv:2101.09459v6 fatcat:j7djzhrv6fazpogmnj7r4e4f2y

Variations in the Pronunciation of French

J. L. Borgerhoff
1907 The School Review  
Among scholars and orators it will be found that in conversation most of them use a close e, while in public speaking the tendency is toward open e.  ...  Witnesses gave for Lyons sable, diable, f ble (with long-deep a), as recommended by Ploetz, Lesaint,4 and Thurot.5 Littrie6 and Sachs7 call this antiquated and recommend a semi-long a.  ... 
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