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Exploiting Relational Information in Social Networks using Geometric Deep Learning on Hypergraphs

Devanshu Arya, Marcel Worring
2018 Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval - ICMR '18  
In these communities, some relations are much more complicated than pairwise relations, thus cannot be simply modeled by a graph; (b) there are different types of entities and relations in a social network  ...  However, there are two challenges: (a) a social network has an intrinsic community structure.  ...  [14] , music recommendation [7] and link prediction in social networks [24] .  ... 
doi:10.1145/3206025.3206062 dblp:conf/mir/AryaW18 fatcat:efr5s4otpvc3hdcdgmxv6gu7my

An Effective Transmission Strategy Exploiting Node Preference and Social Relations in Opportunistic Social Networks

Yeqing Yan, Zhigang Chen, Jia Wu, Leilei Wang, Kanghuai Liu, Peng Zheng
2019 IEEE Access  
To improve the transmission efficiency, this paper establishes an effective data transmission strategy (ENPSR) exploiting node preference and social relations in opportunistic social networks.  ...  Since the transmission of a large amount of data in a short time will cause the problem of data redundancy, opportunistic social networks suggest that the most appropriate next hop should be selected to  ...  The optimal next hop of the nodes in the current communication domain and optimal recommended route can be obtained.  ... 
doi:10.1109/access.2019.2914505 fatcat:mt4q6ihpjrckvkf45mdzcklasm

The Role of Market Knowledge in Recognizing and Exploiting Entrepreneurial Opportunities in Technology Intensive Firms

Maija Renko
2008 Social Science Research Network  
I remember clearly the Christmas party in 2003, when Alan Carsrud and I had a conversation about me joining the doctoral program at FIU.  ...  While leaders in all 85 companies were interviewed for the research in 2003-2004, 42 firms provided data in 2007.  ...  If anything, they tell about the social network of the entrepreneur(s) at the time of the business startup.  ... 
doi:10.2139/ssrn.1344649 fatcat:5kbimvbpsvhahmphetdgurorsq

Analysis of the Legal Rules for Exploitation Windows and Commercial Practices in EU Member States and of the Importance of Exploitation Windows for New Business Practices

Heritiana Ranaivoson, Sophie De Vinck, Ben Van Rompuy, Katharina Hoelck
2014 Social Science Research Network  
Marketing impact through social networking strategies may be more difficult to achieve in this context.  ...  in the domain of release windows, e.g. an EU-wide (re)alignment of release windows.  ...  Interviewed VOD services (9) : Cross-border characteristics and potential of VoD in Europe C. Release Windows A.  ... 
doi:10.2139/ssrn.2472973 fatcat:ufww5o4norc67f7jtlwuqoro2a

Interactive Social Recommendation

Xin Wang, Steven C.H. Hoi, Chenghao Liu, Martin Ester
2017 Proceedings of the 2017 ACM on Conference on Information and Knowledge Management - CIKM '17  
is to explore and exploit the extend to which a user trusts his/her friends when utilizing social information to improve recommendations.  ...  To tackle these challenges, we propose a novel method for interactive social recommendation, which not only simultaneously explores user preferences and exploits the e ectiveness of personalization in  ...  As the rich information on social network becomes available [13, 15] , social recommendation which makes use of social information from social networks to enhance recommender systems has a racted lots  ... 
doi:10.1145/3132847.3132880 dblp:conf/cikm/WangHLE17 fatcat:l4xwvhl67nhs7djignsv5obrne

Neural Hybrid Recommender: Recommendation needs collaboration [article]

Ezgi Yıldırım, Payam Azad, Şule Gündüz Öğüdücü
2019 arXiv   pre-print
In this paper, we introduce a generalized neural network-based recommender framework that is easily extendable by additional networks.  ...  The results in these real-world datasets show the superior performance of our approach in comparison with the state-of-the-art methods.  ...  Thus, we exploited the advantage of a hash function which converts a raw text to a sequence of indexes in a fixed-size hashing space.  ... 
arXiv:1909.13330v1 fatcat:a3z2hdzdovgfnmaebovus5ymui

Artificial intelligence in recommender systems

Qian Zhang, Jie Lu, Yaochu Jin
2020 Complex & Intelligent Systems  
The observations in this paper will directly support researchers and professionals to better understand current developments and new directions in the field of recommender systems using AI.  ...  learning, genetic algorithms, evolutionary algorithms, neural networks and deep learning, and active learning.  ...  Other methods have since been developed that exploit social network information to assist cross-domain recommender systems.  ... 
doi:10.1007/s40747-020-00212-w fatcat:ev3cyoy2mjeuhmq3rymkx2shsy

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation [article]

Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, Meng Wang
2021 arXiv   pre-print
Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks.  ...  In this survey paper, we conduct a systematic review on neural recommender models from the perspective of recommendation modeling with the accuracy goal, aiming to summarize this field to facilitate researchers  ...  For each user, her latent embedding p u is composed of two parts: a free embedding e u from the item domain, and a social embedding h u that is similar with social connections in the social domain [152  ... 
arXiv:2104.13030v3 fatcat:7bzwaxcarrgbhe36teik2rhl6e

Enhancing Social Recommendation with Adversarial Graph Convolutional Networks [article]

Junliang Yu, Hongzhi Yin, Jundong Li, Min Gao, Zi Huang, Lizhen Cui
2020 arXiv   pre-print
Most existing social recommendation models only consider the homophily in social networks and neglect these drawbacks.  ...  According to the negative findings, the failure is attributed to: (1) A majority of users only have a very limited number of neighbors in social networks and can hardly benefit from social relations; (  ...  preference domain due to the homophily across social and user-item networks.  ... 
arXiv:2004.02340v4 fatcat:2fg4c4c3kbdvnhu4rf32ssrglu

Towards Cognitive Recommender Systems

Amin Beheshti, Shahpar Yakhchi, Salman Mousaeirad, Seyed Mohssen Ghafari, Srinivasa Reddy Goluguri, Mohammad Amin Edrisi
2020 Algorithms  
In this context, an intelligent Recommender System should be able to learn from domain experts' knowledge and experience, as it is vital to know the domain that the items will be recommended.  ...  We present a motivating scenario in banking and argue that existing Recommender Systems: (i) do not use domain experts' knowledge to adapt to new situations; (ii) may not be able to predict the ratings  ...  , which aim to exploit knowledge from a source domain to perform or improve recommendations in a target domain.  ... 
doi:10.3390/a13080176 fatcat:m6fijkw7srecdhqylayx5u2aay

Social recommendation: a review

Jiliang Tang, Xia Hu, Huan Liu
2013 Social Network Analysis and Mining  
In this paper, we present a review of existing recommender systems and discuss some research directions.  ...  Due to the potential value of social relations in recommender systems, social recommendation has attracted increasing attention in recent years.  ...  In this survey, we do not give a general comparison of existing social recommender systems and the reasons are three-fold.  ... 
doi:10.1007/s13278-013-0141-9 fatcat:qhzr7ojoi5exnprutyje6kyzkq

Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation [article]

Junliang Yu, Hongzhi Yin, Jundong Li, Qinyong Wang, Nguyen Quoc Viet Hung, Xiangliang Zhang
2021 arXiv   pre-print
In this paper, we fill this gap and propose a multi-channel hypergraph convolutional network to enhance social recommendation by leveraging high-order user relations.  ...  Most existing social recommendation models exploit pairwise relations to mine potential user preferences.  ...  Multi-Channel Hypergraph Convolutional Network for Social Recommendation In this section, we present our model MHCN, which stands for Multi-channel Hypergraph Convolutional Network.  ... 
arXiv:2101.06448v3 fatcat:qvrkivpzyrentl2vp2dykw4acu

Recommender Systems: Introduction and Challenges [chapter]

Francesco Ricci, Lior Rokach, Bracha Shapira
2015 Recommender Systems Handbook  
of people in social networks, and recommendations of content social media content such as tweets, Facebook feeds, LinkedIn updates, and others.  ...  The idea of relating recommendations in different domains by exploiting user data collected in one domain to produce recommendations in another, is at the base of the research on cross-domain recommender  ... 
doi:10.1007/978-1-4899-7637-6_1 fatcat:rawfrtd24favficr2szferihgi

Introduction to Recommender Systems Handbook [chapter]

Francesco Ricci, Lior Rokach, Bracha Shapira
2010 Recommender Systems Handbook  
Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user. In this introductory chapter we briefly discuss basic RS ideas and concepts.  ...  Our main goal is to delineate, in a coherent and structured way, the chapters included in this handbook and to help the reader navigate the extremely rich and detailed content that the handbook offers.  ...  Others have showed that in some cases social-network data yields better recommendations than profile similarity data [37] and that adding social network data to traditional CF improves recommendation  ... 
doi:10.1007/978-0-387-85820-3_1 fatcat:s4s5dzb3encezea335dvoqvw5q

Context-Aware Event Recommendation in Event-based Social Networks

Augusto Q. Macedo, Leandro B. Marinho, Rodrygo L.T. Santos
2015 Proceedings of the 9th ACM Conference on Recommender Systems - RecSys '15  
However, the sheer volume of events available in event-based social networks (EBSNs) often undermines the users' ability to choose the events that best fit their interests.  ...  In particular, besides contentbased signals based on the events' description and collaborative signals derived from users' RSVPs, we exploit social signals based on group memberships, location signals  ...  There is a large body of research on venue recommendation in location-based social networks (LBSNs) [2, 4, 5, 6, 18, 10] .  ... 
doi:10.1145/2792838.2800187 dblp:conf/recsys/MacedoMS15 fatcat:rccnr4odlrd5jeplv3rt2ydeve
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