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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  
Social recommendation has been an active research topic over the last decade, based on the assumption that social information from friendship networks is bene cial for improving recommendation accuracy  ...  On the other hand, most existing social recommendation models are non-interactive in that their algorithmic strategies are based on batch learning methodology, which learns to train the model in an o ine  ...  INTERACTIVE SOCIAL RECOMMENDATION In this section, we propose our interactive social recommendation model (ISR) which is capable of re ning itself to best serve the customers a er each interaction with  ... 
doi:10.1145/3132847.3132880 dblp:conf/cikm/WangHLE17 fatcat:l4xwvhl67nhs7djignsv5obrne

Interactive recommendations in social endorsement networks

Theodoros Lappas, Dimitrios Gunopulos
2010 Proceedings of the fourth ACM conference on Recommender systems - RecSys '10  
In this work, we formalize the problem of interactive recommendations in social endorsement networks: given a query of tags and a social endorsement network, the problem is to recommend entities that match  ...  We propose an efficient search engine for the solution of the problem, able to produce high-quality and explainable recommendations.  ...  CONCLUSION In this paper, we formalized the problem of interactive recommendations in social endorsement networks.  ... 
doi:10.1145/1864708.1864735 dblp:conf/recsys/LappasG10 fatcat:rqfzx6lrejdlld6kyr75lvpoqq

Social interaction based video recommendation: Recommending YouTube videos to facebook users

Bin Nie, Honggang Zhang, Yong Liu
2014 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)  
This opens up the possibility of exploiting video-related user social interaction information for better video recommendation.  ...  Towards this goal, we conduct a case study of recommending YouTube videos to Facebook users based on their social interactions.  ...  SOCIAL INTERACTION BASED RECOMMENDATION We next demonstrate that information about social interaction among users on OSNs can significantly improve video recommendation accuracy.  ... 
doi:10.1109/infcomw.2014.6849175 dblp:conf/infocom/NieZL14 fatcat:5swi37jltfacnfa5rhtkuovbeq

Enhancing group recommendation by incorporating social relationship interactions

Mike Gartrell, Xinyu Xing, Qin Lv, Aaron Beach, Richard Han, Shivakant Mishra, Karim Seada
2010 Proceedings of the 16th ACM international conference on Supporting group work - GROUP '10  
Group recommendation, which makes recommendations to a group of users instead of individuals, has become increasingly important in both the workspace and people's social activities, such as brainstorming  ...  In this work, we propose a group recommendation method that utilizes both social and content interests of group members.  ...  Since they interact with and influence each other, the group decision is Table 4 .  ... 
doi:10.1145/1880071.1880087 dblp:conf/group/GartrellXLBHMS10 fatcat:knv7ovvoqfan7go6q5iswzewga

Social recommendation model based on user interaction in complex social networks

Yakun Li, Jiaomin Liu, Jiadong Ren, Floriana Gargiulo
2019 PLoS ONE  
Therefore, applied research on user interaction has become increasingly necessary in the field of social recommendation.  ...  The user interaction in online social networks can not only reveal the social relationships among users in e-commerce systems, but also imply the social preferences of a target user for recommendation  ...  Definition 1 Social interaction is considered to be a social interactive relationship among users in a recommender system, such as, comments, forwards, push messages, blog posts, other social services  ... 
doi:10.1371/journal.pone.0218957 pmid:31291288 pmcid:PMC6619984 fatcat:3uaiqwomnravloms4mduv2dr4m

Interface and interaction design for group and social recommender systems

Yu Chen
2011 Proceedings of the fifth ACM conference on Recommender systems - RecSys '11  
Group and social recommender systems aim to recommend items of interest to a group or a community of people.  ...  We further apply the techniques used in the current recommender systems to GroupFun, a music social group recommender system.  ...  We have summarized the state-of-the-art of interface and interaction design in current group and social recommender systems.  ... 
doi:10.1145/2043932.2044007 dblp:conf/recsys/Chen11 fatcat:dnbxdihdmva4bo4mtl5jclgrr4

GuideMe – A Tourist Guide with a Recommender System and Social Interaction

Artem Umanets, Artur Ferreira, Nuno Leite
2014 Procedia Technology - Elsevier  
As compared to previous recommender based tourist guides, the key novelties of GuideMe are its integration with social networks and the unique set of options offered in the application.  ...  The recommendations are carried out using the well-known Mahout library.  ...  Currently, there is no interaction between the RS and the supported social services.  ... 
doi:10.1016/j.protcy.2014.10.248 fatcat:4ekupcbx4bd3lkrsrwlm6j264e

Social Network Influence Ranking via Embedding Network Interactions for User Recommendation

Hongbo Bo, Ruan McConville, Jun Hong, Weiru Liu
2020 Companion Proceedings of the Web Conference 2020  
CONCLUSION This paper proposed a new social network influence ranking method based on embedded interaction networks, and studied this influence ranking within the application of user recommendation on  ...  Once user influence has been modelled, it may be used for tasks such as user recommendation. In this paper, we study how we can assign influence values to users in a social network.  ... 
doi:10.1145/3366424.3383299 dblp:conf/www/BoM0L20 fatcat:lyu6hxh35vbcplcpbyaltw6gdq

A M-Learning Content Recommendation Service by Exploiting Mobile Social Interactions

Han-Chieh Chao, Chin-Feng Lai, Shih-Yeh Chen, Yueh-Min Huang
2014 IEEE Transactions on Learning Technologies  
INTRODUCTION The learning aspects such as m learning are learning across multiple Interactions with portable technologies.  ... 
doi:10.1109/tlt.2014.2323053 fatcat:36gn4yaryfeyhlpis4vfju4q2q

Measuring interactivity and geographical closeness of Online Social Network users to support social recommendation systems

Guilherme Sperb Machado, Thomas Bocek, Alexander Filitz, Burkhard Stiller
2014 10th International Conference on Network and Service Management (CNSM) and Workshop  
It provides (1) an interaction-and (2) a location-based method in support of social recommendations systems.  ...  Although this integration enables more accurate social recommendation systems, the collection and monitoring of relevant OSN data by thirdparty applications is a challenging management task, since OSNs  ...  In contrast to traditional recommendations, social recommendations have to deal with heterogeneous information of one or more sources.  ... 
doi:10.1109/cnsm.2014.7014157 dblp:conf/cnsm/MachadoBFS14 fatcat:xow2kjzoprfzrkbwjfpuwffqlq

Social recommendation using speech recognition: Sharing TV scenes in social networks

Daniel Schneider, Sebastian Tschopel, Jochen Schwenninger
2012 2012 13th International Workshop on Image Analysis for Multimedia Interactive Services  
We describe a novel system which simplifies recommendation of video scenes in social networks, thereby attracting a new audience for existing video portals.  ...  Users can select interesting quotes from a speech recognition transcript, and share the corresponding video scene with their social circle with minimal effort.  ...  An important next step is to collect and analyze the usage statistics in order to evaluate the impact of the social recommendation, and quantify to which extent socially recommended scenes attract more  ... 
doi:10.1109/wiamis.2012.6226755 dblp:conf/wiamis/SchneiderTS12 fatcat:nrs7zcgquzeojeyloljyveheqy

On the Effects of Idiotypic Interactions for Recommendation Communities in Artificial Immune Systems

Steve Cayzer, Uwe Aickelin
2002 Social Science Research Network  
The initial findings are that the immune system recommender tends to produce different neighbourhoods, and that the superior performance of this recommender is due partly to the different neighbourhoods  ...  , and partly to the way that the idiotypic effect is used to weight each neighbour's recommendations.  ...  Even in the absence of any idiotypic interactions, an antibody's correlation (weighted by the stimulation rate) must outweigh the death rate; otherwise, it will not survive in the Artificial Immune System  ... 
doi:10.2139/ssrn.2832048 fatcat:pm5fy4bnuraxjh4jz3imtjhszu

BROAD-RSI – educational recommender system using social networks interactions and linked data

Crystiam Kelle Pereira, Fernanda Campos, Victor Ströele, José Maria N. David, Regina Braga
2018 Journal of Internet Services and Applications  
The constant and ever-increasing use of social networks allows the identification of different information about profile, interests, preferences, style and behavior from the spontaneous interaction.  ...  This paper presents an infrastructure able to extract users' profile and educational context, from the Facebook social network and recommend educational resources.  ...  How long users interact on social networks? Which social networks users prefer to receive educational recommendations?  ... 
doi:10.1186/s13174-018-0076-5 fatcat:b6urdqfauvhvjc7x7p27gocdae

Applying Trust Metrics Based on User Interactions to Recommendation in Social Networks

A. Lumbreras, R. Gavalda
2012 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining  
Recommender systems have been strongly researched within the last decade. With the arising and popularization of digital social networks a new field has been opened for social recommendations.  ...  Considering the network topology, users interactions, or estimating trust between users are some of the new strategies that recommender systems can take into account in order to adapt their techniques  ...  For the second question, we will develop a basic recommender system framework for social networks.  ... 
doi:10.1109/asonam.2012.200 dblp:conf/asunam/LumbrerasG12 fatcat:5pia2hxbcjemhognvvwiqbso2m

Mining Social and Affective Data for Recommendation of Student Tutors

Elisa Boff, Berni Reategui
2013 International Journal of Interactive Multimedia and Artificial Intelligence  
Ketwords -Collaboration, Learning Environment, Recommender Systems, Social-Affective Data.  ...  The paper presents the educational environment, the representation mechanism and learning algorithm used to mine social-affective data in order to create a recommendation model of tutors.  ...  A recommender system analyses students' interactions and finds suitable tutors among them as well as contents to be recommended.  ... 
doi:10.9781/ijimai.2013.214 fatcat:s5i3a4vc6reb7cftcho5fofr5a
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