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Interactive Social Recommendation
2017
Proceedings of the 2017 ACM on Conference on Information and Knowledge Management - CIKM '17
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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
2010
Proceedings of the fourth ACM conference on Recommender systems - RecSys '10
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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
2014
2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
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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
2010
Proceedings of the 16th ACM international conference on Supporting group work - GROUP '10
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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
2019
PLoS ONE
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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
2011
Proceedings of the fifth ACM conference on Recommender systems - RecSys '11
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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
2014
Procedia Technology - Elsevier
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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
2020
Companion Proceedings of the Web Conference 2020
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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
2014
IEEE Transactions on Learning Technologies
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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
2014
10th International Conference on Network and Service Management (CNSM) and Workshop
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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
2012
2012 13th International Workshop on Image Analysis for Multimedia Interactive Services
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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
2002
Social Science Research Network
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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
2018
Journal of Internet Services and Applications
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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
2012
2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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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
2013
International Journal of Interactive Multimedia and Artificial Intelligence
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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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