Social Content Recommendation Based on Spatial-Temporal Aware Diffusion Modeling in Social Networks
User interactions in online social networks (OSNs) enable the spread of information and enhance the information dissemination process, but at the same time they exacerbate the information overload problem. In this paper, we propose a social content recommendation method based on spatial-temporal awa...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2016-09-01
|
| Series: | Symmetry |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2073-8994/8/9/89 |
| _version_ | 1811306940766093312 |
|---|---|
| author | Farman Ullah Sungchang Lee |
| author_facet | Farman Ullah Sungchang Lee |
| author_sort | Farman Ullah |
| collection | DOAJ |
| description | User interactions in online social networks (OSNs) enable the spread of information and enhance the information dissemination process, but at the same time they exacerbate the information overload problem. In this paper, we propose a social content recommendation method based on spatial-temporal aware controlled information diffusion modeling in OSNs. Users interact more frequently when they are close to each other geographically, have similar behaviors, and fall into similar demographic categories. Considering these facts, we propose multicriteria-based social ties relationship and temporal-aware probabilistic information diffusion modeling for controlled information spread maximization in OSNs. The proposed social ties relationship modeling takes into account user spatial information, content trust, opinion similarity, and demographics. We suggest a ranking algorithm that considers the user ties strength with friends and friends-of-friends to rank users in OSNs and select highly influential injection nodes. These nodes are able to improve social content recommendations, minimize information diffusion time, and maximize information spread. Furthermore, the proposed temporal-aware probabilistic diffusion process categorizes the nodes and diffuses the recommended content to only those users who are highly influential and can enhance information dissemination. The experimental results show the effectiveness of the proposed scheme. |
| first_indexed | 2024-04-13T08:54:41Z |
| format | Article |
| id | doaj.art-a7f91b7f6dd24fbf85558e366c3664ca |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-13T08:54:41Z |
| publishDate | 2016-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-a7f91b7f6dd24fbf85558e366c3664ca2022-12-22T02:53:20ZengMDPI AGSymmetry2073-89942016-09-01898910.3390/sym8090089sym8090089Social Content Recommendation Based on Spatial-Temporal Aware Diffusion Modeling in Social NetworksFarman Ullah0Sungchang Lee1School of Electronics and Information Engineering, Korea Aerospace University, Deogyang-gu, Goyang-si, Gyeonggi-do 412-791, KoreaSchool of Electronics and Information Engineering, Korea Aerospace University, Deogyang-gu, Goyang-si, Gyeonggi-do 412-791, KoreaUser interactions in online social networks (OSNs) enable the spread of information and enhance the information dissemination process, but at the same time they exacerbate the information overload problem. In this paper, we propose a social content recommendation method based on spatial-temporal aware controlled information diffusion modeling in OSNs. Users interact more frequently when they are close to each other geographically, have similar behaviors, and fall into similar demographic categories. Considering these facts, we propose multicriteria-based social ties relationship and temporal-aware probabilistic information diffusion modeling for controlled information spread maximization in OSNs. The proposed social ties relationship modeling takes into account user spatial information, content trust, opinion similarity, and demographics. We suggest a ranking algorithm that considers the user ties strength with friends and friends-of-friends to rank users in OSNs and select highly influential injection nodes. These nodes are able to improve social content recommendations, minimize information diffusion time, and maximize information spread. Furthermore, the proposed temporal-aware probabilistic diffusion process categorizes the nodes and diffuses the recommended content to only those users who are highly influential and can enhance information dissemination. The experimental results show the effectiveness of the proposed scheme.http://www.mdpi.com/2073-8994/8/9/89spatialtemporalinformation diffusionprobabilistic diffusion modelrecommender systemonline social networks |
| spellingShingle | Farman Ullah Sungchang Lee Social Content Recommendation Based on Spatial-Temporal Aware Diffusion Modeling in Social Networks Symmetry spatial temporal information diffusion probabilistic diffusion model recommender system online social networks |
| title | Social Content Recommendation Based on Spatial-Temporal Aware Diffusion Modeling in Social Networks |
| title_full | Social Content Recommendation Based on Spatial-Temporal Aware Diffusion Modeling in Social Networks |
| title_fullStr | Social Content Recommendation Based on Spatial-Temporal Aware Diffusion Modeling in Social Networks |
| title_full_unstemmed | Social Content Recommendation Based on Spatial-Temporal Aware Diffusion Modeling in Social Networks |
| title_short | Social Content Recommendation Based on Spatial-Temporal Aware Diffusion Modeling in Social Networks |
| title_sort | social content recommendation based on spatial temporal aware diffusion modeling in social networks |
| topic | spatial temporal information diffusion probabilistic diffusion model recommender system online social networks |
| url | http://www.mdpi.com/2073-8994/8/9/89 |
| work_keys_str_mv | AT farmanullah socialcontentrecommendationbasedonspatialtemporalawarediffusionmodelinginsocialnetworks AT sungchanglee socialcontentrecommendationbasedonspatialtemporalawarediffusionmodelinginsocialnetworks |