Unknown but interesting recommendation using social penetration
Abstract With the recent rise in popularity of social networks, millions of users have included social network Web sites into their daily lives. Traditional social recommendation systems suggest items with high popularity, familiarity, and similarity to users. Such recommendation processes might enc...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Huang, Jen-Wei [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Soft computing - Springer Berlin Heidelberg, 1997, 23(2018), 16 vom: 11. Juli, Seite 7249-7262 |
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| Übergeordnetes Werk: |
volume:23 ; year:2018 ; number:16 ; day:11 ; month:07 ; pages:7249-7262 |
| Links: |
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| DOI / URN: |
10.1007/s00500-018-3371-y |
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| 520 | |a Abstract With the recent rise in popularity of social networks, millions of users have included social network Web sites into their daily lives. Traditional social recommendation systems suggest items with high popularity, familiarity, and similarity to users. Such recommendation processes might encounter two problems: (1) if the recommended item is very popular, the target user may already be familiar with it; (2) the target user may not be interested in items recommended by users familiar to them. To improve upon traditional recommendation systems, we propose a SPUBI algorithm to discover unknown but interesting items for users using social penetration phenomenon. SPUBI considers the popularity of items, familiarity of other users, similarity of users, users interests and categories, and item freshness to obtain a social penetration score, which are used to generate a recommendation list to the target user. Experimental results demonstrate that the proposed SPUBI algorithm can provide a satisfactory recommendation list while discovering unknown but interesting items effectively. | ||
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| 700 | 1 | |a Chung, Chih-Chin |4 aut | |
| 700 | 1 | |a Jian, Zhi-Jia |4 aut | |
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10.1007/s00500-018-3371-y doi (DE-627)OLC2034899008 (DE-He213)s00500-018-3371-y-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Huang, Jen-Wei verfasserin aut Unknown but interesting recommendation using social penetration 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract With the recent rise in popularity of social networks, millions of users have included social network Web sites into their daily lives. Traditional social recommendation systems suggest items with high popularity, familiarity, and similarity to users. Such recommendation processes might encounter two problems: (1) if the recommended item is very popular, the target user may already be familiar with it; (2) the target user may not be interested in items recommended by users familiar to them. To improve upon traditional recommendation systems, we propose a SPUBI algorithm to discover unknown but interesting items for users using social penetration phenomenon. SPUBI considers the popularity of items, familiarity of other users, similarity of users, users interests and categories, and item freshness to obtain a social penetration score, which are used to generate a recommendation list to the target user. Experimental results demonstrate that the proposed SPUBI algorithm can provide a satisfactory recommendation list while discovering unknown but interesting items effectively. Social network analysis Recommendation system Unknown but interesting Ma, Hao-Shang (orcid)0000-0001-8919-4021 aut Chung, Chih-Chin aut Jian, Zhi-Jia aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 23(2018), 16 vom: 11. Juli, Seite 7249-7262 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:23 year:2018 number:16 day:11 month:07 pages:7249-7262 https://doi.org/10.1007/s00500-018-3371-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 23 2018 16 11 07 7249-7262 |
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10.1007/s00500-018-3371-y doi (DE-627)OLC2034899008 (DE-He213)s00500-018-3371-y-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Huang, Jen-Wei verfasserin aut Unknown but interesting recommendation using social penetration 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract With the recent rise in popularity of social networks, millions of users have included social network Web sites into their daily lives. Traditional social recommendation systems suggest items with high popularity, familiarity, and similarity to users. Such recommendation processes might encounter two problems: (1) if the recommended item is very popular, the target user may already be familiar with it; (2) the target user may not be interested in items recommended by users familiar to them. To improve upon traditional recommendation systems, we propose a SPUBI algorithm to discover unknown but interesting items for users using social penetration phenomenon. SPUBI considers the popularity of items, familiarity of other users, similarity of users, users interests and categories, and item freshness to obtain a social penetration score, which are used to generate a recommendation list to the target user. Experimental results demonstrate that the proposed SPUBI algorithm can provide a satisfactory recommendation list while discovering unknown but interesting items effectively. Social network analysis Recommendation system Unknown but interesting Ma, Hao-Shang (orcid)0000-0001-8919-4021 aut Chung, Chih-Chin aut Jian, Zhi-Jia aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 23(2018), 16 vom: 11. Juli, Seite 7249-7262 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:23 year:2018 number:16 day:11 month:07 pages:7249-7262 https://doi.org/10.1007/s00500-018-3371-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 23 2018 16 11 07 7249-7262 |
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10.1007/s00500-018-3371-y doi (DE-627)OLC2034899008 (DE-He213)s00500-018-3371-y-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Huang, Jen-Wei verfasserin aut Unknown but interesting recommendation using social penetration 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract With the recent rise in popularity of social networks, millions of users have included social network Web sites into their daily lives. Traditional social recommendation systems suggest items with high popularity, familiarity, and similarity to users. Such recommendation processes might encounter two problems: (1) if the recommended item is very popular, the target user may already be familiar with it; (2) the target user may not be interested in items recommended by users familiar to them. To improve upon traditional recommendation systems, we propose a SPUBI algorithm to discover unknown but interesting items for users using social penetration phenomenon. SPUBI considers the popularity of items, familiarity of other users, similarity of users, users interests and categories, and item freshness to obtain a social penetration score, which are used to generate a recommendation list to the target user. Experimental results demonstrate that the proposed SPUBI algorithm can provide a satisfactory recommendation list while discovering unknown but interesting items effectively. Social network analysis Recommendation system Unknown but interesting Ma, Hao-Shang (orcid)0000-0001-8919-4021 aut Chung, Chih-Chin aut Jian, Zhi-Jia aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 23(2018), 16 vom: 11. Juli, Seite 7249-7262 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:23 year:2018 number:16 day:11 month:07 pages:7249-7262 https://doi.org/10.1007/s00500-018-3371-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 23 2018 16 11 07 7249-7262 |
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10.1007/s00500-018-3371-y doi (DE-627)OLC2034899008 (DE-He213)s00500-018-3371-y-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Huang, Jen-Wei verfasserin aut Unknown but interesting recommendation using social penetration 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract With the recent rise in popularity of social networks, millions of users have included social network Web sites into their daily lives. Traditional social recommendation systems suggest items with high popularity, familiarity, and similarity to users. Such recommendation processes might encounter two problems: (1) if the recommended item is very popular, the target user may already be familiar with it; (2) the target user may not be interested in items recommended by users familiar to them. To improve upon traditional recommendation systems, we propose a SPUBI algorithm to discover unknown but interesting items for users using social penetration phenomenon. SPUBI considers the popularity of items, familiarity of other users, similarity of users, users interests and categories, and item freshness to obtain a social penetration score, which are used to generate a recommendation list to the target user. Experimental results demonstrate that the proposed SPUBI algorithm can provide a satisfactory recommendation list while discovering unknown but interesting items effectively. Social network analysis Recommendation system Unknown but interesting Ma, Hao-Shang (orcid)0000-0001-8919-4021 aut Chung, Chih-Chin aut Jian, Zhi-Jia aut Enthalten in Soft computing Springer Berlin Heidelberg, 1997 23(2018), 16 vom: 11. Juli, Seite 7249-7262 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:23 year:2018 number:16 day:11 month:07 pages:7249-7262 https://doi.org/10.1007/s00500-018-3371-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 23 2018 16 11 07 7249-7262 |
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Abstract With the recent rise in popularity of social networks, millions of users have included social network Web sites into their daily lives. Traditional social recommendation systems suggest items with high popularity, familiarity, and similarity to users. Such recommendation processes might encounter two problems: (1) if the recommended item is very popular, the target user may already be familiar with it; (2) the target user may not be interested in items recommended by users familiar to them. To improve upon traditional recommendation systems, we propose a SPUBI algorithm to discover unknown but interesting items for users using social penetration phenomenon. SPUBI considers the popularity of items, familiarity of other users, similarity of users, users interests and categories, and item freshness to obtain a social penetration score, which are used to generate a recommendation list to the target user. Experimental results demonstrate that the proposed SPUBI algorithm can provide a satisfactory recommendation list while discovering unknown but interesting items effectively. © Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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Abstract With the recent rise in popularity of social networks, millions of users have included social network Web sites into their daily lives. Traditional social recommendation systems suggest items with high popularity, familiarity, and similarity to users. Such recommendation processes might encounter two problems: (1) if the recommended item is very popular, the target user may already be familiar with it; (2) the target user may not be interested in items recommended by users familiar to them. To improve upon traditional recommendation systems, we propose a SPUBI algorithm to discover unknown but interesting items for users using social penetration phenomenon. SPUBI considers the popularity of items, familiarity of other users, similarity of users, users interests and categories, and item freshness to obtain a social penetration score, which are used to generate a recommendation list to the target user. Experimental results demonstrate that the proposed SPUBI algorithm can provide a satisfactory recommendation list while discovering unknown but interesting items effectively. © Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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Abstract With the recent rise in popularity of social networks, millions of users have included social network Web sites into their daily lives. Traditional social recommendation systems suggest items with high popularity, familiarity, and similarity to users. Such recommendation processes might encounter two problems: (1) if the recommended item is very popular, the target user may already be familiar with it; (2) the target user may not be interested in items recommended by users familiar to them. To improve upon traditional recommendation systems, we propose a SPUBI algorithm to discover unknown but interesting items for users using social penetration phenomenon. SPUBI considers the popularity of items, familiarity of other users, similarity of users, users interests and categories, and item freshness to obtain a social penetration score, which are used to generate a recommendation list to the target user. Experimental results demonstrate that the proposed SPUBI algorithm can provide a satisfactory recommendation list while discovering unknown but interesting items effectively. © Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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Unknown but interesting recommendation using social penetration |
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Ma, Hao-Shang Chung, Chih-Chin Jian, Zhi-Jia |
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