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] Ma, Hao-Shang [verfasserIn] Chung, Chih-Chin [verfasserIn] Jian, Zhi-Jia [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 23(2018), 16 vom: 11. Juli, Seite 7249-7262 |
|---|---|
| Übergeordnetes Werk: |
volume:23 ; year:2018 ; number:16 ; day:11 ; month:07 ; pages:7249-7262 |
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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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10.1007/s00500-018-3371-y doi (DE-627)SPR006505597 (SPR)s00500-018-3371-y-e DE-627 ger DE-627 rakwb eng Huang, Jen-Wei verfasserin aut Unknown but interesting recommendation using social penetration 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 Recommendation system (dpeaa)DE-He213 Unknown but interesting (dpeaa)DE-He213 Ma, Hao-Shang verfasserin aut Chung, Chih-Chin verfasserin aut Jian, Zhi-Jia verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2018), 16 vom: 11. Juli, Seite 7249-7262 (DE-627)SPR006469531 nnns volume:23 year:2018 number:16 day:11 month:07 pages:7249-7262 https://dx.doi.org/10.1007/s00500-018-3371-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2018 16 11 07 7249-7262 |
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10.1007/s00500-018-3371-y doi (DE-627)SPR006505597 (SPR)s00500-018-3371-y-e DE-627 ger DE-627 rakwb eng Huang, Jen-Wei verfasserin aut Unknown but interesting recommendation using social penetration 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 Recommendation system (dpeaa)DE-He213 Unknown but interesting (dpeaa)DE-He213 Ma, Hao-Shang verfasserin aut Chung, Chih-Chin verfasserin aut Jian, Zhi-Jia verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2018), 16 vom: 11. Juli, Seite 7249-7262 (DE-627)SPR006469531 nnns volume:23 year:2018 number:16 day:11 month:07 pages:7249-7262 https://dx.doi.org/10.1007/s00500-018-3371-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2018 16 11 07 7249-7262 |
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10.1007/s00500-018-3371-y doi (DE-627)SPR006505597 (SPR)s00500-018-3371-y-e DE-627 ger DE-627 rakwb eng Huang, Jen-Wei verfasserin aut Unknown but interesting recommendation using social penetration 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 Recommendation system (dpeaa)DE-He213 Unknown but interesting (dpeaa)DE-He213 Ma, Hao-Shang verfasserin aut Chung, Chih-Chin verfasserin aut Jian, Zhi-Jia verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2018), 16 vom: 11. Juli, Seite 7249-7262 (DE-627)SPR006469531 nnns volume:23 year:2018 number:16 day:11 month:07 pages:7249-7262 https://dx.doi.org/10.1007/s00500-018-3371-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2018 16 11 07 7249-7262 |
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10.1007/s00500-018-3371-y doi (DE-627)SPR006505597 (SPR)s00500-018-3371-y-e DE-627 ger DE-627 rakwb eng Huang, Jen-Wei verfasserin aut Unknown but interesting recommendation using social penetration 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 Recommendation system (dpeaa)DE-He213 Unknown but interesting (dpeaa)DE-He213 Ma, Hao-Shang verfasserin aut Chung, Chih-Chin verfasserin aut Jian, Zhi-Jia verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2018), 16 vom: 11. Juli, Seite 7249-7262 (DE-627)SPR006469531 nnns volume:23 year:2018 number:16 day:11 month:07 pages:7249-7262 https://dx.doi.org/10.1007/s00500-018-3371-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2018 16 11 07 7249-7262 |
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10.1007/s00500-018-3371-y doi (DE-627)SPR006505597 (SPR)s00500-018-3371-y-e DE-627 ger DE-627 rakwb eng Huang, Jen-Wei verfasserin aut Unknown but interesting recommendation using social penetration 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 Recommendation system (dpeaa)DE-He213 Unknown but interesting (dpeaa)DE-He213 Ma, Hao-Shang verfasserin aut Chung, Chih-Chin verfasserin aut Jian, Zhi-Jia verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2018), 16 vom: 11. Juli, Seite 7249-7262 (DE-627)SPR006469531 nnns volume:23 year:2018 number:16 day:11 month:07 pages:7249-7262 https://dx.doi.org/10.1007/s00500-018-3371-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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. |
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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. |
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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. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR006505597</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201124002907.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2018 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-018-3371-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR006505597</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-018-3371-y-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Huang, Jen-Wei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Unknown but interesting recommendation using social penetration</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Social network analysis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Recommendation system</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Unknown but interesting</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ma, Hao-Shang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chung, Chih-Chin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jian, Zhi-Jia</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Soft Computing</subfield><subfield code="d">Springer-Verlag, 2003</subfield><subfield code="g">23(2018), 16 vom: 11. Juli, Seite 7249-7262</subfield><subfield code="w">(DE-627)SPR006469531</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:23</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:16</subfield><subfield code="g">day:11</subfield><subfield code="g">month:07</subfield><subfield code="g">pages:7249-7262</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-018-3371-y</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">23</subfield><subfield code="j">2018</subfield><subfield code="e">16</subfield><subfield code="b">11</subfield><subfield code="c">07</subfield><subfield code="h">7249-7262</subfield></datafield></record></collection>
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