| ²é¿´: 1964 | »Ø¸´: 5 | ||
| µ±Ç°Ö»ÏÔʾÂú×ãÖ¸¶¨Ìõ¼þµÄ»ØÌû£¬µã»÷ÕâÀï²é¿´±¾»°ÌâµÄËùÓлØÌû | ||
perry_zhangгæ (ÕýʽдÊÖ)
|
[ÇóÖú]
ÂÛÎÄÊÇ·ñÒѱ»¼ìË÷ÇóÖú£¡ ÒÑÓÐ2È˲ÎÓë
|
|
|
±¾ÈËз¢±íÁ˼¸ÆªSCIÂÛÎÄ£¬·³Çë°ïæ²é¿´Ò»ÏÂÊÇ·ñÒѱ»SCI¼ìË÷£¬Ð»Ð»£¡ [1] Zhipeng Zhang, Yasuo Kudo, Tetsuya Murai, Yonggong Ren. Improved covering-based collaborative filtering for new users¡¯ personalized recommendations[J]. Knowledge and Information Systems, 2020, 62: 3133¨C3154. [2] Zhipeng Zhang, Yao Zhang, Yonggong Ren. Employing neighborhood reduction for alleviating sparsity and cold start problems in user-based collaborative filtering[J]. Information Retrieval Journal, 2020, 23: 449¨C472. [3] Zhipeng Zhang, Yasuo Kudo, Tetsuya Murai, Yonggong Ren. Addressing completed new item cold-start recommendation: A niche item-based collaborative filtering via interrelationship mining[J]. Applied Sciences-Basel, 2019, 9, 1894. [4] Zhipeng Zhang, Yasuo Kudo, Tetsuya Murai, Yonggong Ren. Enhancing recommendation accuracy of item-based collaborative filtering via item-variance weighting[J]. Applied Sciences-Basel, 2019, 9, 1928. |
» ²ÂÄãϲ»¶
¹¤¿Æ08-»úеר˶-Çóµ÷¼Á
ÒѾÓÐ3È˻ظ´
Ò»Ö¾Ô¸ËÕÖÝ´óѧ²ÄÁϹ¤³Ì£¨085601£©×¨Ë¶ÓпÆÑоÀúÈýÏî¹ú½±Á½¸öʵÓÃÐÍרÀûÒ»ÏîÊ¡¼¶Á¢Ïî
ÒѾÓÐ9È˻ظ´
²ÄÁϹ¤³Ì302·ÖÇóµ÷¼Á
ÒѾÓÐ8È˻ظ´
315Çóµ÷¼Á
ÒѾÓÐ14È˻ظ´
307·Ö²ÄÁÏרҵÇóµ÷¼Á
ÒѾÓÐ7È˻ظ´
²ÄÁϵ÷¼Á
ÒѾÓÐ11È˻ظ´
262Çóµ÷¼Á
ÒѾÓÐ6È˻ظ´
Ò»Ö¾Ô¸±±¾©½»Í¨´óѧ²ÄÁϹ¤³Ì×Ü·Ö358Çóµ÷¼Á
ÒѾÓÐ4È˻ظ´
Ò»Ö¾Ô¸Çà¿Æ085500£¬³õÊÔ295·Ö£¬¹«¹²¿Î213·Ö
ÒѾÓÐ3È˻ظ´
304Çóµ÷¼Á
ÒѾÓÐ3È˻ظ´
4Â¥2020-09-13 23:00:20
knight7120
Ìú¸Ëľ³æ (ÕýʽдÊÖ)
- Ó¦Öú: 1 (Ó×¶ùÔ°)
- ½ð±Ò: 5984.1
- É¢½ð: 44
- ºì»¨: 2
- Ìû×Ó: 452
- ÔÚÏß: 190.9Сʱ
- ³æºÅ: 616065
- ×¢²á: 2008-10-03
- ÐÔ±ð: GG
- רҵ: Ãñ×åҩѧ
2Â¥2020-09-13 21:49:45
3Â¥2020-09-13 21:53:43
bobvan
ÖÁ×ðľ³æ (ÎÄ̳¾«Ó¢)
- SEPI: 1
- Ó¦Öú: 4798 (¸±½ÌÊÚ)
- ½ð±Ò: 32669.2
- É¢½ð: 20785
- ºì»¨: 351
- Ìû×Ó: 11197
- ÔÚÏß: 1517.9Сʱ
- ³æºÅ: 2171443
- ×¢²á: 2012-12-07
- רҵ: ´ß»¯»¯Ñ§
¡¾´ð°¸¡¿Ó¦Öú»ØÌû
¡ï ¡ï ¡ï ¡ï ¡ï ¡ï ¡ï ¡ï ¡ï ¡ï
¸Ðл²ÎÓ룬ӦÖúÖ¸Êý +1
perry_zhang: ½ð±Ò+10, ¡ï¡ï¡ï¡ï¡ï×î¼Ñ´ð°¸ 2020-09-14 06:57:28
¸Ðл²ÎÓ룬ӦÖúÖ¸Êý +1
perry_zhang: ½ð±Ò+10, ¡ï¡ï¡ï¡ï¡ï×î¼Ñ´ð°¸ 2020-09-14 06:57:28
|
1) Improved covering-based collaborative filtering for new users¡¯ personalized recommendations Èë²ØºÅ: WOS:000544063400008 2) Employing neighborhood reduction for alleviating sparsity and cold start problems in user-based collaborative filtering Èë²ØºÅ: WOS:000541331600001 3) Addressing complete new item cold-start recommendation: A niche item-based collaborative filtering via interrelationship mining Èë²ØºÅ: WOS:000469756000173 4) Enhancing recommendation accuracy of item-based collaborative filtering via item-variance weighting Èë²ØºÅ: WOS:000469756000207 |
5Â¥2020-09-14 00:43:13














»Ø¸´´ËÂ¥
120