全文获取类型
收费全文 | 735篇 |
免费 | 6篇 |
专业分类
教育 | 471篇 |
科学研究 | 111篇 |
各国文化 | 11篇 |
体育 | 51篇 |
综合类 | 1篇 |
文化理论 | 4篇 |
信息传播 | 92篇 |
出版年
2024年 | 3篇 |
2023年 | 5篇 |
2022年 | 11篇 |
2021年 | 22篇 |
2020年 | 24篇 |
2019年 | 51篇 |
2018年 | 66篇 |
2017年 | 47篇 |
2016年 | 39篇 |
2015年 | 18篇 |
2014年 | 49篇 |
2013年 | 149篇 |
2012年 | 31篇 |
2011年 | 35篇 |
2010年 | 24篇 |
2009年 | 28篇 |
2008年 | 20篇 |
2007年 | 10篇 |
2006年 | 19篇 |
2005年 | 10篇 |
2004年 | 8篇 |
2003年 | 5篇 |
2002年 | 6篇 |
2001年 | 8篇 |
2000年 | 7篇 |
1999年 | 2篇 |
1998年 | 6篇 |
1997年 | 5篇 |
1995年 | 4篇 |
1993年 | 2篇 |
1992年 | 5篇 |
1991年 | 1篇 |
1990年 | 2篇 |
1989年 | 3篇 |
1987年 | 1篇 |
1986年 | 2篇 |
1985年 | 1篇 |
1984年 | 1篇 |
1983年 | 1篇 |
1979年 | 1篇 |
1978年 | 1篇 |
1977年 | 2篇 |
1975年 | 1篇 |
1974年 | 2篇 |
1973年 | 1篇 |
1971年 | 1篇 |
1903年 | 1篇 |
排序方式: 共有741条查询结果,搜索用时 0 毫秒
741.
A recommender system has an obvious appeal in an environment where the amount of on-line information vastly outstrips any individual’s capability to survey. Music recommendation is considered a popular application area. In order to make personalized recommendations, many collaborative music recommender systems (CMRS) focus on capturing precise similarities among users or items based on user historical ratings. Despite the valuable information from audio features of music itself, however, few studies have investigated how to utilize information extracted directly from music for personalized recommendation in CMRS. In this paper, we describe a CMRS based on our proposed item-based probabilistic model, where items are classified into groups and predictions are made for users considering the Gaussian distribution of user ratings. In addition, this model has been extended for improved recommendation performance by utilizing audio features that help alleviate three well-known problems associated with data sparseness in collaborative recommender systems: user bias, non-association, and cold start problems in capturing accurate similarities among items. Experimental results based on two real-world data sets lead us to believe that content information is crucial in achieving better personalized recommendation beyond user ratings. We further show how primitive audio features can be combined into aggregate features for the proposed CRMS and analyze their influences on recommendation performance. Although this model was developed originally for music collaborative recommendation based on audio features, our experiment with the movie data set demonstrates that it can be applied to other domains. 相似文献