A M-Learning Content Recommendation Service by Exploiting Mobile Social Interactions
نویسندگان
چکیده
منابع مشابه
Exploiting Social Interactions in Mobile Systems
The popularity of handheld devices has created a flurry of research activity into new protocols and applications that can handle and exploit the defining characteristic of this new environment – user mobility. In addition to mobility, another defining characteristic of mobile systems is user social interaction. This paper investigates how mobile systems could exploit people’s social interaction...
متن کاملExploiting FrameNet for Content-Based Book Recommendation
Adding semantic knowledge to a content-based recommender helps to better understand the items and user representations. Most recent research has focused on examining the added value of adding semantic features based on structured web data, in particular Linked Open Data (LOD). In this paper, we focus in contrast on semantic feature construction from text, by incorporating features based on sema...
متن کاملExploiting Space-Time Status for Service Recommendation
The prevalence of smart devices allows people to record their spacetime status. This paper focuses on exploiting user space-time status and the related semantic information for service recommendation. Firstly, event DAG is employed to organize the space-time information generated based on the service invocation history. Generation algorithm of the event DAG is then proposed. Secondly, a novel c...
متن کاملLearning Landmarks by Exploiting Social Media
This paper introduces methods for automatic annotation of landmark photographs via learning textual tags and visual features of landmarks from landmark photographs that are appropriately location-tagged from social media. By analyzing spatial distributions of text tags from Flickr’s geotagged photos, we identify thousands of tags that likely refer to landmarks. Further verification by utilizing...
متن کاملAutomatic Hashtag Recommendation in Social Networking and Microblogging Platforms Using a Knowledge-Intensive Content-based Approach
In social networking/microblogging environments, #tag is often used for categorizing messages and marking their key points. Also, since some social networks such as twitter apply restrictions on the number of characters in messages, #tags can serve as a useful tool for helping users express their messages. In this paper, a new knowledge-intensive content-based #tag recommendation system is intr...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Learning Technologies
سال: 2014
ISSN: 1939-1382,2372-0050
DOI: 10.1109/tlt.2014.2323053