Cloud-Based Mobile Multimedia Recommendation System with User Behavior Information
Recommendation systems mainly focus on three specific domain like that CB filtering, CF-based filtering and Context-aware filtering.In content filtering, using users publishing and Interaction with in social network to calculate the similarity between users.Then recommend items to a user. The multimedia services and contents in the Internet, People usually waste a lot of time to obtain their interests.Mobile devices used in such smart communities have limited storage and can therefore not store a number of multimedia contents for users. Online video sharing systems, out of which YouTube is the most popular, provide features that allow users to post a video as a response to a discussion topic.On multimediasharing video, audio,images websites, such as Flickr, Facebook, and Twitter, users assign tags on the resources. Analyzing the tagging information in former research studies, the users with co-tagging behaviors show high similarity on specific items.online users often click the resources recommended by their concerning users and interesting groups. Based on the implicit relationship of user–user and user–resource in social networks, the recommendation system can achieve better performance and lower time cost.The MapReduce procedure can speed up the existing recommendation algorithm, such as CB, CF, or SNF (social-network-based filter).
Jayshri M. Somwanshi, Prof. Y.B. Gurav