AXMEDIS: an MPEG-21 based solution for protected cross media content production and distribution

2006 ◽  
Author(s):  
S. Chellini ◽  
T. Martini ◽  
P. Nesi
Keyword(s):  
Media Content ◽  
Cross Media ◽  
Production And Distribution ◽  
Cross Media Content

scholarly journals Your Tweets Reveal What You Like: Introducing Cross-media Content Information into Multi-domain Recommendation

2018 ◽  
Author(s):  
Weizhi Ma ◽  
Min Zhang ◽  
Chenyang Wang ◽  
Cheng Luo ◽  
Yiqun Liu ◽  
...  
Keyword(s):  
Cold Start ◽  
Media Content ◽  
Learning Approaches ◽  
Target Domain ◽  
Underlying Assumption ◽  
Cross Media ◽  
Cross Platform ◽  
Rating Prediction ◽  
Cross Media Content ◽  
Content Information

Cold start is a challenging problem in recommender systems. Many previous studies attempt to utilize extra information from other platforms to alleviate the problem. Most of the leveraged information is on-topic, directly related to users' preferences in the target domain. Thought to be unrelated, users' off-topic content information (such as user tweets) is usually omitted. However, the off-topic content information also helps to indicate the similarity of users on their tastes, interests, and opinions, which matches the underlying assumption of Collaborative Filtering (CF) algorithms. In this paper, we propose a framework to capture the features from user's off-topic content information in social media and introduce them into Matrix Factorization (MF) based algorithms. The framework is easy to understand and flexible in different embedding approaches and MF based algorithms. To the best of our knowledge, there is no previous study in which user's off-topic content in other platforms is taken into consideration. By capturing the cross-platform content including both on-topic and off-topic information, multiple algorithms with several embedding learning approaches have achieved significant improvements in rating prediction on three datasets. Especially in cold start scenarios, we observe greater enhancement. The results confirm our suggestion that off-topic cross-media information also contributes to the recommendation.

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