Personalized video recommendation based on cross-platform user modeling

Author(s):  
Zhengyu Deng ◽  
Jitao Sang ◽  
Changsheng Xu
2019 ◽  
Vol 9 (18) ◽  
pp. 3858
Author(s):  
Jiafeng Li ◽  
Chenhao Li ◽  
Jihong Liu ◽  
Jing Zhang ◽  
Li Zhuo ◽  
...  

With the explosive growth of mobile videos, helping users quickly and effectively find mobile videos of interest and further provide personalized recommendation services are the developing trends of mobile video applications. Mobile videos are characterized by their wide variety, single content, and short duration, and thus traditional personalized video recommendation methods cannot produce effective recommendation performance. Therefore, a personalized mobile video recommendation method is proposed based on user preference modeling by deep features and social tags. The main contribution of our work is three-fold: (1) deep features of mobile videos are extracted by an improved exponential linear units-3D convolutional neural network (ELU-3DCNN) for representing video content; (2) user preference is modeled by combining user preference for deep features with user preference for social tags that are respectively modeled by maximum likelihood estimation and exponential moving average method; (3) a personalized mobile video recommendation system based on user preference modeling is built after detecting key frames with a differential evolution optimization algorithm. Experiments on YouTube-8M dataset have shown that our method outperforms state-of-the-art methods in terms of both precision and recall of personalized mobile video recommendation.


Author(s):  
Shengze Yu ◽  
Xin Wang ◽  
Wenwu Zhu ◽  
Peng Cui ◽  
Jingdong Wang

Cross-platform recommendation aims to improve recommendation accuracy through associating information from different platforms. Existing cross-platform recommendation approaches assume all cross-platform information to be consistent with each other and can be aligned. However, there remain two unsolved challenges: i) there exist inconsistencies in cross-platform association due to platform-specific disparity, and ii) data from distinct platforms may have different semantic granularities. In this paper, we propose a cross-platform association model for cross-platform video recommendation, i.e., Disparity-preserved Deep Cross-platform Association (DCA), taking platform-specific disparity and granularity difference into consideration. The proposed DCA model employs a partially-connected multi-modal autoencoder, which is capable of explicitly capturing platform-specific information, as well as utilizing nonlinear mapping functions to handle granularity differences. We then present a cross-platform video recommendation approach based on the proposed DCA model. Extensive experiments for our cross-platform recommendation framework on real-world dataset demonstrate that the proposed DCA model significantly outperform existing cross-platform recommendation methods in terms of various evaluation metrics.


Author(s):  
Qinghua Huang ◽  
Bisheng Chen ◽  
Jingdong Wang ◽  
Tao Mei

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Ko-Hsun Huang ◽  
Yi-Shin Deng ◽  
Ming-Chuen Chuang

User modeling and profiling has been used to evaluate systems and predict user behaviors for a considerable time. Models and profiles are generally constructed based on studies of users’ behavior patterns, cognitive characteristics, or demographic data and provide an efficient way to present users’ preferences and interests. However, such modeling focuses on users’ interactions with a system and cannot support complicated social interaction, which is the emerging focus of serious games, educational hypermedia systems, experience, and service design. On the other hand, personas are used to portray and represent different groups and types of users and help designers propose suitable solutions in iterative design processes. However, clear guidelines and research approaches for developing useful personas for large-scale and complex social networks have not been well established. In this research, we reflect on three different design studies related to social interaction, experience, and cross-platform service design to discuss multiple ways of identifying both direct users and invisible users in design research. In addition, research methods and attributes to portray users are discussed.


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