A hybrid recommender system based non-common items in social media

Yu, Chi-Chih; Yamaguchi, Toru; Takama, Yasufumi

doi:10.1109/icawst.2013.6765443

Cited by 1 publication

(1 citation statement)

References 10 publications

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“…This combined score is used to determine the item's suitability for recommendation. The authors of [58] further extend the weighted techniques to user-to-user, user-to-tag and user-to-item relations in social media. The aggregated scores are the predicted preference score of a user on an item.…”

Section: Hybrid Recommender Systemmentioning

confidence: 99%

Impact of temporal context on recommender systems along global timeline

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Sun Aixin, for his continuous guidance and support throughout my research journey. Prof. Sun always provides valuable insights and inspiring ideas during our discussions. He inspires me to perceive recommender system from a new perspective, and motivates critical thinking in research problems. Beyond his expertise, Prof. Sun is also a friendly and kind person. My PhD study has been interesting and fruitful because of him. Furthermore, I would like to express my appreciation to Prof. Zhang Jie, for his continuous help during my research. Prof. Zhang provides valuable feedback and constructive comments during our discussions. Moreover, he provided opportunities for me to work in real-life recommendation project, which further broadens my knowledge in my research area. Lastly, I would like to thank my parents, my husband Zhao Yue and my friends, Zhang Yixiang, Shi Jiachen and Xia Fan, my pet friends Pepper, Bun and Button for accompanying me throughout the years. Their encouragement and belief in my abilities have been a source of strength throughout my research journey.

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Section: Hybrid Recommender Systemmentioning

confidence: 99%

Impact of temporal context on recommender systems along global timeline

View full text Add to dashboard Cite

show abstract

A hybrid recommender system based non-common items in social media

Cited by 1 publication

References 10 publications

Impact of temporal context on recommender systems along global timeline

Impact of temporal context on recommender systems along global timeline

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