Titlebook: Web Technologies and Applications; 18th Asia-Pacific We Feifei Li,Kyuseok Shim,Guanfeng Liu Conference proceedings 2016 Springer Internatio

津貼

Improving Temporal Recommendation Accuracy and Diversity via Long and Short-Term Preference Transferds to improve recommendation performance. In this paper, we present a novel approach to improve personalized recommendation performance with changing user preferences based on temporal dataset. In the approach, we take consideration of different influence of long and short-term user preferences and

廢止

An Approach for Cross-Community Content Recommendation: A Case Study on Dockeredented level. However, the rapid expansion of open source communities results in a lot of redundant contents within the community, and most importantly, among communities since they overlap each other with shared issues. On the one hand, redundant contents that are expressed in informal free texts

增長(zhǎng)

Scalable Private Blocking Technique for Privacy-Preserving Record Linkagesubject in many application areas, including business, government, and health. When we collect data which is about people from these areas, integrating such data across organizations can raise privacy concerns. To prevent privacy breaches, ideally records should be linked in a private way such that

柳樹；枯黃

嫻熟

Finding Frequent Items in Time Decayed Data StreamsSSQ) and Filtered Space Saving with Quasi-heap (FSSQ), are proposed to find the frequently occurring items based on the Quasi-heap structure. Extensive experiments demonstrate the superiority of proposed algorithms in terms of both efficiency (i.e., response time) and effectiveness (i.e., accuracy).

吵鬧

A Target-Dependent Sentiment Analysis Method for Micro-blog Streamslevant parts from the syntax tree. The original recursive neural network model is extended to support target-dependent sentiment analysis better. Experimental results on two corpuses with different targets show that the performance of our method is better than previous methods.

–FER

Finding Frequent Items in Time Decayed Data StreamsSSQ) and Filtered Space Saving with Quasi-heap (FSSQ), are proposed to find the frequently occurring items based on the Quasi-heap structure. Extensive experiments demonstrate the superiority of proposed algorithms in terms of both efficiency (i.e., response time) and effectiveness (i.e., accuracy).

搖晃

miracle

Improving Temporal Recommendation Accuracy and Diversity via Long and Short-Term Preference Transfererm Graph, a graph model modified from Session-based Temporal Graph, to recommend unknown items. Finally,?the experimental results show that our approach achieves important improvements compared to some existing approaches in performance.

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