Titlebook: Web and Big Data; Third International Jie Shao,Man Lung Yiu,Bin Cui Conference proceedings 2019 Springer Nature Switzerland AG 2019 artifi

APNEA

Streaming Recommendation Algorithm with User Interest Drift Analysis are embedded to analyze the users’ current interest and preference accurately and timely. To evaluate the performance of our proposed model, the experiments are designed on the popular dataset MovieLens, and different algorithms are compared in streaming environment. The results show that our appro

CLAP

IRATE

Unified Group Recommendation Towards Multiple Criteriaand how to make a trade-off among their preferences for the recommended items is still the main research point. To address these challenges, we present a Unified Group Recommendation (UGR) model, which intertwines the user grouping and group recommendation in a unified multi-objective optimization p

VAN

Unified Group Recommendation Towards Multiple Criteriaand how to make a trade-off among their preferences for the recommended items is still the main research point. To address these challenges, we present a Unified Group Recommendation (UGR) model, which intertwines the user grouping and group recommendation in a unified multi-objective optimization p

encomiast

葡萄糖

A Novel Ensemble Approach for Click-Through Rate Prediction Based on Factorization Machines and Gradation for transforming the raw features to cross-combined features. In addition, we import the attention mechanism to calculate users’ latent attention on different features. To illustrate the performance of GFM, we conduct experiments on two real-world datasets, including a movie dataset and a musi

和平主義者

Latent Path Connected Space Model for Recommendationdegree. Extensive evaluations on four real-world datasets show that our approach outperforms the Matrix Factorization model on rating prediction task especially when the rating data is extremely sparse.

排他

GEST

Which Category Is Better: Benchmarking the RDBMSs and GDBMSsport which category is better in different application scenarios. We conduct extensive experiments over the unified benchmark, and report our findings: (1) RDBMSs are significantly faster for aggregations and order by operations, (2) GDBMSs are shown to be superior for projection, multi-table join a

Pepsin

Which Category Is Better: Benchmarking the RDBMSs and GDBMSsport which category is better in different application scenarios. We conduct extensive experiments over the unified benchmark, and report our findings: (1) RDBMSs are significantly faster for aggregations and order by operations, (2) GDBMSs are shown to be superior for projection, multi-table join a