Titlebook: Information Retrieval Technology; 13th Asia Informatio Won-Kyung Sung,Hanmin Jung,Seungbock Lee Conference proceedings 2017 Springer Intern

太平間

書(shū)目名稱Information Retrieval Technology影響因子(影響力)




書(shū)目名稱Information Retrieval Technology影響因子(影響力)學(xué)科排名




書(shū)目名稱Information Retrieval Technology網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Information Retrieval Technology網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Information Retrieval Technology被引頻次




書(shū)目名稱Information Retrieval Technology被引頻次學(xué)科排名




書(shū)目名稱Information Retrieval Technology年度引用




書(shū)目名稱Information Retrieval Technology年度引用學(xué)科排名




書(shū)目名稱Information Retrieval Technology讀者反饋




書(shū)目名稱Information Retrieval Technology讀者反饋學(xué)科排名

圣歌

RALES

Wide & Deep Learning in Job Recommendation: An Empirical Study how to joint-train the two parts of the network? Finally, how to conduct online training with new data? For all of these, we focus on the job recommendation task, which often suffers from the cold-start problem. The experiments give us the answers of these questions.

Innovative

A Neural Network Model for Social-Aware Recommendationwe aim to fill this gap and propose a social-aware neural recommender system. Extensive experiments on real-world datasets demonstrate that our model outperforms state-of-art approaches significantly.

Nonconformist

Automatic Query Generation from Legal Texts for Case Law Retrievalgenerated queries were found to be more effective than the average Boolean queries from experts. However, the best keyword and Boolean queries from experts significantly outperformed automatic queries.

bizarre

Conference proceedings 201717...?..The 17 full papers presented were carefully reviewed and selected from numerous submissions. The final program of AIRS 2017 is divided in the following tracks: IR Infrastructure and Systems; IR Models and Theories; Personalization and Recommendation; Data Mining for IR; and IR Evaluation..

CUR

Amylase

淡紫色花

Fast Exact Algorithm to Solve Continuous Similarity Search for Evolving Queriesidentify such sets very fast with frequency-based inverted lists (FIL). Moreover, we derive the similarity values at . in .(1) time by updating the previous values computed at time .. Experimentally, our exact algorithm runs faster than the previous exact algorithm by one order of magnitude.

maculated