排序方式： 共有73条查询结果，搜索用时 250 毫秒
71.
基于用户行为的全文检索系统个性化研究 总被引：1，自引：0，他引：1
总结国内有关检索系统个性化研究的现状并进行分析,针对全文检索系统个性化服务方面存在的不足提出了基于用户行为全文检索系统模型,阐释了模型中的三个关键问题,包括相关反馈行为评价体系的制定、用户显式隐式行为的获取、用户兴趣建模和基于行为的相关度算法优化,最后列举了基于用户行为的全文检索系统可提供的四项个性化服务内容,包括个性化用户界面、优化检索策略、个性化检索结果、个性化推荐. 相似文献
72.
In this article, we address the measurement of individualized instruction in the context of regular classroom instruction. Our study assessed instructional practices geared towards individualization in German third grade reading lessons by combining self-report data from 621 students, from their teachers (n = 57), and live observations. We then investigated the reliability of these different approaches to measuring individualization as well as the agreement between them. All three approaches yielded reliable indicators of individualized practices, but not all of them corresponded with each other. We found considerable agreement between students and observers, but neither agreed with teachers' self-reports. Upon closer examination, we found that students’ ratings only correlated with teacher ratings that were provided close to the timepoint of interest. This correlation increased when teacher measures were corrected for response tendencies. We conclude with some recommendations for future studies that aim to measure individualized instruction in the classroom. 相似文献
73.
《Information processing & management》2022,59(2):102858
Recommender Systems (RSs) aim to model and predict the user preference while interacting with items, such as Points of Interest (POIs). These systems face several challenges, such as data sparsity, limiting their effectiveness. In this paper, we address this problem by incorporating social, geographical, and temporal information into the Matrix Factorization (MF) technique. To this end, we model social influence based on two factors: similarities between users in terms of common check-ins and the friendships between them. We introduce two levels of friendship based on explicit friendship networks and high check-in overlap between users. We base our friendship algorithm on users’ geographical activity centers. The results show that our proposed model outperforms the state-of-the-art on two real-world datasets. More specifically, our ablation study shows that the social model improves the performance of our proposed POI recommendation system by 31% and 14% on the Gowalla and Yelp datasets in terms of Precision@10, respectively. 相似文献