基于语义网的个性化信息检索模型研究

本文提出了一个基于语义网的个性化信息检索模型以实现用户的个性化信息检索。文章论述了该模型的模块组成，并对模型的功能模块进行了分析。     

基于本体的政务信息资源个性化应用研究

随着我国电子政务信息化建设的逐渐深入,对信息资源服务的需求呈现出与传统信息服务需求不同的特点。本文研究了电子政务信息资源的个性化应用,提炼出新时期信息资源服务应该具有的特征,针对个性化应用中存在的问题,基于《政务信息资源目录体系国家标准》,提出了一种基于本体的政务信息资源个性化应用模型,并探讨了该模型的应用前景。     

基于社会化群体作用的信息聚合服务

信息聚合服务在一定程度卜提高了用户获取信息的效率和质量,但越来越多且越来越"碎片"化的信息分散了人们的注意力,纯粹的信息聚合服务已不能有效地进行信息的质量控制、无法满足用户的社会化需求.在分析当前社会化信息推荐聚合的基础上,结合社会化的群体作用,构建基于社会化群体作用的信息推荐聚合服务模型,并以"玩聚SR"为例阐述了其实现的策略.研究表明,社会化推荐聚合服务可以有效地提高信息推荐的准确度,将成为互联网环境下挖掘有价值信息的有效方式.     

高校图书馆资源荐购服务探讨

资源荐购是高校图书馆文献采访工作中的重要举措。荐购所采用的各种模式在开展方法、受众人群、荐购效果及采访馆员的工作效率等方面都有各自的特点,只有保持各种模式并存才能使资源荐购更好地为读者服务。资源荐购服务开展过程中普遍存在参与读者少、荐购资源与馆藏原则之间产生矛盾等问题,通过多种途径扩大宣传、把读者的需要放在第一位是解决问题的必要措施。     

通过实施推荐系统强化图书馆目录的实用性——以德国卡尔斯鲁厄大学图书馆为例

2002年至2007年间,DFG资助德国卡尔斯鲁厄大学图书馆开发了Bib Tip推荐系统,它是一种有益于增加图书馆目录参考分量的工具。BibTip应用了Web2．0技术,有利于读者向导门户——图书馆目录的开发。BibTip是在统计模式的基础上建立起来的,它可以引导读者用户从检索一条书目记录而附带检索到目录中拥有的相似文献记录。文章对卡尔斯鲁厄大学的Bib Tip进行了介绍,并对其在图书馆中的应用进行了研究。     

EIP的功能与实现技术研究

在讨论EIP产生与发展的基础上，分析了EIP的概念和特点，介绍了EIP的主要功能，并对其实现技术进行了研究。     

信息技术与个性化教学模式探讨

个性化教学的思想和实践经历了从古代、近代、现代、当代的漫长历程，在其漫长的发展历程中,信息技术为个性化教学提供了有力的物质基础和技术支持。文章从信息技术的角度,讨论了技术辅助的个性化教学模式,探讨了网络环境下的个性化教学模式,并分析了和这个模式相关的一些问题。     

外审专家信息的更新与维护

【目的】提出一种切实可行的外审专家信息更新方法,解决稿件送审困难的问题。【方法】 通过分析编委对期刊提出的意见,确定外审专家信息缺失是影响编委送审的主要问题,得出外审专家信息维护的必要性。通过对获取编委信息渠道的讨论及对微软办公软件的研究,提出外审专家信息更新维护的方法。将提出方法用到2013-2014年为IJAC审理过稿件的外审专家数据库上。【结果】为了评估提出方法的性能,定义了信息缺失率和期刊退回率。数据表明,通过提出的信息更新方法,外审专家的信息缺失率为9.14%,即90.86% 的外审专家信息不缺失。IJAC为这90.86% 的外审专家寄送样刊,期刊的退回率仅为0.99%。可以看出,提出的外审专家信息更新方法可以极大地提高外审专家信息的全面性和准确性。【结论】 面对外审专家信息缺失的难题,提出的方法可以有效地解决由于信息缺失导致的稿件送审难题,进而促进期刊稿件质量的提升和编辑部的长远发展。     

Personalized fashion outfit generation with user coordination preference learning

This paper focuses on personalized outfit generation, aiming to generate compatible fashion outfits catering to given users. Personalized recommendation by generating outfits of compatible items is an emerging task in the recommendation community with great commercial value but less explored. The task requires to explore both user-outfit personalization and outfit compatibility, any of which is challenging due to the huge learning space resulted from large number of items, users, and possible outfit options. To specify the user preference on outfits and regulate the outfit compatibility modeling, we propose to incorporate coordination knowledge in fashion. Inspired by the fact that users might have coordination preference in terms of category combination, we first define category combinations as templates and propose to model user-template relationship to capture users’ coordination preferences. Moreover, since a small number of templates can cover the majority of fashion outfits, leveraging templates is also promising to guide the outfit generation process. In this paper, we propose Template-guided Outfit Generation (TOG) framework, which unifies the learning of user-template interaction, user–item interaction and outfit compatibility modeling. The personal preference modeling and outfit generation are organically blended together in our problem formulation, and therefore can be achieved simultaneously. Furthermore, we propose new evaluation protocols to evaluate different models from both the personalization and compatibility perspectives. Extensive experiments on two public datasets have demonstrated that the proposed TOG can achieve preferable performance in both evaluation perspectives, namely outperforming the most competitive baseline BGN by 7.8% and 10.3% in terms of personalization precision on iFashion and Polyvore datasets, respectively, and improving the compatibility of the generated outfits by over 2%.     

PERSON: Personalized information retrieval evaluation based on citation networks

Despite the importance of personalization in information retrieval, there is a big lack of standard datasets and methodologies for evaluating personalized information retrieval (PIR) systems, due to the costly process of producing such datasets. Subsequently, a group of evaluation frameworks (EFs) have been proposed that use surrogates of the PIR evaluation problem, instead of addressing it directly, to make PIR evaluation more feasible. We call this group of EFs, indirect evaluation frameworks. Indirect frameworks are designed to be more flexible than the classic (direct) ones and much cheaper to be employed. However, since there are many different settings and methods for PIR, e.g., social-network-based vs. profile-based PIR, and each needs some special kind of data to do the personalization based on, not all the evaluation frameworks are applicable to all the PIR methods. In this paper, we first review and categorize the frameworks that have already been introduced for evaluating PIR. We further propose a novel indirect EF based on citation networks (called PERSON), which allows repeatable, large-scale, and low-cost PIR experiments. It is also more information-rich compared to the existing EFs and can be employed in many different scenarios. The fundamental idea behind PERSON is that in each document (paper) d, the cited documents are generally related to d from the perspective of d’s author(s). To investigate the effectiveness of the proposed EF, we use a large collection of scientific papers. We conduct several sets of experiments and demonstrate that PERSON is a reliable and valid EF. In the experiments, we show that PERSON is consistent with the traditional Cranfield-based evaluation in comparing non-personalized IR methods. In addition, we show that PERSON can correctly capture the improvements made by personalization. We also demonstrate that its results are highly correlated with those of another salient EF. Our experiments on some issues about the validity of PERSON also show its validity. It is also shown that PERSON is robust w.r.t. its parameter settings.