Fabric3支持分布式事务和恢复（我们依赖于Atomikos，它是一款很优秀的产品，我们非常推荐它），并且支持基于补偿/记忆模型的可靠部署 。
F3 supports XA transactions and recovery (we rely on Atomikos, which is a great product we highly recommend) and reliable deployment based on a compensation/memento model.
本文提出了基于数量化i类理论的人工心理模型的建模方法，并介绍了该模型在个性化商品推荐系统中的应用。
We propose a modeling method of artificial psychology which based on Quantification Theory I and introduce how to apply it in recommender system in this paper.
提出一种基于项目特征模型的协同过滤推荐算法。
A collaborative filtering recommendation algorithm based on the item features model is proposed in this paper.
提出了一种新的基于用户访问路径分析的页面推荐模型。
In this paper, a new Web page recommendation model is proposed, which is based on analyzing user access pattern.
解决推荐问题有三个通常的途径：传统的协同过滤，聚类模型，以及基于搜索的方法。
There are three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods.
最后，论文给出了基于该知识模型的自适应知识点推荐和个性化学习页面生成的算法设计和实现结果。
At last, this paper gives the arithmetic design and the realization of the self-adaptive knowledge recommendation and individually learning pages compose.
目前，基于情景模型的数字图书馆个性化服务主要通过个性化检索和个性化推荐两种方式实现。
At present, there are mainly two ways to implement digital library personalized services based on contextual model, that is, through personalized retrieval and personalized recommendation.
这个模型的核心是基于推荐的信任模型，被组织成信任授权树(TDT)，并以证书链的形式进行授权。
The core of this model is the recommendation-based trust model, organized as a trust delegation Tree (TDT), and the authorization delegation realized by delegation certificate chains.
最后给出了该方案的应用实例，用于表达分布式网络环境中基于直接信任和推荐信任的计算模型。
At last, an example is described about trust inferring and policy between direct trust and recommend trust.
在上述工作的基础上，设计并实现了基于兴趣模型的个性化信息推荐系统。
On the ground of above work, we designed and implemented a personalized information recommendation system of interest-based model.
为了对用户提供更优质的推荐，基于用户兴趣模型的推荐系统的效率显得尤为重要。
The efficiency of recommendation system which is based on user interest model is particularly important for the goal of better quality of the recommendation.
协同过滤技术分为基于内存和基于模型两种，前者的推荐准确度更高，但可扩展性比后者低。
Collaborative filtering can be divided into memory based and model based. The former is more accurate while the latter performs better in scalability.
基于多主题追踪的推荐算法采用多个用户模型表示用户对不同主题的兴趣，并动态更新用户模型以动态反映用户的兴趣变化。
The proposed algorithm used multiple user profiles to represent user's interests in different topics, and dynamically updated user's profile to reflect the changing of user's interests.
分析了现有文章推荐系统中基于关键词向量的用户模型表示方法存在的不足，提出了基于聚类兴趣点的用户模型表示方法。
After analyzing the disadvantages of the user profile based on-keywords vector in the existing document recommendation system, a novel representation of user profile based on clustering was proposed.
分析了现有文章推荐系统中基于关键词向量的用户模型表示方法存在的不足，提出了基于聚类兴趣点的用户模型表示方法。
After analyzing the disadvantages of the user profile based on-keywords vector in the existing document recommendation system, a novel representation of user profile based on clustering was proposed.
应用推荐