结合项目相似度的协同过滤算法

传统的协同过滤算法因为数据集稀疏性的增加而导致推荐准确性降低。针对该问题提出一种结合项目相似度的协同过滤推荐算法。首先计算项目之间的相似度,然后根据项目之间相似度,预测用户未评分项目评分估值,以减小目标用户与候选最近邻居所形成的数据集稀疏性,最后根据用户相似度获得项目推荐集。实验结果表明,该算法能提高寻找最近邻居的准确性,从而改善协同过滤的推荐质量。     

Multi-domain collaborative exploration mechanisms for query expansion in an agent-based filtering framework

Novice users often do not have enough domain knowledge to create good queries for searching information on-line. To help alleviate the situation, exploration techniques have been used to increase the diversity of the search results so that not only those explicitly asked will be returned, but also those potentially relevant ones will be returned too. Most existing approaches, such as collaborative filtering, do not allow the level of exploration to be controlled. Consequently, the search results can be very different from what is expected. We propose an exploration strategy that performs intelligent query processing by first searching usable old queries, and then utilising them to adapt the current query, with the hope that the adapted query will be more relevant to the user’s areas of interest. We applied the proposed strategy to the implementation of a personal information assistant (PIA) set up for user evaluation for 3 months. The experimental results showed that the proposed exploration method outperformed collaborative filtering, and mutation and crossover methods by around 25% in terms of the elimination of off-topic results.     

基于知识网格的电子商务推荐系统设计*

提出电子商务网站、知识网格节点和虚拟知识网格服务社区三级结构模型,建立自动实现商品知识获取、聚合和智能推荐的运作机制,设计了基于知识网格的电子商务智能推荐系统结构。     

普适个性化多媒体服务技术综述*

随着数字技术和通信技术的快速发展,越来越多的普适设备能够接收和处理更多的多媒体信息。提供面向普适环境的多媒体个性化服务成为今天研究的热点。从普适个性化多媒体服务研究的不同侧面对其进行了综述,介绍了普适个性化多媒体服务的通用体系结构;对实现普适个性化多媒体服务的关键技术进行了详细的描述,包括描述模型、媒体代码转换与适应技术、用户喜好获取与更新和多媒体推荐技术;最后总结了现有的研究及其不足,指出进一步需要研究的方向。     

Agent-mediated knowledge sharing for intelligent services management

For service-oriented architectures that span multiple businesses, organizations must transfer information back-and-forth about their available services. Because of the potential large volume, it is unreasonable and impractical to expect human practitioners to handle the scale of interactions desired and/or required on a continual basis. Intelligent agents offer the adaptability and flexibility to handle the knowledge transfer that must occur in order to share service offerings. Effectively transferring service-oriented information in this domain requires autonomous systems that adapt to heterogeneous environments. This work introduces an architecture and specialized communication procedures designed for this sort of knowledge sharing environment. We show that these procedures perform reasonably when evaluated with current agent communication technologies.

基于多上下文信息的协同过滤推荐算法

随着电子商务和互联网的发展,数据信息呈爆炸式增长,协同过滤算法作为一种简单而高效的推荐算法,能在一定程度上有效地解决信息爆炸问题。但是传统协同过滤算法仅通过单一评分来挖掘相似用户,推荐效果并不占优势。为了提高个性化推荐的质量,如何充分利用用户(物品)的文本、图片、标签等上下文信息以使数据价值最大化是当前推荐系统亟待解决的问题。对此,提出了一种融合多种类型上下文信息的协同过滤算法。以用户商品交互信息为二部图,根据不同类型上下文的特点构建不同的相似度网络,设计目标函数在多种上下文信息网络的约束下联合矩阵分解,并学得用户商品的表示学习。在多个数据集上进行了充分实验,结果表明,融合多种类型上下文信息的协同过滤算法不仅能有效提高推荐的准确度,而且能在一定程度上解决数据稀疏性问题。     

Context-aware cognitive design assistant: Implementation and study of design rules recommendations

Design rules are an essential interface to facilitate the information exchange between designers and experts. Despite many innovations in Knowledge-Based Engineering and Knowledge Management, unstructured design rules documents are still widely used in the manufacturing industry. Due to the complexity of the design process, these documents often contain hundreds of design rules, applicable in varying design contexts. Searching for the right rules according to a design context is demanding in time and cognitive resources. In this paper, we propose a Context-Aware Cognitive Design Assistant (CACDA) to capture the design context and perform tasks such as the recommendation of design rules, the verification of design solutions, or the automation of design routines. Contrary to existing works in model quality testing, the CACDA uses a proactive approach of design rules application and helps designers to provide error-free designs on first attempt. In this paper, we present the design rules recommendation system of the CACDA, its capabilities and its implementation. Then, to measure the impact of design rules recommendations on the design process, we compare our approach with the use of traditional design rules documents. Results show that the use of the CACDA’s design rules recommendations lower the perceived difficulty of design rules retrieval from 75 to 43.5 on a scale of 100. On average, participants that used the demonstrator successfully applied 8.6 design rules on the 25 applicable design rules of our set. Participants that used unstructured documentation correctly applied 4.3 design rules. The global cognitive weight of the design activity as well as the design rules retrieval performances appear to be unchanged. These results demonstrate the usability of the demonstrator and show a positive impact on the design process and on the quality of CAD models. Future works will focus on the overcome of the main limitations of our current experiments, with a panel of professional designers, a lager design rules set and the implementation of several lacking features of the CACDA into the demonstrator.     

基于深度学习的内容推荐算法研究综述

推荐系统是学习用户偏好,实现个性化推荐的系统化应用技术,在商品购买、影音推荐、关联阅读等多领域得到了广泛的应用。近年来,随着多源异构数据的激增和深度学习的兴起,传统推荐算法中的表征学习模式逐步被深度学习代替。梳理推荐算法的背景和发展趋势,并给出内容推荐的算法思路及其优劣评价,分别介绍多层感知机、自动编码器、卷积神经网络以及循环神经网络等深度学习方法的网络结构和算法优势。从技术应用的视角综述深度学习在内容推荐中的应用现状与研究成果,对不同经典深度推荐算法进行分析与比较。在此基础上,指出深度学习在可解释性、学习效率等方面的不足,并对交叉领域学习、多任务学习、表征学习等未来研究方向进行展望。     

基于层次隐马尔可夫模型和神经网络的个性化推荐算法

传统推荐系统将推荐准确性作为主要目标,而推荐结果的多样性和个性化有所欠缺。对此,设计一种基于层次隐马尔可夫模型和神经网络的推荐算法。采用层次隐马尔可夫模型建模用户喜好和上下文环境的关系,并通过隐马尔可夫模型预测上下文。设计神经网络结构来解决协同过滤推荐的问题,同时神经网络满足贝叶斯个性化排序的条件,实现对推荐列表的个性化排序。实验结果表明,该算法在保持推荐准确性的前提下,提高了推荐的多样性和个性化。     

基于数据挖掘的Web个性化信息推荐系统

基于数据挖掘的Web个性化信息推荐日益成为一个重要的研究课题。文章设计了一个基于数据挖掘的Web个性化信息推荐系统(WPIRS)。在WPIRS中,提出了推荐策略,在推荐策略中考虑针对不同类型的用户采用不同的推荐算法。根据用户是否有新颖信息的需求,WPIRS采用了两种推荐算法。