一种面向大规模P2P系统的快速搜索算法

提出一种面向大规模P2P系统的概率搜索小组(probabilistic search team,简称PST)算法.各节点首先发布本节点的资源共享信息,并基于分布式丢弃Bloom Filter技术(distributed discarding bloom filter,简称DDBF)对从其他节点收到的信息进行保存和转发PST算法把RW算法中漫步者的概念扩充为搜索小组通过聚合各小组在搜索过程中获得的资源信息,PST算法实现了多个小组之间相互协同的并行搜索.分析模拟结果表明,PST算法在保持低定位开销的同时取得了较好的定位性能.     

A query expansion and user profile enrichment approach to improve the performance of recommender systems operating on a folksonomy

In this paper we propose a query expansion and user profile enrichment approach to improve the performance of recommender systems operating on a folksonomy, storing and classifying the tags used to label a set of available resources. Our approach builds and maintains a profile for each user. When he submits a query (consisting of a set of tags) on this folksonomy to retrieve a set of resources of his interest, it automatically finds further “authoritative” tags to enrich his query and proposes them to him. All “authoritative” tags considered interesting by the user are exploited to refine his query and, along with those tags directly specified by him, are stored in his profile in such a way to enrich it. The expansion of user queries and the enrichment of user profiles allow any content-based recommender system operating on the folksonomy to retrieve and suggest a high number of resources matching with user needs and desires. Moreover, enriched user profiles can guide any collaborative filtering recommender system to proactively discover and suggest to a user many resources relevant to him, even if he has not explicitly searched for them.     

Distributed recommender for peer-to-peer knowledge sharing

A novel model of distributed knowledge recommender system is proposed to facilitate knowledge sharing among collaborative team members. Different from traditional recommender systems in the client-server architecture, our model is oriented to the peer-to-peer (P2P) environment without the centralized control. Among the P2P network of collaborative team members, each peer is deployed with one distributed knowledge recommender, which can supply proper knowledge resources to peers who may need them. This paper investigates the key techniques for implementing the distributed knowledge recommender model. Moreover, a series of simulation-based experiments are conducted by using the data from a real-world collaborative team in an enterprise. The experimental results validate the efficiency of the proposed model. This research paves the way for developing platforms that can share and manage large-scale distributed knowledge resources. This study also provides a new framework for simulating and studying individual or organizational behaviors of knowledge sharing in a collaborative team.     

A Review of Ontology‐Based Tag Recommendation Approaches

Tag recommender schemes suggest related tags for an untagged resource and better tag suggestions to tagged resources. Tagging is very important if the user identifies the tag that is more precise to use in searching interesting blogs. There is no clear information regarding the meaning of each tag in a tagging process. An user can use various tags for the same content, and he can also use new tags for an item in a blog. When the user selects tags, the resultant metadata may comprise homonyms and synonyms. This may cause an improper relationship among items and ineffective searches for topic information. The collaborative tag recommendation allows a set of freely selected text keywords as tags assigned by users. These tags are imprecise, irrelevant, and misleading because there is no control over the tag assignment. It does not follow any formal guidelines to assist tag generation, and tags are assigned to resources based on the knowledge of the users. This causes misspelled tags, multiple tags with the same meaning, bad word encoding, and personalized words without common meaning. This problem leads to miscategorization of items, irrelevant search results, wrong prediction, and their recommendations. Tag relevancy can be judged only by a specific user. These aspects could provide new challenges and opportunities to its tag recommendation problem. This paper reviews the challenges to meet the tag recommendation problem. A brief comparison between existing works is presented, which we can identify and point out the novel research directions. The overall performance of our ontology‐based recommender systems is favorably compared to other systems in the literature.     

Semantic similarity measures for enhancing information retrieval in folksonomies

Collaborative tagging systems, also known as folksonomies, enable a user to annotate various web resources with a free set of tags for sharing and searching purposes. Tags in a folksonomy reflect users’ collaborative cognition about information. Tags play an important role in a folksonomy as a means of indexing information to facilitate search and navigation of resources. However, the semantics of the tags, and therefore the semantics of the resources, are neither known nor explicitly stated. It is therefore difficult for users to find related resources due to the absence of a consistent semantic meaning among tags. The shortage of relevant tags increases data sparseness and decreases the rate of information extraction with respect to user queries. Defining semantic relationships between tags, resources, and users is an important research issue for the retrieval of related information from folksonomies. In this research, a method for finding semantic relationships among tags is proposed. The present study considers not only the pairwise relationships between tags, resources, and users, but also the relationships among all three. Experimental results using real datasets from Flickr and Del.icio.us show that the method proposed here is more effective than previous methods such as LCH, JCN, and LIN in finding semantic relationships among tags in a folksonomy.     

Community-Aware Resource Profiling for Personalized Search in Folksonomy

In recent years,there is a fast proliferation of collaborative tagging(a.k.a.folksonomy) systems in Web 2.0 communities.With the increasingly large amount of data,how to assist users in searching their interested resources by utilizing these semantic tags becomes a crucial problem.Collaborative tagging systems provide an environment for users to annotate resources,and most users give annotations according to their perspectives or feelings.However,users may have different perspectives or feelings on resources,e.g.,some of them may share similar perspectives yet have a conflict with others.Thus,modeling the profile of a resource based on tags given by all users who have annotated the resource is neither suitable nor reasonable.We propose,to tackle this problem in this paper,a community-aware approach to constructing resource profiles via social filtering.In order to discover user communities,three different strategies are devised and discussed.Moreover,we present a personalized search approach by combining a switching fusion method and a revised needs-relevance function,to optimize personalized resources ranking based on user preferences and user issued query.We conduct experiments on a collected real life dataset by comparing the performance of our proposed approach and baseline methods.The experimental results verify our observations and effectiveness of proposed method.     

Recommender systems for personal knowledge management in collaborative environments

Personal knowledge management (PKM) is different from the traditional centralized knowledge management (KM) modes. The PKM is suitable for distributed collaborative KM environments. This paper makes an explorative study on the PKM, and analyzes various forms of personal knowledge resources in the product development process. Then a model of recommender systems for PKM is proposed for knowledge sharing among members in the collaborative environment. The key function of the PKM recommender systems is to supply potentially useful personal knowledge resources from the sites where these knowledge resources are created to the sites where the members may need the knowledge. The PKM is in a mode of distributed control rather than a mode of centralized control, which is widely used by traditional KM methods and tools. This study paves a way for developing an advanced mode of KM platforms for PKM sharing in collaborative environments.     

A collaborative desktop tagging system for group knowledge management based on concept space

The advent of internet has led to a significant growth in the amount of information available, resulting in information overload, i.e. individuals have too much information to make a decision. To resolve this problem, collaborative tagging systems form a categorization called folksonomy in order to organize web resources. A folksonomy aggregates the results of personal free tagging of information and objects to form a categorization structure that applies utilizes the collective intelligence of crowds. Folksonomy is more appropriate for organizing huge amounts of information on the Web than traditional taxonomies established by expert cataloguers. However, the attributes of collaborative tagging systems and their folksonomy make them impractical for organizing resources in personal environments.This work designs a desktop collaborative tagging (DCT) system that enables collaborative workers to tag their documents. This work proposes an application in patent analysis based on the DCT system. Folksonomy in DCT is built by aggregating personal tagging results, and is represented by a concept space. Concept spaces provide synonym control, tag recommendation and relevant search. Additionally, to protect privacy of authors and to decrease the transmission cost, relations between tagged and untagged documents are constructed by extracting document’s features rather than adopting the full text.Experimental results reveal that the adoption rate of recommended tags for new documents increases by 10% after users have tagged five or six documents. Furthermore, DCT can recommend tags with higher adoption rates when given new documents with similar topics to previously tagged ones. The relevant search in DCT is observed to be superior to keyword search when adopting frequently used tags as queries. The average precision, recall, and F-measure of DCT are 12.12%, 23.08%, and 26.92% higher than those of keyword searching.DCT allows a multi-faceted categorization of resources for collaborative workers and recommends tags for categorizing resources to simplify categorization easier. Additionally, DCT system provides relevance searching, which is more effective than traditional keyword searching for searching personal resources.     

An analysis of peer similarity for recommendations in P2P systems

In this paper, we propose a novel recommender framework for partially decentralized file sharing Peer-to-Peer systems. The proposed recommender system is based on user-based collaborative filtering. We take advantage from the partial search process used in partially decentralized systems to explore the relationships between peers. The proposed recommender system does not require any additional effort from the users since implicit rating is used. The recommender system also does not suffer from the problems that traditional collaborative filtering schemes suffer from like the Cold start and the Data sparseness. To measure the similarity between peers, we propose Files?? Popularity Based Recommendation (FP) and Asymmetric Peers?? Similarity Based Recommendation with File Popularity (ASFP). We also investigate similarity metrics that were proposed in other fields and adapt them to file sharing P2P systems. We analyze the impact of each similarity metric on the accuracy of the recommendations. Both weighted and non weighted approaches were studied.     

Evaluation and selection of group recommendation strategies for collaborative searching of learning objects

Nowadays, there is a wide variety of e-learning repositories that provide digital resources for education in the form of learning objects. Some of these systems provide recommender systems in order to help users in the search for and selection of the learning objects most appropriate to their individual needs. The search for and recommendation of learning objects are usually viewed as a solitary and individual task. However, a collaborative search can be more effective than an individual search in some situations – for example, when developing a digital course between a group of instructors. The problem of recommending learning objects to a group of users or instructors is much more difficult than the traditional problem of recommending to only one individual. To resolve this problem, this paper proposes a collaborative methodology for searching, selecting, rating and recommending learning objects. Additionally, voting aggregation strategies and meta-learning techniques are used in order to automatically obtain the final ratings without having to reach a consensus between all the instructors. A functional model has been implemented within the DELPHOS hybrid recommender system. Finally, various experiments have been carried out using 50 different groups in order to validate the proposed learning object group recommendation approach.     

基于项目属性的用户聚类协同过滤推荐算法

协同过滤推荐算法是个性化推荐服务系统的关键技术,由于项目空间上用户评分数据的极端稀疏性,传统推荐系统中的用户相似度量算法开销较大并且无法保证项目推荐精度.通过对共同感兴趣的项目属性的相似用户进行聚类,构建了不同项目评价的用户相似性,设计了一种优化的协同过滤推荐算法.实验结果表明,该算法能够有效避免由于数据稀疏性带来的弊端,提高了系统的推荐质量.     

Swallow: Resource and Tag Recommender System Based on Heat Diffusion Algorithm in Social Annotation Systems

Social annotation systems (SAS) allow users to annotate different online resources with keywords (tags). These systems help users in finding, organizing, and retrieving online resources to significantly provide collaborative semantic data to be potentially applied by recommender systems. Previous studies on SAS had been worked on tag recommendation. Recently, SAS‐based resource recommendation has received more attention by scholars. In the most of such systems, with respect to annotated tags, searched resources are recommended to user, and their recent behavior and click‐through is not taken into account. In the current study, to be able to design and implement a more precise recommender system, because of previous users' tagging data and users' current click‐through, it was attempted to work on the both resource (such as web pages, research papers, etc.) and tag recommendation problem. Moreover, by applying heat diffusion algorithm during the recommendation process, more diverse options would present to the user. After extracting data, such as users, tags, resources, and relations between them, the recommender system so called “Swallow” creates a graph‐based pattern from system log files. Eventually, following the active user path and observing heat conduction on the created pattern, user further goals are anticipated and recommended to him. Test results on SAS data set demonstrate that the proposed algorithm has improved the accuracy of former recommendation algorithms.     

Folksonomy‐Based Recommender Systems: A State‐of‐the‐Art Review

Collaborative tagging systems, also known as folksonomies, have grown in popularity over the Web on account of their simplicity to organize several types of content (e.g., Web pages, pictures, and video) using open‐ended tags. The rapid adoption of these systems has led to an increasing amount of users providing information about themselves and, at the same time, a growing and rich corpus of social knowledge that can be exploited by recommendation technologies. In this context, tripartite relationships between users, resources, and tags contained in folksonomies set new challenges for knowledge discovery approaches to be applied for the purposes of assisting users through recommendation systems. This review aims at providing a comprehensive overview of the literature in the field of folksonomy‐based recommender systems. Current recommendation approaches stemming from fields such as user modeling, collaborative filtering, content, and link‐analysis are reviewed and discussed to provide a starting point for researchers in the field as well as explore future research lines.     

On the role of trust in collaborative Web search

Recommender systems combine ideas from information retrieval, user modelling, and artificial intelligence to focus on the provision of more intelligent and proactive information services. As such, recommender systems play an important role when it comes to assisting the user during both routine and specialised information retrieval tasks. Like any good assistant it is important that users can trust in the ability of a recommender system to respond with timely and relevant suggestions. In this paper, we will look at a collaborative recommendation system operating in the domain of Web search. We will show how explicit models of trust can help to inform more reliable recommendations that translate into more relevant search results. Moreover, we demonstrate how the availability of this trust-model facilitates important interface enhancements that provide a means to declare the provenance of result recommendations in a way that will allow searchers to evaluate their likely relevance based on the reputation and trustworthiness of the recommendation partners behind these suggestions.     

Understanding knowledge contributors’ satisfaction in transactional virtual communities: A cost–benefit trade-off perspective

This study investigates knowledge contributors’ satisfaction with a distinct type of virtual communities (i.e., transactional virtual communities, TVCs), where knowledge sharing is guided mainly under the principle of economic exchange, and cost–benefit tradeoff is the primary motive for knowledge sharing. Drawing upon the goal attainment theory, we examine the effects of two types of benefits (i.e., extrinsic and intrinsic) and two types of costs (i.e., actual and opportunity) on knowledge contributors’ satisfaction, and highlight the mediating role of perceived net goal attainment. A field survey with 205 subjects in a TVC in China is conducted to test the research model.     

协同过滤推荐系统中聚类搜索方法研究

最近邻计算是协同过滤方法中直接影响到推荐系统的运行效率和推荐准确率的重要一环。当用户和项目数目达到一定规模的时候,推荐系统的可扩展性明显降低。聚类的方法能在一定程度上弥补这个缺陷,但同时又会带来推荐准确性的下降。提出了一种与信息检索领域中的倒排索引相结合并采用“成员策略”的用户聚类搜索算法,缩短了最近邻计算的时间,实验的结果证明,该方法能在保证推荐正确性的前提下有效改善协同过滤推荐系统的可扩展性。     

Quality based dynamic incentive tagging

Distributed and Parallel Databases - Social tags take an important role in exploratory search. In collaborative tagging systems, users are allowed to annotate resources with tags. The significant...     

XML与信息共享

本文论述了信息共享的关键问题，即如何建立一致的元数据资源描述。作者指出，XML在信息共享方面扮演了重要角色，但在元数据的语义、结构和语法三个方面，XML仅在语法层次上保证了元数据描述语言的一致性，并不能解决信息共享的根本问题。信息共享需要在元数据的交换格式、标记格式、元素内容结构、元素语义和数据元等方面能够互操作，需要建立一套完整的标准体系。作者在文章中提出了目前信息共享面临的问题及其解决途径，对信息共享的标准体系作了概要的描述。