Folksonomy-based personalized search and ranking in social media services

In recent years, social Web users have been overwhelmed by the huge numbers of social media available. Consequentially, users have trouble finding social media suited to their needs. To help such users retrieve useful social media content, we propose a new model of tag-based personalized searches to enhance not only retrieval accuracy but also retrieval coverage. By leveraging social tagging as a preference indicator, we build two models: (i) a latent tag preference model that reflects how a certain user has assigned tags similar to a given tag and (ii) a latent tag annotation model that captures how users have tagged a certain tag to resources similar to a given resource. We then seamlessly map the tags onto items, depending on an individual user's query, to find the most desirable content relevant to the user's needs. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the art algorithms and show our method's feasibility for personalized searches in social media services.     

Minimal privacy authorization in web services collaboration

With the popularity of Internet technology, web services are becoming the most promising paradigm for distributed computing. This increased use of web services has meant that more and more personal information of consumers is being shared with web service providers, leading to the need to guarantee that the private data of consumers are not illegitimate collected, used and disclosed in services collaboration. This paper studies how to realize the minimal privacy authorization while achieving the functional goals. Initially, this paper uses authorization policies to specify the privacy privileges of the services collaboration, and utilizes the trust relationships among services to make authorization decision. Next, it models the interface behaviors of services by extending the interface automata to support privacy semantics. Furthermore, it quantitatively analyzes the minimum set of privacy privileges which are required by the services to achieve the functional goals, and presents the minimal authorization algorithm, which helps us to automatically derive optimal authorization policies for a services collaboration. Finally, it verifies the correctness and efficiency of the approach proposed by this paper through a case study.     

LotusNet: Tunable privacy for distributed online social network services

The evolution of the role of online social networks in the Web has led to a collision between private, public and commercial spheres that have been inevitably connected together in social networking services since their beginning. The growing awareness on the opaque data management operated by many providers reveals that a privacy-aware service that protects user information from privacy leaks would be very attractive for a consistent portion of users. In order to meet this need we propose LotusNet, a framework for the development of social network services relying on a peer-to-peer paradigm which supports strong user authentication. We tackle the trade-off problem between security, privacy and services in distributed social networks by providing the users the possibility to tune their privacy settings through a very flexible and fine-grained access control system. Moreover, our architecture is provided with a powerful suite of high-level services that greatly facilitates custom application development and mash up.     

一种构建个性化网络购物搜索引擎模型研究*

通过分析在电子商务环境下购物搜索引擎所面临的问题,提出了一种跨网站式的模糊识别多媒体信息购物搜索引擎的模型架构方案,并结合用户个性化的需求进行学习和调整来提高用户的搜索满意度,以提升其购物意愿,进而促进电子商务的发展。运用相关检索指标对该模型进行效能评估,以证明模型的可行性和有效性,并通过分析模型的局限性,提出未来的改进方向。     

基于Web日志的个性化搜索引擎模型的发现*

个性化搜索是指同样的关键字对不同的人返回其感兴趣的搜索结果。对于不同的用户个体,同样的关键字可能有不同含义,如关键字“apple”被爱好音乐的人士理解为Apple iPod,但也会被健康饮食的人士理解为apple fruit。每次用户搜索关键字的过程,都会被记录在网站服务器的后台日志中。通过若干挖掘算法,将Web原始日志信息进行用户识别,会话分组后,提取单一用户多次会话中的搜索关键字关联规则,为实现个性化搜索引擎提供参考。     

Understanding social network site users’ privacy tool use

Every day hundreds of millions of people log into social network sites and deposit terabytes of data as they share status updates, photographs, and more. This article explores how background factors, motivations, and social network site experiences relate to people’s use of social network site technology to protect their privacy. The findings indicate that during technology-mediated communication on social network sites, not only do traditional privacy factors relate to the technological boundaries people enact, but people’s experiences with the mediating technology itself do, too. The results also identify privacy inequalities, in which certain groups are more likely to take advantage of the technology to protect their privacy—suggesting that some individuals’ information and reputations may be more at risk than others’.     

Exploiting temporal information in Web search

Time plays important roles in Web search, because most Web pages contain temporal information and a lot of Web queries are time-related. How to integrate temporal information in Web search engines has been a research focus in recent years. However, traditional search engines have little support in processing temporal-textual Web queries. Aiming at solving this problem, in this paper, we concentrate on the extraction of the focused time for Web pages, which refers to the most appropriate time associated with Web pages, and then we used focused time to improve the search efficiency for time-sensitive queries. In particular, three critical issues are deeply studied in this paper. The first issue is to extract implicit temporal expressions from Web pages. The second one is to determine the focused time among all the extracted temporal information, and the last issue is to integrate focused time into a search engine. For the first issue, we propose a new dynamic approach to resolve the implicit temporal expressions in Web pages. For the second issue, we present a score model to determine the focused time for Web pages. Our score model takes into account both the frequency of temporal information in Web pages and the containment relationship among temporal information. For the third issue, we combine the textual similarity and the temporal similarity between queries and documents in the ranking process. To evaluate the effectiveness and efficiency of the proposed approaches, we build a prototype system called Time-Aware Search Engine (TASE). TASE is able to extract both the explicit and implicit temporal expressions for Web pages, and calculate the relevant score between Web pages and each temporal expression, and re-rank search results based on the temporal-textual relevance between Web pages and queries. Finally, we conduct experiments on real data sets. The results show that our approach has high accuracy in resolving implicit temporal expressions and extracting focused time, and has better ranking effectiveness for time-sensitive Web queries than its competitor algorithms.     

Measuring semantic similarity between words by removing noise and redundancy in web snippets

Semantic similarity measures play important roles in many Web‐related tasks such as Web browsing and query suggestion. Because taxonomy‐based methods can not deal with continually emerging words, recently Web‐based methods have been proposed to solve this problem. Because of the noise and redundancy hidden in the Web data, robustness and accuracy are still challenges. In this paper, we propose a method integrating page counts and snippets returned by Web search engines. Then, the semantic snippets and the number of search results are used to remove noise and redundancy in the Web snippets (‘Web‐snippet’ includes the title, summary, and URL of a Web page returned by a search engine). After that, a method integrating page counts, semantics snippets, and the number of already displayed search results are proposed. The proposed method does not need any human annotated knowledge (e.g., ontologies), and can be applied Web‐related tasks (e.g., query suggestion) easily. A correlation coefficient of 0.851 against Rubenstein–Goodenough benchmark dataset shows that the proposed method outperforms the existing Web‐based methods by a wide margin. Moreover, the proposed semantic similarity measure significantly improves the quality of query suggestion against some page counts based methods. Copyright © 2011 John Wiley & Sons, Ltd.     

On the search behaviour of users in the context of interactive social book search

ABSTRACT

Understanding the search behaviour of online users is among the long-tail practices of Interactive Information Retrieval that helps identify the user information needs. The Interactive Social Book Search (SBS), under the umbrella of Interactive Information Retrieval (IIR), aims to understand the user interactions with book collections and the associated professionally-curated and socially-constructed metadata on the baseline and multistage user interfaces (UIs). This paper reports on the book search behaviour of users by reviewing research publications related to the Interactive SBS published during the last two decades. It presents a holistic view of the overall progress of Interactive SBS by summarising and visualising the experimental structure, search systems, datasets, demographics of participants, and findings to identify the research trends and possible future directions. Based on the collected evidence, it attempts to answer how the search system, user interface (UI), and the nature of tasks affect the book search behaviour of users. The article is the first of its kind that attempts to understand the book search behaviour of users in the context of Social Book Search with implications for usability experts and others working in UI design, web search engines, book search engines, digital libraries, collaborative social cataloguing websites, and e-Commerce applications.     

Protecting personalized privacy against sensitivity homogeneity attacks over road networks in mobile services

Privacy preservation has recently received considerable attention for location-based mobile services. A lot of location cloaking approaches focus on identity and location protection, but few algorithms pay attention to prevent sensitive information disclosure using query semantics. In terms of personalized privacy requirements, all queries in a cloaking set, from some user’s point of view, are sensitive. These users regard the privacy is breached. This attack is called as the sensitivity homogeneity attack. We show that none of the existing location cloaking approaches can effectively resolve this problem over road networks. We propose a (K, L, P)-anonymity model and a personalized privacy protection cloaking algorithm over road networks, aiming at protecting the identity, location and sensitive information for each user. The main idea of our method is first to partition users into different groups as anonymity requirements. Then, unsafe groups are adjusted by inserting relaxed conservative users considering sensitivity requirements. Finally, segments covered by each group are published to protect location information. The efficiency and effectiveness of the method are validated by a series of carefully designed experiments. The experimental results also show that the price paid for defending against sensitivity homogeneity attacks is small.     

基于P2P的个性化Web搜索系统的设计与实现

针对中心化的Web信息搜索系统在覆盖率、及时性、个性化、可扩展性等方面存在的问题,提出了一种基于Peer-to-Peer(P2P)的可扩展、个性化的Web搜索系统PeerBridge。PeerBridge基于分布式哈希表组织大量的网络结点形成有组织的P2P覆盖网络,每个对等体作为一个主题搜索引擎,根据用户兴趣从Web中搜索特定主题相关的信息,而具有相似主题的对等体被聚集在一起形成基于主题的对等体簇,协作进行Web搜索与信息共享。并采用主题驱动的Web爬行、基于语义概念的文档分类、个性化的链接分析和基于主题划分的P2P搜索等机制来改善PeerBridge的性能。     

企业资料上传及查询的设计与实现

本文以德州电业局创一流管理信息系统为例，详细介绍了企业Web站点资料上传，查询的设计与实现方法。     

Exploiting location information for Web search

Most Web pages contain location information, which are usually neglected by traditional search engines. Queries combining location and textual terms are called as spatial textual Web queries. Based on the fact that traditional search engines pay little attention in the location information in Web pages, in this paper we study a framework to utilize location information for Web search. The proposed framework consists of an offline stage to extract focused locations for crawled Web pages, as well as an online ranking stage to perform location-aware ranking for search results. The focused locations of a Web page refer to the most appropriate locations associated with the Web page. In the offline stage, we extract the focused locations and keywords from Web pages and map each keyword with specific focused locations, which forms a set of <keyword, location> pairs. In the second online query processing stage, we extract keywords from the query, and computer the ranking scores based on location relevance and the location-constrained scores for each querying keyword. The experiments on various real datasets crawled from nj.gov, BBC and New York Time show that the performance of our algorithm on focused location extraction is superior to previous methods and the proposed ranking algorithm has the best performance w.r.t different spatial textual queries.     

Designing for privacy and self-presentation in social awareness

Social awareness applications are based on the idea of a group sharing real-time context information via personal and ubiquitous terminals. Studies of such applications have shown that users are not only concerned with the preservation privacy through non-disclosure. Instead, disclosure is manipulated for the constant presentation of self to the group in everyday social situations. Basing on 3 years of research with the mobile social awareness system ContextContacts, established findings in social psychology and ubiquitous computing, we propose a number of design principles to support users in this management of privacy and presentation. These principles are to apply even if disclosure is automated, and include support for lightweight permissions, assuming reciprocity, appearing differently to different audiences, providing for feedback on presentation and allowing lying. These principles are applied in interaction design and protocol engineering for the next version of a mobile awareness system called ContextContacts.

Flink: Semantic Web technology for the extraction and analysis of social networks

We present the Flink system for the extraction, aggregation and visualization of online social networks. Flink employs semantic technology for reasoning with personal information extracted from a number of electronic information sources including web pages, emails, publication archives and FOAF profiles. The acquired knowledge is used for the purposes of social network analysis and for generating a web-based presentation of the community. We demonstrate our novel method to social science based on electronic data using the example of the Semantic Web research community.     

Scalable percolation search on complex networks

We introduce a scalable searching protocol for locating and retrieving content in random networks with heavy-tailed and in particular power-law (PL) degree distributions. The proposed algorithm is capable of finding any content in the network with probability one   in time O(logN)$\mathrm{O}(\log N)$, with a total traffic that provably scales sub-linearly with the network size, N. Unlike other proposed solutions, there is no need to assume that the network has multiple copies of contents; the protocol finds all contents reliably, even if every node in the network starts with a unique content. The scaling behavior of the size of the giant connected component of a random graph with heavy-tailed degree distributions under bond percolation is at the heart of our results. The percolation search algorithm can be directly applied to make unstructured peer-to-peer (P2P) networks, such as Gnutella, Limewire and other file-sharing systems (which naturally display heavy-tailed degree distributions and approximate scale-free network structures), scalable. For example, simulations of the protocol on the limewire crawl number 5 network [Ripeanu et al., Mapping the Gnutella network: properties of large-scale peer-to-peer systems and implications for system design, IEEE Internet Comput. J. 6 (1) (2002)], consisting of over 65,000 links and 10,000 nodes, shows that even for this snapshot network, the traffic can be reduced by a factor of at least 100, and yet achieve a hit-rate greater than 90%.     

基于Web的个性化搜索引擎的研究

分析了传统搜索引擎系统的缺点.设计了一种个性化搜索引擎的体系架构,提出了一种改进的向量空间模型.该模型利用非线性加权的思想来处理特征权值的计算.最后,给出了一种基于改进的向量空间模型的个性化搜索算法,能够针对不同用户提供不同的检索结果.实验结果表明,该结构和算法能够有效地提高搜索引擎的性能,满足用户的个性化需求.     

Approaches and challenges of privacy preserving search over encrypted data

More and more data owners are encouraged to outsource their data onto cloud servers for reducing infrastructure, maintenance cost and also to get ubiquitous access to their stored data. However, security is one issue that discourages data owners from adopting cloud servers for data storage. Searchable Encryption (SE) is one of the few ways of assuring privacy and confidentiality of such data by storing them in encrypted form at the cloud servers. SE enables the data owners and users to search over encrypted data through trapdoors. Most of the user information requirements are fulfilled either through Boolean or Ranked search approaches. This paper aims at understanding how the confidentiality and privacy of information can be guaranteed while processing single and multi-keyword queries over encrypted data using Boolean and Ranked search approaches. This paper presents all possible leakages that happen in SE and also specifies which privacy preserving approach to be adopted in SE schemes to prevent those leakages to help the practitioners and researchers to design and implement secure searchable encryption systems. It also highlights various application scenarios where SE could be utilized. This paper also explores the research challenges and open problems that need to be focused in future.     

A personalized search engine based on Web‐snippet hierarchical clustering

We propose a (meta‐)search engine, called SnakeT (SNippet Aggregation for Knowledge ExtracTion), which queries more than 18 commodity search engines and offers two complementary views on their returned results. One is the classical flat‐ranked list, the other consists of a hierarchical organization of these results into folders created on‐the‐fly at query time and labeled with intelligible sentences that capture the themes of the results contained in them. Users can browse this hierarchy with various goals: knowledge extraction, query refinement and personalization of search results. In this novel form of personalization, the user is requested to interact with the hierarchy by selecting the folders whose labels (themes) best fit her query needs. SnakeT then personalizes on‐the‐fly the original ranked list by filtering out those results that do not belong to the selected folders. Consequently, this form of personalization is carried out by the users themselves and thus results fully adaptive, privacy preserving, scalable and non‐intrusive for the underlying search engines. We have extensively tested SnakeT and compared it against the best available Web‐snippet clustering engines. SnakeT is efficient and effective, and shows that a mutual reinforcement relationship between ranking and Web‐snippet clustering does exist. In fact, the better the ranking of the underlying search engines, the more relevant the results from which SnakeT distills the hierarchy of labeled folders, and hence the more useful this hierarchy is to the user. Vice versa, the more intelligible the folder hierarchy, the more effective the personalization offered by SnakeT on the ranking of the query results. Copyright © 2007 John Wiley & Sons, Ltd.     

Arnetminer: expertise oriented search using social networks

Expertise Oriented Search (EOS) aims at providing comprehensive expertise analysis on data from distributed sources. It is useful in many application domains, for example, finding experts on a given topic, detecting the confliction of interest between researchers, and assigning reviewers to proposals. In this paper, we present the design and implementation of our expertise oriented search system, Arnetminer (). Arnetminer has gathered and integrated information about a half-million computer science researchers from the Web, including their profiles and publications. Moreover, Arnetminer constructs a social network among these researchers through their co-authorship, and utilizes this network information as well as the individual profiles to facilitate expertise oriented search tasks. In particular, the co-authorship information is used both in ranking the expertise of individual researchers for a given topic and in searching for associations between researchers. We have conducted initial experiments on Arnetminer. Our results demonstrate that the proposed relevancy propagation expert finding method outperforms the method that only uses person local information, and the proposed two-stage association search on a large-scale social network is order of magnitude faster than the baseline method.