Community-supported collaborative navigation with FoxPeer (original) (raw)
Related papers
FoxPeer: Navigating the Web with Community Recommendations
2007
The web holds a large number of resources, and navigating can sometimes become hard. As individuals get together to discuss and exchange information, web based communities start to form. These communities form a nexus around which individuals organize and learn. As they grow and accumulate shared resources, they become rich information sources. The usual way to navigate the web is to search, starting from a search page and then browsing to look for adequate resources. When looking for information, individuals often turn to others for recommendations or answers. This paper presents a peer-to-peer tool to assist web navigation and search by leveraging a community's existent knowledge. Individuals rate web sites and share these ratings with the community. Recommendations are made based on these ratings. This enables a community-geared rating, where individuals know they will find ratings that reflect the opinions of the community in which he or she is inserted. This mimics the recommendation-seeking behavior and has the potential to lead to better results, as searches become more directed to a community's context.
2003
Abstract Web search engines struggle to satisfy the needs of Web users. Users are notoriously poor at representing their needs in the form of a query, and search engines are poor at responding to vague queries. However progress has been made by introducing context into the search process. In this paper we describe and evaluate a novel approach to using context in Web search that adapts a generic search engine for the needs of a specialist community of users.
(Web Search) shared: social aspects of a collaborative, community-based search network
2008
Collaborative Web search (CWS) is a community-based approach to Web search that supports the sharing of past result selections among a group of related searchers so as to personalize result-lists to reflect the preferences of the community as a whole. In this paper, we present the results of a recent live-user trial which demonstrates how CWS elicits high levels of participation and how the search activities of a community of related users form a type of social search network.
Cooperating search communities
2006
Abstract. Collaborative Web Search (CWS) seeks to exploit the high degree of natural query repetition and result selection regularity that is prevalent among communities of searchers. CWS reuses the search experiences of community members, to promote results that have previously been judged relevant for queries. This facilitates a better response to the type of vague queries that are commonplace in Web search and allows a generic search engine to adapt to the preferences of communities of individuals.
Further experiments on collaborative ranking in community-based web search
2004
As the search engine arms-race continues, search engines are constantly looking for ways to improve the manner in which they respond to user queries. Given the vagueness of Web search queries, recent research has focused on ways to introduce context into the search process as a means of clarifying vague, under-specified or ambiguous query terms.
Social and collaborative web search
Proceedings of the 16th international conference on Intelligent user interfaces, 2011
In this paper we describe the results of a live-user study to demonstrate the benefits of using the social search utility HeyStaks, a novel approach to Web search that combines ideas from personalization and social networking to provide a more collaborative search experience.
Evaluation of Peer Based Web Search
sixearch.org
Peer network systems are becoming an increasingly important development in Web search technology. To provide insight into the comparison of different peer network systems, proper evaluation criteria are needed. This paper proposes a novel evaluation framework for peer search systems based on the concept of semantic locality. In order for the network to be functional, its dynamic communication topology must match the semantic clustering of peers. We introduce two criteria to evaluate the semantic locality of a peer network. First, the "small-world" topology of the network. Second, we use topical semantic similarity to monitor the quality of a peer's neighbors over time by looking at whether a peer chooses semantically appropriate neighbors to route its queries. Furthermore, we evaluate the quality of search results based on the notions of global coherence and coverage. These two performance functions generalize the well known IR measures of precision and recall by relaxing the condition that all relevant resources are identified in advance. We demonstrate the feasibility of the proposed evaluation framework by presenting several simulation experiments conducted with different types of peer network systems. The results of these simulations suggest that our methodology can be effectively applied to the evaluation of peer based Web search.
Tag-based navigation for peer-to-peer wikipedia
2006
We introduce P2P Wikipedia, a prototype of a personalized tag-based navigation system for Wikipedia multimedia content. It is the first peer-to-peer (P2P) file sharing system able to deal with large files like movies, music, and software, but that is also scalable to HTML content. The combined techniques in our prototype are the automated calculation of tags from HTML content, a personalized P2P file sharing system built on a social network, the use of incentives for user cooperation to optimize system performance, and the design of a user interface with advanced navigational features.
Searchius: A Collaborative Search Engine
Eighth Mexican International Conference on Current Trends in Computer Science (ENC 2007), 2007
Searchius is a collaborative search engine that produces search results based solely on user provided web-related data. We discuss the architecture of this system and how it compares to current state-of-the-art search engines. We show that the global users' preference over pages can be efficiently used as a metric of page quality, and that the inherent organization of the collected data can be used to discover related URLs. We also conduct an extensive experimental study, based on the web related data of 36483 users, to analyze the qualitative and quantitative characteristics of user collected URL collections, to investigate how well the users URL collections cover the web and discover the characteristics that affect the quality of the search results under the proposed setting.
Personalized Web Search Using Clickthrough Data and Web Page Rating
Journal of Computers, 2012
Personalization of Web search is to carry out retrieval for each user incorporating his/her interests. We propose a novel technique to construct personalized information retrieval model from the users' clickthrough data and Web page ratings. This model builds on the userbased collaborative filtering technology and the top-N resource recommending algorithm, which consists of three parts: user profile, user-based collaborative filtering, and the personalized search model. Firstly, we conduct user's preference score to construct the user profile from clicked sequence score and Web page rating. Then it attains similar users with a given user by user-based collaborative filtering algorithm and calculates the recommendable Web page scoring value. Finally, personalized informaion retrieval be modeled by three case applies (rating information for the user himself; at least rating information by similar users; not make use of any rating information). Experimental results indicate that our technique significantly improves the search performance.