With the rapid development of network techniques, huge information resources glut the whole web world. Locating useful information effectively from the World Wide Web (WWW) is of wide interest. The huge volume of the return results makes the user only focus on the top results. So the ranking problem becomes the important task for the search systems. Now, so many ranking algorithms were proposed. But the existing search systems just use the relationships or links between user query and web pages in the traditional Web1.0 environment. Now, the success and popularity of social network systems, such as del.icio.us, Facebook, etc., have generated many interesting problems to the research community. This gives us a new viewpoint on how to improve the quality of information retrieval. This paper firstly summarizes the present ranking algorithms, and analyses their merits and demerits. Secondly, we present a new search ranking algorithm bases on web pages and tags clustering, and use several evaluating methods to assess and contrast with Google.

A search result ranking algorithm based on web pages and tags clustering

Query expansion technology is usually used to make abundant query to search information. Traditional query expansion based on WordNet, user search log or user feedback couldn’t get the user’s real intent, sometimes the latter could expand overly. In this paper, we propose a novel query expansion method based both on the user’s long-term relation and short-term relation in XML social book search. We first propose recommendation degree of a tagged word based on a user’s long-term social relation and short-term relation respectively, and the combined recommendation degree of a tagged word, then based on these definitions, we propose a new query expansion algorithm. The experimental results on real-world data show that our method has greatly increased the retrieval precise and users’ satisfaction . Keywordsquery expansion; long-term social relation; short-term social relation; book search; recommendation degree

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Query Expansion Based On Both Long-Term And Short-Term Social Relation In XML Social Book Search

WWW is a huge collection of information and most of the time people are engaged with it in order to retrieve different types of information. People want to get accurate and appropriate data at the top of search results in a user friendly manner. People also want to get a personal space over the internet when they are browsing on web, from this arises a need of personalization of the search history. Thus there is a need of a highly efficient and effective ranking algorithm that provides search results according to user preferences. In this paper we have proposed a new approach which we call CUSTOM PERSONALISED SEARCHING. In this approach first we will use Page Rank algorithm, then social signals to rank the pages then we provide personalization by creating individual search history for each user on the browser and here we have also focused on the search results to get customized according to the user demand. We have also focused on parameters like page authority and domain authority.

A new approach to ranking algorithm - Custom Personalized Searching

This research examines the problem of Information Overload (IO) and give an overview of various attempts to resolve it. Furthermore, argue that instead of fighting IO, it is advisable to start learning how to live with it. It is unlikely that in modern information age, where users are producer and consumer of information, the amount of data and information generated would decrease. Furthermore, when managing IO, users are confined to the algorithms and policies of commercial Search Engines and Recommender Systems (RSs), which create results that also add to IO. this research calls to initiate a change in thinking: this by giving greater power to users when addressing the relevance and accuracy of internet searches, which helps in IO. However powerful search engines are, they do not process enough semantics in the moment when search queries are formulated. This research proposes a semantic selection of internet sources, through SWRL enabled OWL ontologies. the research focuses on SWT and its Stack because they (a)secure the semantic interpretation of the environments where internet searches take place and (b) guarantee reasoning that results in the selection of suitable internet sources in a particular moment of internet searches. Therefore, it is important to model the behaviour of users through OWL concepts and reason upon them in order to address IO when searching the internet. Thus, user behaviour is itemized through user preferences, perceptions and expectations from internet
searches. The proposed approach in this research is a Software Engineering (SE) solution which provides computations based on the semantics of the environment stored in the ontological model.

Semantic Selection of Internet Sources through SWRL Enabled OWL Ontologies

The Web provides an enormous amount of learning tutorials. Searching standard content is not easy for common user of traditional search engine. The user only focuses on the top results from the enormous quantity of the arrived results. So the Re-ranking problem turns into the significant responsibility for the search systems. This paper proposes a novel model of Re-ranking Agent on Hybrid search engine (Meta-search engine and Topical search engine) for helping learners searching online Learning tutorials efficiently and in the effective way. With the aim of providing users with relevant tutorials we have proposed model classified by subject topic, prepared by professional persons and preferred by learners.

Model for Re-ranking Agent on Hybrid Search Engine for E-learning

In order to let people be able to get information from the Web easily, search engine comes into being and continues to grow and develop. People begin to explore all kinds of ranking algorithms and try to give user a good result list. However, the expression format of the web information and user queries are very simple, which results in the difficulty of determining the relevance between user queries and web information. The success and popularity of social network systems, such as del.icio.us, Face book, etc., have generated many interesting problems to the research community. This gives us a new viewpoint on how to improve the quality of information retrieval. The contributions of our research are twofold. First, the existing ranking algorithms of search engine are classified. And we extend expression of queries by “keyword + tags&#8221, instead of keywords only. Second, a new ranking algorithm based on user feedback and semantic tags is proposed, and it is also compared with Google by several evaluation methods.

Chongchong Zhao

Papers

A search result ranking algorithm based on web pages and tags clustering

A New Keywords Method to Improve Web Search