A Survey on Improving the Web Search Ranking by User Behavior Information

Search engines provide ranked information based on the query given by the user. Understanding user search behavior is an important task for satisfaction of the users with the needed information. Understanding user search behaviors and recommending more information or more sites to the user is an emerging task. The work is based on the queries given by the user, the amount of time the user spending on the particular page, the number of clicks done by the user particular URL. These details will be available in the dataset of web search log. The web search log is nothing but the log which contains the user searching activities and other details like machine ID, browser ID, timestamp, query given by the user, URL accessed etc., four things considered as the important: 1) Extraction of tasks from the sequence of queries given by the user 2) suggesting some similar query to the user 3) ranking URLs based on the implicit user behaviors 4) increasing web page utilities based on the implicit behaviors. For increasing the web page utility and ranking the URLs predicting implicit user behavior is a needed task. For each of these four things designing and implementation of some algorithms and techniques are needed to increase the efficiency and effectiveness.

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Improving web search ranking by incorporating user behavior information

Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '06 ◽

10.1145/1148170.1148177 ◽

2006 ◽

Cited By ~ 380

Author(s):

Eugene Agichtein ◽

Eric Brill ◽

Susan Dumais

Keyword(s):

Web Search ◽

User Behavior ◽

Web Search Ranking

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User behavior modeling for better Web search ranking

Frontiers of Computer Science ◽

10.1007/s11704-017-6518-6 ◽

2017 ◽

Vol 11 (6) ◽

pp. 923-936 ◽

Cited By ~ 1

Author(s):

Yiqun Liu ◽

Chao Wang ◽

Min Zhang ◽

Shaoping Ma

Keyword(s):

Web Search ◽

User Behavior ◽

Behavior Modeling ◽

User Behavior Modeling ◽

Web Search Ranking

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Improving Web Search Ranking by Incorporating User Behavior Information

ACM SIGIR Forum ◽

10.1145/3308774.3308778 ◽

2019 ◽

Vol 52 (2) ◽

pp. 11-18 ◽

Cited By ~ 7

Author(s):

Eugene Agichtein ◽

Eric Brill ◽

Susan Dumais

Keyword(s):

Web Search ◽

User Behavior ◽

Web Search Ranking

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Classification of means and methods of the Web semantic retrieval

PROBLEMS IN PROGRAMMING ◽

10.15407/pp2017.01.030 ◽

2017 ◽

pp. 030-050

Author(s):

J.V. Rogushina ◽

Keyword(s):

Search Engines ◽

Domain Knowledge ◽

Information Needs ◽

Web Search ◽

User Interaction ◽

Query Languages ◽

Semantic Search ◽

Semantic Retrieval ◽

The Web

Problems associated with the improve ment of information retrieval for open environment are considered and the need for it’s semantization is grounded. Thecurrent state and prospects of development of semantic search engines that are focused on the Web information resources processing are analysed, the criteria for the classification of such systems are reviewed. In this analysis the significant attention is paid to the semantic search use of ontologies that contain knowledge about the subject area and the search users. The sources of ontological knowledge and methods of their processing for the improvement of the search procedures are considered. Examples of semantic search systems that use structured query languages (eg, SPARQL), lists of keywords and queries in natural language are proposed. Such criteria for the classification of semantic search engines like architecture, coupling, transparency, user context, modification requests, ontology structure, etc. are considered. Different ways of support of semantic and otology based modification of user queries that improve the completeness and accuracy of the search are analyzed. On base of analysis of the properties of existing semantic search engines in terms of these criteria, the areas for further improvement of these systems are selected: the development of metasearch systems, semantic modification of user requests, the determination of an user-acceptable transparency level of the search procedures, flexibility of domain knowledge management tools, increasing productivity and scalability. In addition, the development of means of semantic Web search needs in use of some external knowledge base which contains knowledge about the domain of user information needs, and in providing the users with the ability to independent selection of knowledge that is used in the search process. There is necessary to take into account the history of user interaction with the retrieval system and the search context for personalization of the query results and their ordering in accordance with the user information needs. All these aspects were taken into account in the design and implementation of semantic search engine "MAIPS" that is based on an ontological model of users and resources cooperation into the Web.

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Big Data Analytics for Search Engine Optimization

Big Data and Cognitive Computing ◽

10.3390/bdcc4020005 ◽

2020 ◽

Vol 4 (2) ◽

pp. 5 ◽

Cited By ~ 1

Author(s):

Ioannis C. Drivas ◽

Damianos P. Sakas ◽

Georgios A. Giannakopoulos ◽

Daphne Kyriaki-Manessi

Keyword(s):

Big Data ◽

Cultural Heritage ◽

Search Engine ◽

Data Analytics ◽

User Behavior ◽

Big Data Analytics ◽

Data Types ◽

Search Engine Optimization ◽

The Impact ◽

The Web

In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.

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