Web Page Recommender System using hybrid of Genetic Algorithm and Trust for Personalized Web Search

Author:

Chawla Suruchi1

Affiliation:

1. Shaheed Rajguru College Delhi University, Delhi, India

Abstract

The main challenge to effective information retrieval is to optimize the page ranking in order to retrieve relevant documents for user queries. In this article, a method is proposed which uses hybrid of genetic algorithms (GA) and trust for generating the optimal ranking of trusted clicked URLs for web page recommendations. The trusted web pages are selected based on clustered query sessions for GA based optimal ranking in order to retrieve more relevant documents up in ranking and improves the precision of search results. Thus, the optimal ranking of trusted clicked URLs recommends relevant documents to web users for their search goal and satisfy the information need of the user effectively. The experiment was conducted on a data set captured in three domains, academics, entertainment and sports, to evaluate the performance of GA based optimal ranking (with/without trust) and search results confirms the improvement of precision of search results.

Publisher

IGI Global

Subject

General Computer Science

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Deep Fuzzy Clustering and Deep Residual Network for Prediction of Web Pages from Weblog Data with Fractional Order Based Ranking;International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems;2023-06

2. Image Retrieval Technology and System Implementation of Handicraft Based on Genetic Algorithm;2023 International Conference on Mechatronics, IoT and Industrial Informatics (ICMIII);2023-06

3. Content Based Recommender System: Methodologies, Performance, Evaluation and Application;2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N);2022-12-16

4. Personalized Search Using User Preferences on Social Media;Electronics;2022-09-24

5. An Improved Recommender System Solution to Mitigate the Over-Specialization Problem Using Genetic Algorithms;Electronics;2022-01-13

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