A novel page clipping search engine based on page discussion topics

Chen, Lin‐Chih

doi:10.1007/s10115-018-1173-2

Cited by 3 publications

(7 citation statements)

References 31 publications

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“…Negm et al [56] used a PREFCA (Portal Retrieval Engine based on Formal Concept Analysis) to analyze the snippets returned by search engines to find conceptual links between different snippets. Chen [58] proposed a cost-effective GA (Genetic Algorithm) to generate page clipping results consisting of snippets corresponding to different topics.…”

Section: Document Clusteringmentioning

confidence: 99%

“…The first one is that the specific topic it generates can bring together related documents so that it can effectively reduce the time for user judgment [55], [87]. The second is that it is based on a short document (a fragment of a Web page) to cluster related documents so it can quickly produce relevant topic results [58], [88]. However, this type has the following two disadvantages.…”

Section: Recommendation Systemmentioning

confidence: 99%

“…Since its source document is a fragment of the Web page rather than the entire Web page to generate the topics, it cannot cover all the topics of the Web page [7], [89]. The second is that it produces a series of topic names rather than numerical results, so it is more difficult to evaluate different methods [58], [61]. Some researchers have used different topic searching methods to build relevant recommendation systems.…”

Section: Recommendation Systemmentioning

confidence: 99%

“…Gamare and Patil [55] proposed a system that uses HAC (Hierarchical Agglomerative Clustering) and link-based algorithms to cluster the documents based on the contents of the page and the links in the pages. Chen [58] proposed a hybrid system that uses an N-gram language model and a hashing method to generate different topics. To facilitate the user to browse the topic results, it also uses the concept of mathematical set to place different topics into a topic tree.…”

Section: Recommendation Systemmentioning

confidence: 99%

“…The snippet is the search result generated by the search engine. It mainly includes the title, URL and fragment text related to the query processed by the search engine [17], [58], [95], [96]. Snippets generated by the search engine have the following three advantages [17], [95]: (a) the essence of the Web page, (b) saving the user time to browse the whole page, and (c) the processing time of the snippet is often shorter than the whole page.…”

Section: A Serp Processmentioning

confidence: 99%

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A Study of Optimizing Search Engine Results Through User Interaction

Chen

2020

IEEE Access

Self Cite

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In this paper, we propose a method for optimizing search engine results based on user interaction. This method generates different search results mainly through the user's operation on different search topics. There are two main differences between our method and the traditional personalized search method: personal privacy and storage space. First, traditional personal search methods need to record search and click records for individual users. However, these records have great personal privacy issues for users; especially in recent years, there have been personal privacy leaks that occurred in many large online companies (such as Facebook and Yahoo). Secondly, because different users need to record their own search records, the size of the storage space is closely related to the number of users and the amount of search records stored. However, the sum of individual users' search and click records is a huge storage space in today's Internet environment. To avoid these two issues for traditional personalized search methods, this study proposes a storage-free approach based on individual users operating on different search topics. In general, in addition to avoiding personal privacy and storage space issues, our method can also achieve optimal linear time in generating personalized search results. INDEX TERMS Document clustering, machine learning, natural language processing, personalized topic search, user log.

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Section: Document Clusteringmentioning

confidence: 99%

Section: Recommendation Systemmentioning

confidence: 99%

Section: Recommendation Systemmentioning

confidence: 99%

Section: Recommendation Systemmentioning

confidence: 99%

Section: A Serp Processmentioning

confidence: 99%

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A Study of Optimizing Search Engine Results Through User Interaction

Chen

2020

IEEE Access

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A novel time-shifting method to find popular blog post topics

Chen

Lai

2019

Soft Comput

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Building a Multilevel Inflection Handling Stemmer to Improve Search Effectiveness for Urdu Language

Jabbar,

Iqbal,

Alaulamie

et al. 2024

IEEE Access

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Stemming is an essential step in various Natural Language Processing (NLP) applications and is used to reduce different variants of the query words to a standard form to avoid the vocabulary mismatch issue in Information Retrieval (IR) systems. Due to specific grammatical rules and complex morphological structures, finding an effective stemming algorithm in Urdu is challenging task. Although, several stemming algorithms have been proposed for the Urdu text stemming; however, none of them extract the stem from multilevel inflected forms. In this context, according to the best of our knowledge, this is a first effort towards the proposition and evaluation of a novel Urdu Text Stemmer (UTS) that can deal with multi-level inflection forms in Urdu text. The experimental evaluation of the proposed scheme has been conducted on the text-based and word-based custom-developed corpus. The proposed stemming technique is rigorously evaluated and compared with state-of-the-art stemming algorithms. Experimental results demonstrate that UTS outperforms existing Urdu stemmers and achieves an accuracy of 94.92% and 91.8% on word corpus and text corpus, respectively. We also evaluated our proposed system in an Information Retrieval application for Urdu, using the Collection for Urdu Retrieval Evaluation (CURE) dataset. Our approach for information retrieval outperformed and improved both recall and precision metrics.

show abstract

A novel page clipping search engine based on page discussion topics

Cited by 3 publications

References 31 publications

A Study of Optimizing Search Engine Results Through User Interaction

A Study of Optimizing Search Engine Results Through User Interaction

A novel time-shifting method to find popular blog post topics

Building a Multilevel Inflection Handling Stemmer to Improve Search Effectiveness for Urdu Language

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