Performance Evaluation of Search Engines Using Enhanced Vector Space Model

Abstract

Vector space model allows computing a continuous degree of similarity between queries and retrieved documents and then ranks the documents in increasing order of cosine (similarity) value. It computes cosine or similarity value using their cosine function. The cosine function computes the similarity value by computing the weight of each term in the documents using a weighting scheme but it is a complex process to compute the weight of each term in the documents. It is also found that sometimes it fails to compute a similarity score, Firstly if there is only one document in the corpus and query terms match with the document and secondly, if the number of documents containing query terms and total number of documents retrieved are equal. To address this problem in order to improve the performance, we proposed an enhanced approach for computation of cosine or similarity value by enhancing the vector space model. Our work intends to analyze and implement our proposed method in performance evaluation of three search engines Google, Yahoo and MSN. To verify our method, we compared our proposed method with a manually computed relevance score and found that our evaluations match with manual method.

Proposing a New Combined Indicator for Measuring Search Engine Performance and Evaluating Google, Yahoo, DuckDuckGo, and Bing Search Engines based on Combined Indicator

“…Lewandowski (2012) offered a framework to measure search engines retrieval efficiency. Singh and Dwivedi (2015) introduced an advanced vector space to evaluate search engines performance and then applied this model on Google, Yahoo, and MSN. Arkhipova et al (2015) developed “pSwitch” metric to assess search engines efficency.…”

Evaluating Retrieval Effectiveness by Sustainable Rank List

“…The article [30] is a study regarding adaptive smart homes using user-created feedback and [31] discusses the evaluation of self-monitoring devices for clinical purposes. Singh and Dwivedi in [32] study the reason (e.g., only one document in the corpus or if a number of documents containing query terms and total documents are same) behind the failing of cosine similarity. After studying the impacts of these factors, they proposed enhanced from of vector space model to improve efficiency.…”