Concurrency and Computation Practice and Experience · 2019 · 13 citations · 20 references
ComputingComputer ScienceMarketingDigital MarketingInformation RetrievalRecommender SystemsEvaluation Of Retrieval SystemsRelevance RankingRetrieval ModelsSearch Engine OptimizationSearch SystemsWeb SearchHeuristic ApproachMetadataMetadata-based RecommendationRanking AlgorithmsRelevance EstimationSearch Engine IndexingSearch Engine Ranking AlgorithmsSearch Engine Result Pages
Online marketing relies on top search engine rankings, yet traditional on‑page SEO lacks a logical basis for selecting metadata, which is crucial for website prioritization during indexing. The study proposes a two‑step method to recommend metadata that combines keywords and metadata from high‑ranked sites and evaluates term importance via semantic relevance to improve SERP rankings. The method first selects terms using keyword and metadata frequency and weight, then prioritizes them by semantic relevance via a competitive learning model, and its validity was tested on three Google queries. Experiments show that using the recommended terms from high‑ranked sites and semantic relevance increases website traffic.
Summary This study aims to recommend metadata for building a high ranking in Search Engine Result Page (SERP) by considering Search Engine Optimizations (SEO). For online marketing, it is important to place their websites on the top rank in a result of search engines. However, on‐page techniques of traditional SEO do not have logical foundation to select metadata. Metadata is an important element to prioritize of websites when search engine indexing for user queries. Thereby, for online marketing, this study proposes a method for recommending metadata, which consists of two steps: i) combining keywords and metadata from high‐ranked websites, and ii) evaluating the importance of terms based on semantic relevance. First, terms are selected with influential keywords and metadata by using their frequency and weight. Second, prioritize the terms according to semantic relevance based on a competitive learning model. We evaluated the validity of the proposed method by using three queries in Google. Experimental results demonstrate that it increases traffic of a website, by using terms, which are high‐ranked websites and semantic relevance.
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