Reducing the dependency of having prior domain knowledge for effective online information retrieval

Abstract

Sometimes Internet users struggle to find what they are looking for on the Internet due to information overload. Search engines intend to identify documents related to a given keyphrase on the Internet and provide suggestions. Having some background knowledge about a topic or a domain will help in building effective search keyphrases that will lead to accurate results in information retrieval. This is further pronounced among students that rely on the internet to learn about a new topic. Students might not have the required background knowledge to build effective keyphrases and find what they are looking for. In this research, we are addressing this problem, and aim to help students find relevant information online. This research furthers existing literature by enhancing information retrieval frameworks through keyphrase assignment, aiming to expose students to new terminologies, therefore reducing the dependency of having background knowledge about the domain under study. We evaluated this framework and identified how it can be enhanced to suggest more effective search keyphrases. Our proposed suggestion is to introduce a keyphrase Ranking Mechanism that will improve the keyphrase assignment part of the framework by taking into consideration the part‐of‐speech of the generated keyphrases. To evaluate the proposed approach, various data sets were downloaded and processed. The results obtained showed that our proposed approach produces more effective keyphrases than the existing framework.

A Novel Multimodal Online News Popularity Prediction Model based on Ensemble Learning

“…Generally, the foregoing research studies focus on regression models and machine learning models usage in various domains such as healthcare, privacy and safety [45], e-commerce [46], medicines to detect, identify, and predict influencing factors, adverse effects, adverse factors, patterns, etc [47]. Iglesias et.…”

A novel multimodal online news popularity prediction model based on ensemble learning

“…Similarly, ResNet (Wu et al, 2019) is used to fetch Image features that are high in amount so PCA (Kim, 1996) is used for dimensionality reduction of ResNet provided approximately 10,000 features. Generally, the foregoing research studies focus on regression models and machine learning models usage in various domains such as healthcare, privacy and safety (Estrada-Jimenez et al, 2019), e-commerce (Zammit et al, 2022), medicines to detect, identify and predict influencing factors, adverse effects, adverse factors, patterns, and so on (Shi et al, 2019). Iglesias et al used evolving fuzzy systems to classify real-time news articles into various topics (Gerasimovic & Bugaric, 2018).…”

“…Similarly, ResNet [56] is used to fetch Image features that are high in amount so PCA [57] is used for dimensionality reduction of ResNet provided approximately 10,000 features. Generally, the foregoing research studies focus on regression models and machine learning models usage in various domains such as healthcare, privacy and safety [58], e-commerce [59], medicines to detect, identify, and predict influencing factors, adverse effects, adverse factors, patterns, etc [60]. Iglesias et.…”