This paper presents a stock trading recommendation system that integrates news sentiment analysis with the relative strength index (RSI) to provide informed buy–sell decisions. The system uses a rule-based natural language processing (NLP) approach to analyze recent news articles and combines the resulting sentiment scores with the RSI, which tracks stock momentum. By evaluating seven days of news data, the system assigns a sentiment score (1 to 100) that reflects market sentiment, while the RSI identifies overbought or oversold conditions. This combined approach allows traders to make data-driven buy, sell, or hold decisions in real time. In this study, we conducted a comparative study with benchmark indices across various subsets of stocks to evaluate their relative performance, highlighting our system’s competitive edge in terms of accuracy, profitability, and lightweight design with low computational cost. The results showed the system’s adaptability across different market segments and its potential to enhance trading outcomes. By integrating real-time sentiment analysis with technical indicators, the system offers a practical and actionable investment strategy.

Unexpected yet advantageous findings, often referred to as serendipitous discoveries, are becoming increasingly significant in the field of computer science. This research aims to examine the impact of factors that could potentially trigger such serendipity within a recommender system (RS) and consequently proposes a novel, serendipity-incorporated recommender system (SRS). The SRS is developed by integrating elements that could stimulate the occurrence of serendipity into an RS algorithm. These elements include interestingness, diversity, and unexpectedness. As a result, the SRS is equipped to provide users with recommendations that are surprising, intriguing, and atypical. The algorithm within the SRS recommends three items predicated on a user’s preferred item. To facilitate the selection of items to be recommended, we have designed a computation method called the ’serendipity measure’, which is tasked with calculating the weights of all items. Our innovative algorithm and its efficient execution are expounded upon extensively in this study. The performance of the SRS was assessed using a quantitative serendipity evaluation model (QSEM). This model is a quantitative tool designed to measure the probability of users encountering serendipitous events within a specific information space. We conducted a user study to compare the SRS with the traditional cold-start recommender system (CRS), and the feedback for the SRS was positively received. The experiments confirm the viability of cultivating a serendipitous environment from a system’s perspective. The test results also underline the exciting potential that serendipity brings to recommender systems.

The Semantic Web is an extension of the current Web where information is represented in a machine processable way. It is not separate from the current Web and one of the confusions that novice users might have is where the Semantic Web is. In fact, users can easily encounter RDF documents that are components of the Semantic Web while they navigate among Web pages. In addition, the number of RDF documents on the Web is ever-increasing and a lot of educational materials in RDF format become available on the Web. In this paper, we propose a simple Semantic Web browser by which non-expert users such as secondary school students who do not know technical details about the Semantic Web can browse through HTML documents and RDF documents.