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.

Serendipity is the phenomenon of people making unexpected and beneficial discoveries. While research on the mechanism and effectiveness of serendipity in information acquisition has been actively conducted, little attempt has been made to quantify serendipity when it occurs. In this paper, we present a quantitative model that measures serendipity experienced by users in a hypertext environment. In order to propose an evaluation model that measures how probable users would experience serendipitous moments in the process of an active search, we define a serendipitous discovery as an unexpected discovery that can happen during a sidetracked search. The proposed model consists of three parts: (a) pre-encountering—how early the user falls into the sidetracked search in the process of an active search; (b) post-encountering—the degree of interests of the entire process from the active search to obtaining unxpected information; and (c) discovery—the degree of the unexpectedness of the information obtained from the discovery. We evaluated the proposed model against examples with different structures and the potential serendipity values computed indicated the difference between the spaces in a meaningful way.

On the Semantic Web, resources are connected to each other by the IRI. As the basic unit is comprised of linked data, machines can use semantic data and reason their relations without additional intervention on the Semantic Web. However, it is necessary for users who first encounter the Semantic Web to understand its underlying structure and some grammatical rules. This study suggests linking data sets of the Semantic Web through the Euler diagram, which does not require any prior knowledge. We performed a user study with our relationship-building system and verified that users could better understand linked data through the usage of the system. Users can indirectly be guided by using our Euler diagram-based data relationship-building system to understand the Semantic Web and its data linkage system. We also expect that the data sets defined through our system can be used in various applications.

In this poster, we report our on-going project that aims to design a good set of course materials for introduction to programming using a functional programming language, Clean[2]. While procedural languages such as C, Python, etc are used in most introduction to programming classes, we believe that Clean might be better for students, because the syntax is very simple and in many cases, the size of a program in Clean is shorter than a corresponding program in C or other procedural languages. In addition, if Clean is used in an introduction to programming course, a lot of recursive examples can be introduced intuitively. This is important because even though recursion is one of important concepts that students should understand, many students have difficulty understanding the concept[3].