Preference learning plays a crucial role in AI applications, particularly in recommender systems and personalized services. Traditional pairwise comparisons, while foundational, present scalability challenges in large-scale systems. This study explores alternative elicitation methods such as ranking, numerical ratings, and natural language feedback, alongside a novel hybrid framework that dynamically integrates these approaches. The proposed methods demonstrate improved efficiency, reduced cognitive load, and enhanced accuracy. Results from simulated user studies reveal that hybrid approaches outperform traditional methods, achieving a 40% reduction in user effort while maintaining high predictive accuracy. These findings open pathways for deploying user-centric, scalable preference learning systems in dynamic environments.

Smallholder farmers face challenges when they lack information on their agricultural activities. To address this, we suggest a web-based system that can be used by farmers to help them in decision making considering the fact that all necessary information is provided by the system. Farmers can input crop type they want to grow and area. This data will help to recommend them the best crops that are suitable to be grown in that area and the necessary growing practices that can be done to produce high yield and have maximum profits, considering the average rainfall of that year. A persistent issue we face in Zimbabwe is the lack of access to reliable agricultural data. In the agricultural sector, one major uncertainty for farmers is the outlook of their future harvest. Once their produce is ready for sale, the presence of other potential buyers compels traders to offer prices that align closely with those in the formal market. However, without timely information, traders can take advantage of the situation by purchasing crops at unfairly low rates. Having data that tracks prices across various markets in near real-time would enable farmers to have a precise and complete understanding of their selling choices to maximize their profits.

Most people can't subscribe to every direct-to-consumer platform today, and the number is growing. The platform's content and the user's experience influence the decision to subscribe or buy. Today's consumers anticipate instantaneously curated content exploration, acquisition, and consumption. Media firms actively seek to increase both click-through rate and profitability by enhancing the user experience and enticing customers to subscribe or buy premium content through recommender systems. The direct-to-consumer platforms may maintain user engagement after consumers have visited the contents by providing suggestions that make the most of the site's rich content catalogs. By bringing it to the attention of viewers based on their viewing habits, for instance, effective recommendation systems might boost earnings for underappreciated "long tail" content. This research explores various recommender system types currently in widespread usage with an analysis of some of the fascinating breakthroughs.

There are different traditional and nontraditional datasets available to investigate the performance of recommender systems. This article focuses on the different datasets required for the investigation of recommender systems.