Explore the vast range of titles available.

This book is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior.  It comprehensively covers the topic of recommender.

Acclaimed by various content platforms (books, music, movies) and auction sites online, recommendation systems are key elements of digital strategies. If development was originally intended for the performance of information systems, the issues are now massively moved on logical optimization of the customer relationship, with the main objective to maximize potential sales. On the transdisciplinary approach, engines and recommender systems brings together contributions linking information science and communications, marketing, sociology, mathematics and computing. It deals with the understanding of the underlying models for recommender systems and describes their historical perspective. It also analyzes their development in the content offerings and assesses their impact on user behavior.

This study investigates how users’ ongoing intentions to interact with over-the-top (OTT) streaming media services are improved by gamified recommendation engines. Users of streaming media services who had dealt with gamified recommendation algorithms were given a quantitative survey to complete. A strong direct effect (R2 = 0.864) of Gamification-Recommendation Engine Usage (GREU) on user engagement makes it an effective predictor. Intrinsic Motivation (IM) has a moderately positive effect on Continuance Intention towards Streaming Media (CISM), while Extrinsic Motivation (EM) has a negative effect on (CISM), which shows that excessive external benefits might not be helpful. GREU has significant impacts on both IM and EM, which makes its effect on driving factors stronger. The study lacks a longitudinal technique; therefore, it cannot determine variable relationships or user behaviour progression. The study only examines streaming media platforms and no other digital service sectors that use gamification and recommendation systems. AI-powered gamified recommendation systems and experience-oriented incentives (e.g., exclusive material, milestone achievements) can boost user retention through personalised playlists, narrative features, and interactive tools. This study highlights the special relationship between gamification, motivation, and consistent use of streaming services, adding to the expanding body of research on gamification and digital media engagement.

In this era of big data, the amount of video content has dramatically increased with an exponential broadening of video streaming services. Hence, it has become very strenuous for end-users to search for their desired videos. Therefore, to attain an accurate and robust clustering of information, a hybrid algorithm was used to introduce a recommender engine with collaborative filtering using Apache Spark and machine learning (ML) libraries. In this study, we implemented a movie recommendation system based on a collaborative filtering approach using the alternating least squared (ALS) model to predict the best-rated movies. Our proposed system uses the last search data of a user regarding movie category and references this to instruct the recommender engine, thereby making a list of predictions for top ratings. The proposed study used a model-based approach of matrix factorization, the ALS algorithm along with a collaborative filtering technique, which solved the cold start, sparse, and scalability problems. In particular, we performed experimental analysis and successfully obtained minimum root mean squared errors (oRMSEs) of 0.8959 to 0.97613, approximately. Moreover, our proposed movie recommendation system showed an accuracy of 97% and predicted the top 1000 ratings for movies.

The outbreak of COVID-19 has entirely changed how consumers behave, due to an over-reliance on online shopping. With the global pandemic demanding people to stay home, multiple companies had to find innovative strategies to remain competitive and adapt to these rapid changes. However, the pandemic has also propelled the development of technologies, such as artificial intelligence (AI). AI concerns the engineering of machines and programs to make them intelligent, make decisions on their own or provide humans with information that will aid them in the decision-making process. Artificial intelligence software can be programmed according to an organization’s needs and performance goals. Although AI offers e-businesses multiple advantages, in order to differentiate themselves from their competitors, it is still a relatively new technology. A lack of understanding of its implementation will hinder organizations from reaping the full benefits of this technology. Moreover, multiple disputes regarding AI’s ethicality and privacy concerns have led to further research focused on making these systems more reliable and ethical.

This paper applies a nonlinear differential equation to the information management system of college course selection. A teaching information management system based on an approximate learning strategy is presented by using statistical linearization technology. An imprecise controller is obtained by numerical simulation of Riccati differential equations with statistical linearization. This kind of Riccati differential equation differs significantly from the ordinary one. Then the system proposes a collaborative filtering method based on nonlinear differentiation based on student feature classification. At last, this paper systematically analyzes the differences between course selection systems, business recommendations, and student attributes—the system experiments on college students' choice of a learning platform. The study found that the method was correct 34.6% of the time. This system can provide practical guidance for students to choose courses.

In today's fast-paced world, recommendation systems have become indispensable tools, aiding users in making personalized decisions amidst an overwhelming array of choices. These systems leverage user data and preferences to generate tailor-made recommendations based on individual tastes and behaviors. This research paper introduces the development and implementation of Pranik Movies, an ultimate recommendation system for personalized movie suggestions. The system incorporates collaborative and content-based filtering techniques, utilizing machine learning algorithms to analyze user behaviors, ratings, and viewing histories. A comprehensive overview of the research framework is provided, encompassing system architecture, data pre-processing, feature engineering techniques, and model selection and design. Text processing methods such as stemming, bag-of-words (BoW), and TF-IDF (Term Frequency-Inverse Document Frequency) are employed for processing and analyzing textual movie data. The accuracy of recommendations is enhanced through the assessment of film similarities, utilizing algorithms like cosine similarity and Euclidean distance. The paper concludes by outlining future directions for advanced machine learning techniques, social media integration, expanded content support, and the refinement of the evaluation framework. Pranik Movies signifies a significant advancement in recommendation systems, enabling personalized and precise movie recommendations within a vast and diverse cinematic landscape.

This is the age of intelligent retailing where airlines want to optimize the sale of the base fare and air ancillary bundle to maximize revenues. This paper discusses customer segmentation based on the context for travel, air ancillary bundle recommendation for the personas and personalization of the offer for a segment of one.

Artificial intelligence (AI)-powered tools such as recommendation engines are widely used in online marketing and e-commerce; however, online retailers often deploy these tools without understanding which human factors play a role in which products and at which stage of the customer journey. Understanding the interaction between AI-powered tools and humans can help practitioners create more effective online marketing platforms and improve human interaction with e-commerce tools. This paper examines customers' reliance on recommendation engines when purchasing fashion goods, electronics, and media content such as video and music. This paper also discusses the potential for improvement in recommendation engines in online marketing and e-commerce.

With the dramatic rise of internet users in the last decade, there has been a massive rise in the number of daily web searches. This leads to a plethora of data available online, which is growing by the days. A recommendation engine leverages this massive amount of data by finding patterns of user behavior. Movie recommendation for users is one of the most prevalent implementations. Although it goes way back in the history of recommendation engines, collaborative filtering is still the most predominant method when it comes to the underlying technique implemented in recommendation engines. The main reasons behind that are its simplicity and flexibility. However, collaborative filtering has always suffered from the Cold-Start problem. When a new movie enters the rating platform, we do not have any user interaction for the movie. The foundation of collaborative filtering is based on the user-movie rating. In this paper, we have proposed a hybrid filtering to combat this problem using the genre labeled for a new movie. The proposed algorithm utilizes the nonlinear similarities among various movie genres and predicts the rating of a user for the new movie with the associated genres for the movie.