In this age of information overload, people use a variety of strategies to make choices about what to buy, how to spend their leisure time, and even whom to date. Recommender systems automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. This book offers an overview of approaches to developing state-of-the-art recommender systems. The authors present current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. They also discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical case studies. The final chapters cover emerging topics such as recommender systems in the social web and consumer buying behavior theory. Suitable for computer science researchers and students interested in getting an overview of the field, this book will also be useful for professionals looking for the right technology to build real-world recommender systems.

This viewpoint article argues that the impacts of the novel coronavirus COVID-19 call for transformative e-Tourism research. We are at a crossroads where one road takes us to e-Tourism as it was before the crisis, whereas the other holds the potential to transform e-Tourism. To realize this potential, e-Tourism research needs to challenge existing paradigms and critically evaluate its ontological and epistemological foundations. In light of the paramount importance to rethink contemporary science, growth, and technology paradigms, we present six pillars to guide scholars in their efforts to transform e-Tourism through their research, including historicity, reflexivity, equity, transparency, plurality, and creativity. We conclude the paper with a call to the e-Tourism research community to embrace transformative research.

The World Wide Web is moving from a Web of hyper-linked Documents to a Web of linked Data. Thanks to the Semantic Web spread and to the more recent Linked Open Data (LOD) initiative, a vast amount of RDF data have been published in freely accessible datasets. These datasets are connected with each other to form the so called Linked Open Data cloud. As of today, there are tons of RDF data available in the Web of Data, but only few applications really exploit their potential power. In this paper we show how these data can successfully be used to develop a recommender system (RS) that relies exclusively on the information encoded in the Web of Data. We implemented a content-based RS that leverages the data available within Linked Open Data datasets (in particular DBpedia, Freebase and LinkedMDB) in order to recommend movies to the end users. We extensively evaluated the approach and validated the effectiveness of the algorithms by experimentally measuring their accuracy with precision and recall metrics.

The complexity of the product assortments offered by on-line selling platforms makes selection a challenging task. Customers differ in respect to expertise and product knowledge, but intelligent recommender systems offer personalized dialogues that support the product-selection process. This paper describes CWAdvisor, a domain-independent, knowledge-based recommender environment that provides users with consistently appropriate solutions, identifies additional selling opportunities, and explains solutions. The discussion uses examples from several application domains to show how model-based diagnosis, personalization, and intuitive knowledge-acquisition techniques support customer-oriented sales dialogues. Experience obtained in industrial projects is reported, and successfully deployed recommender applications are evaluated.

In recommender systems, explanations serve as an additional type of information that can help users to better understand the system's output and promote objectives such as trust, confidence in decision making, or utility. This article proposes a taxonomy to categorize and review the research in the area of explanations. It provides a unified view on the different recommendation paradigms, allowing similarities and differences to be clearly identified. Finally, the authors present their view on open research issues and opportunities for future work on this topic.