Personalized recommendations have played an increasingly important role in the tourism and hospitality industry in recent years. Through the use of recommendation systems, hotels can provide their guests with tailored offers and information based on their individual preferences and interactions. This personalised communication not only contributes to increased customer satisfaction, but also enables more efficient use of resources and optimization of operational processes.As part of a collaboration between academia and industry, a master's thesis was developed in partnership with ADDITIVE at the University of Innsbruck. The team consists of Ass.-Prof. Dr. Eva Zangerle, an expert in recommendation systems and user modelling, Philip Handl, a master student with practical experience as a software developer, and Simon Knoll, a senior software developer at ADDITIVE. The aim of this collaboration is to develop innovative approaches to recommendation systems based on user behaviour, enabling personalized recommendations for the tourism sector.

<div class="article_quote"><div class="article_quote_contain"><div class="article_quote_quote">""In my daily life, I constantly encounter personalized recommendations on different websites and platforms. I am fascinated by the continuous improvement of these suggestions. Therefore, the topic of this thesis was a good opportunity for me to delve deeper into this research area to gain a better understanding."</div><div class="article_quote_name u-text-style-main">Philip Handl</div></div></div>

Scientific background of recommendation systems

Recommendation systems use complex algorithms to suggest products, services, or content to users that are particularly relevant to them. Traditional recommendation systems often rely on collaborative filtering, which involves analysing the behaviour of user groups to identify similarities between users and generating recommendations based on these similarities.

Ass.-Prof. Dr. Eva Zangerle explains: "Current research has increasingly focused on 'deep learning' approaches, using artificial neural networks to better understand user behaviour and make more accurate predictions. Such recommendation systems capture user behaviour across different sessions on different websites and identify patterns to create personalized recommendations".

Relevance of personalized recommendations to hospitality industry

In the hospitality and tourism industry today, guests are faced with a multitude of options and offers on digital platforms. Personalized recommendations provide a way to offer these guests exactly what they're looking for, providing them with an optimal and personalized user experience. The added value for the user or guest is always at the forefront.

Simon Knoll, shows some applications of personalized recommendations in the tourism industry and their benefits:

• Personalized communication:
  Advances in personalized recommendations allow hotels to tailor their communications to individual guests. Tailored offers, room options or services can be recommended based on guest behaviour and preferences.
• Workflow optimization:
  By predicting user behaviour, hotels can better plan their operational workflows and target their marketing and sales campaigns more efficiently. This results in improved resource utilization and an enhanced customer experience.
• Improved market segmentation:
  Analysis of user behaviour enables finer market segmentation for hotels. Specific audiences can be identified and targeted to create personalized offers and experiences.
• Identify the right customers:
  Insights from user data analysis can be used to identify and target “perfect customers”. These highly valuable customers can be pampered with special offers and services to increase their loyalty to the hotel.

<div class="article_quote"><div class="article_quote_contain"><div class="article_quote_quote">"Such recommendation systems capture user behaviour across different sessions on different websites and identify patterns to create personalized recommendations".</div><div class="article_quote_name u-text-style-main">Ass.-Prof. Dr. Eva Zangerle</div></div></div>