THOR: A Hybrid Recommender System for the Personalized Travel Experience

One of the travelers’ main challenges is that they have to spend a great effort to find and choose the most desired travel offer(s) among a vast list of non-categorized and non-personalized items. Recommendation systems provide an effective way to solve the problem of information overload. In this work, we design and implement “The Hybrid Offer Ranker” (THOR), a hybrid, personalized recommender system for the transportation domain. THOR assigns every traveler a unique contextual preference model built using solely their personal data, which makes the model sensitive to the user’s choices. This model is used to rank travel offers presented to each user according to their personal preferences. We reduce the recommendation problem to one of binary classification that predicts the probability with which the traveler will buy each available travel offer. Travel offers are ranked according to the computed probabilities, hence to the user’s personal preference model. Moreover, to tackle the cold start problem for new users, we apply clustering algorithms to identify groups of travelers with similar profiles and build a preference model for each group. To test the system’s performance, we generate a dataset according to some carefully designed rules. The results of the experiments show that the THOR tool is capable of learning the contextual preferences of each traveler and ranks offers starting from those that have the higher probability of being selected

A multi-perspective approach for analyzing long-running live events on social media. A case study on the “Big Four” international fashion weeks

In the last few years, thanks to the emergence of Web 2.0, social media has made the concept of online live events possible. Users participate more and more in long-running recurring events in social media by sharing their experiences and desires. In the last few years, thanks to the emergence of Web 2.0, social media has made the concept of online live events possible. Users participate more and more in long-running recurring events in social media by sharing their experiences and desires. This work introduces long-running live events (LRLEs), as a type of activity that span physical spaces and digital ecosystems, including social media. LRLEs encompass several individuals, organizations, and brands collaborating/competing in the same event. This provides unprecedented opportunities to understand the dynamics and behavior of event-oriented participation, through collection and analysis of data of user behaviors enabled by the Web platform, where most of the digital traces are left by users. What makes this setting interesting is that the behaviors that are traced are not focused only on one individual brand or organization, and thus allows one to understand and compare the respective roles and influence in a defined setting. In this paper we provide a high-level and multi-perspective roadmap to mine, model, and study LRLEs. Among the various aspects, we develop a multi-modal approach to solve the problem of post popularity prediction that exploits potentially influential factors within LRLE. We employ two methods for implementing feature selection, together with an automated grid search for optimizing hyper-parameters in various regression methods

Analyzing Brand Awareness Strategies on Social Media in the Luxury Market: The Case of Italian Fashion on Instagram

The rapid proliferation of social media has been redefining every facet of the old marketing and customer engagement tactics, not only for low-end and mass-market products but also for luxury brands. In this context, brands are dealing with the challenge of maintaining a balance between using mass marketing strategies concurrent with accentuating the exclusivity of their offerings. Social media can be considered beneficial if brands employ it to reach the right audience and use the right platform and incorporating the right content. In this work, we propose a sector-specific, integrated, and holistic investigation of the social media strategies of luxury brands together with the impact they generate in terms of the engagement level of the users as an indicator of their success. We provide empirical validation of the methods used in the Italian market of the luxury fashion sector, providing a qualitative and quantitative analysis of the content shared on social media, considering the type, timing, and modality of the sharing. We evaluate consumer-brand engagement in different contexts, including important live events in the field