{'fraction_non_alphanumeric': 0.02666666666666667, 'fraction_numerical': 0.0, 'mean_word_length': 5.2555555555555555, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Recommendation of Activity Sequences during Distributed Events

Leisure activities constitute an important part of our life. Nowadays, the offer of activities to undertake is constantly growing. This can be easily seen not only by the increasing number of social events created and promoted on Facebook, Couchsurfing, etc., but also by the appearance of specialised online services and event-based social networks, such as Meetup, Eventbrite, etc. Moreover, multi-day events (e.g. conventions, festivals, cruise trips, exhibitions), to which we refer to as distributed events, attract thousands of participants. Their attendees are often overwhelmed with the amount of available options. Recommender systems appear as a common solution in such a context. In this project, we formulate the problem of recommendation of activity sequences and aim at providing an integrated support for users to create a personalised itinerary of activities in order to facilitate their decision making process which events to join. Such assistance is expected to bring a positive impact on well-being and satisfaction with life of individuals.