Groupme: city data and multi-preference activity allocation

Liu, Tong (2015) Groupme: city data and multi-preference activity allocation. [Laurea magistrale], Università di Bologna, Corso di Studio in Informatica [LM-DM270], Documento ad accesso riservato.
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Classic group recommender systems focus on providing suggestions for a fixed group of people. Our work tries to give an inside look at design- ing a new recommender system that is capable of making suggestions for a sequence of activities, dividing people in subgroups, in order to boost over- all group satisfaction. However, this idea increases problem complexity in more dimensions and creates great challenge to the algorithm’s performance. To understand the e↵ectiveness, due to the enhanced complexity and pre- cise problem solving, we implemented an experimental system from data collected from a variety of web services concerning the city of Paris. The sys- tem recommends activities to a group of users from two di↵erent approaches: Local Search and Constraint Programming. The general results show that the number of subgroups can significantly influence the Constraint Program- ming Approaches’s computational time and e�cacy. Generally, Local Search can find results much quicker than Constraint Programming. Over a lengthy period of time, Local Search performs better than Constraint Programming, with similar final results.

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Liu, Tong
Relatore della tesi
Corso di studio
Ordinamento Cds
Parole chiave
artificial intelligence, recommender systems, local search, constraint programming, simulated annealing, performance evaluation
Data di discussione della Tesi
19 Marzo 2015

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