Name
Abstract | ||
|---|---|---|
Agent-based recommender systems have been exploited in the last years to provide informative suggestions to users, showing the advantage of exploiting components like beliefs, goals and trust in the recommenda-tionsu0027 computation. However, many real-world scenarios, like the traffic one, require the additional feature of representing and reasoning about spatial and temporal knowledge, considering also their vague connotation. This paper tackles this challenge and introduces CARS, a spatio-temporal agent-based recommender system based on the Belief-Desire-Intention (BDI) architecture. Our approach extends the BDI model with spatial and temporal information to represent and reason about fuzzy beliefs and desires dynamics. An experimental evaluation about spatio-temporal reasoning in the traffic domain is carried out using the NetLogo platform, showing the improvements our recommender system introduces to support agents in achieving their goals. |
Year | Venue | Field |
|---|---|---|
2018 | ICAART | Recommender system,Architecture,Computer science,Connotation,Fuzzy logic,Fuzzy set,NetLogo,Artificial intelligence,Machine learning,Computation,Region connection calculus |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amel Ben Othmane | 1 | 4 | 3.09 |
| Andrea Tettamanzi | 2 | 1 | 1.72 |
| serena villata | 3 | 558 | 70.95 |
| Nhan Le Thanh | 4 | 28 | 14.98 |