Name
Abstract | ||
|---|---|---|
Context-aware personalization is one of the possible ways to face the problem of information overload, that is, the difficulty of understanding an issue and making decisions when receiving too much information. Context-aware personalization can reduce the information noise, by proposing to the users only the information which is relevant to their current contexts. In this work we propose an approach that uses data mining algorithms to automatically infer the subset of data that, for each context, must be presented to the user, thus reducing the information noise. |
Year | Venue | Field |
|---|---|---|
2016 | ADBIS (Short Papers and Workshops) | Data mining,Information overload,Information retrieval,Computer science,Association rule learning,Data mining algorithm,Big data,Personalization |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paolo Garza | 1 | 426 | 39.13 |
| Elisa Quintarelli | 2 | 521 | 38.17 |
| Emanuele Rabosio | 3 | 30 | 4.63 |
| Letizia Tanca | 4 | 2330 | 590.73 |