| Title |
Reeder, Samuel_MCS_2020 |
| Alternative Title |
Effects of Explanations of Recommendation Engines |
| Creator |
Reeder, Samuel |
| Collection Name |
Master of Computer Science |
| Description |
Recommendation engines are a ubiquitous feature in the modern era. The advent of the internet as a means of buying and selling products has pushed the field of artificial intelligence to produce software and algorithms that produce accurate predictions with respect to consumer's desirers and buying tendencies. Producing accurate recommendation engines generally involves the use of complex algorithms and machine learning driven models. The complexity of these models brings with it several disadvantages, one of these, is the lack of human comprehensibility. To resolve this issue research teams in the field are involved in programs that attempt to make artificial intelligence systems explainable. In the context of recommendation engines an explainable system would be one that provides users with a recommendation paired with an explanation that is relevant to the user. In this paper research is presented that gives insight into what types of explanations are most effective in changing a user's trust and understanding in recommendation engines. |
| Subject |
Computer science |
| Keywords |
Recommendation engines; Internet; Buying patterns |
| Digital Publisher |
Stewart Library, Weber State University |
| Date |
2020 |
| Language |
eng |
| Rights |
The author has granted Weber State University Archives a limited, non-exclusive, royalty-free license to reproduce their theses, in whole or in part, in electronic or paper form and to make it available to the general public at no charge. The author retains all other rights. |
| Source |
University Archives Electronic Records; Master of Computer Science. Stewart Library, Weber State University |
| Format |
application/pdf |
| ARK |
ark:/87278/s62wfnq7 |
| Setname |
wsu_smt |
| ID |
96827 |
| Reference URL |
https://digital.weber.edu/ark:/87278/s62wfnq7 |
