Name
Title | ||
|---|---|---|
Mrhr: A Modified Reciprocal Hit Rank Metric For Ranking Evaluation Of Multiple Preferences In Top-N Recommender Systems |
Abstract | ||
|---|---|---|
Average reciprocal hit rank (ARHR) is a commonly used metric for ranking evaluation of top-n recommender systems. However, it suffers from an important shortcoming that it cannot be applied when the user has multiple preferences at a time. In order to overcome this problem, a modified version of ARHR metric is introduced and applied to grocery shopping domain by conducting a series of experiments on real-life data. The results show that the proposed measure is feasible for ranking evaluation of Top-N recommender systems in the cases where the users have multiple preferences at a time or a specific time interval. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-44748-3_31 | ARTIFICIAL INTELLIGENCE: METHODOLOGY, SYSTEMS, AND APPLICATIONS, AIMSA 2016 |
Keywords | Field | DocType |
Top-N recommender systems, Ranking evaluation, ARHR, Reciprocal rank, Hit rank | Recommender system,Reciprocal,Ranking,Computer science,Grocery shopping,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
9883 | 0302-9743 | 2 |
PageRank | References | Authors |
0.39 | 14 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peker, S. | 1 | 2 | 1.40 |
| Altan Koçyigit | 2 | 22 | 8.09 |