mRHR: A Modified Reciprocal Hit Rank Metric for Ranking Evaluation of Multiple Preferences in Top-N Recommender Systems

2016-09-10
Peker, Serhat
Koçyiğit, Altan
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.

Suggestions

3D cognitive map construction by active stereo vision in a virtual world
ULUSOY PARNAS, İLKAY; Halıcı, Uğur; Leblebicioğlu, Mehmet Kemal (2004-10-29)
In this study, a multi-scale phase based disparity algorithm is developed. This algorithm is then applied in a simulated world. In this world there is a virtual robot which has a stereo camera system simulated with the properties similar to human eyes and there are 3D virtual objects having predefined simple shapes. The virtual robot explores its environment intelligently based on some heuristics. Only stereo images rendered from the virtual world are supplied to the robot. The robot extracts depth informat...
On Equivalence Relationships Between Classification and Ranking Algorithms
Ertekin Bolelli, Şeyda (2011-10-01)
We demonstrate that there are machine learning algorithms that can achieve success for two separate tasks simultaneously, namely the tasks of classification and bipartite ranking. This means that advantages gained from solving one task can be carried over to the other task, such as the ability to obtain conditional density estimates, and an order-of-magnitude reduction in computational time for training the algorithm. It also means that some algorithms are robust to the choice of evaluation metric used; the...
OPTIMAL RANKING OF MEASUREMENTS FOR STATE ESTIMATION BY THE GRADIENT PROJECTION METHOD
SEVAIOGLU, O; ESKICIOGLU, AM; Leblebicioğlu, Mehmet Kemal (Elsevier BV, 1992-12-01)
Optimal measurement ranking is the first and essential step in meter selection for the design of a reliable measurement system. This paper presents a computationally-efficient algorithm for the optimal ranking of measurements for state estimation. The algorithm maximizes the accuracy of estimates with respect to the measurement variances by performing a transformation on the problem.formulation, and minimizing the resulting cost function subject to a set of linear constraints.
Hybrid ranking approaches based on data envelopment analysis and outranking relations
Eryılmaz, Utkan; Karasakal, Esra; Department of Industrial Engineering (2006)
In this study two different hybrid ranking approaches based on data envelopment analysis and outranking relations for ranking alternatives are proposed. Outranking relations are widely used in Multicriteria Decision Making (MCDM) for ranking the alternatives and appropriate in situations when we have limited information on the preference structure of the decision maker. Yet to apply these methods DM should provide exact values for method parameters (weights, thresholds etc.) as well as basic information suc...
A comparative analysis of global and national university ranking systems
Çakır, Murat Perit; Acartürk, Cengiz (2015-06-01)
Recent interest towards university rankings has led to the development of several ranking systems at national and global levels. Global ranking systems tend to rely on internationally accessible bibliometric databases and reputation surveys to develop league tables at a global level. Given their access and in-depth knowledge about local institutions, national ranking systems tend to include a more comprehensive set of indicators. The purpose of this study is to conduct a systematic comparison of national an...
Citation Formats
S. Peker and A. Koçyiğit, “mRHR: A Modified Reciprocal Hit Rank Metric for Ranking Evaluation of Multiple Preferences in Top-N Recommender Systems,” 2016, vol. 9883, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/30829.