Precision-oriented evaluation of recommender systems: an algorithmic comparison

By Alejandro Bellogín, Pablo Castells, Iván Cantador. 2011

226 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Pedro G. Campos
2. Fernando Díez
3. Iván Cantador
4. Tinghuai Ma
5. Sungyoung Lee

In Information Systems

1. Pedro G. Campos
2. Fernando Díez
3. Iván Cantador
4. Sungyoung Lee

In Management Science and Operations Research

1. Pedro G. Campos
2. Fernando Díez
3. Iván Cantador
4. Sungyoung Lee

Citations in the corpus

• Recurrent Neural Networks with Top-k Gains for Session-based Recommendations (2017, neutral): “ target item’s score to that of the 50,000 most popular items during testing, similarly to [8]. While this evaluation for VIDXL overestimates the performance, the comparison of algorithms remain fair [2]. As recommender systems can only recommend a few items at once, the actual item a user might pic”
• Diversity, Serendipity, Novelty, and Coverage (2016, unknown): “In recent years, rating-based accuracy metrics for offline RS evaluations have been replaced by precision-oriented metrics that more closely reflect the users’ interaction with the system—considering only a small set of topranked recommendations, ignoring the lower-ranked items [Bellogı́n et al. 201”
• Social Network and Tag Sources Based Augmenting Collaborative Recommender System (2015, methodology): “Different from the methodologies in [21], we search test items from nearest neighbors.”
• Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations (2016, unknown): “While this evaluation methodology somewhat overestimates the rank and thus the evaluated metrics are a little bit higher, the comparison of the algorithms remains fair [1].”
• Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols (2013, methodology): “In the recommender systems field, despite the fact that certain metrics have been accepted and are commonly used (Herlocker et al. 2004; Gunawardana and Shani 2009), there is no consensus on the methodologies used (Bellogín et al. 2011).”