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Incorporating context into recommender systems: an empirical comparison of context-based approaches

Abstract

Recently, there has been growing interest in recommender systems (RSs) and particularly in context-aware RSs. Methods for generating context-aware recommendations were classified into the pre-filtering, post-filtering and contextual modeling approaches. This paper focuses on comparing the pre-filtering, the post-filtering, the contextual modeling and the un-contextual approaches and on identifying which method dominates the others and under which circumstances. Although some of these methods have been studied independently, no prior research compared the relative performance to determine which of them is better. This paper proposes an effective method of comparing the three methods to incorporate context and selecting the best alternatives. As a result, it provides analysts with a practical suggestion on how to pick a good approach in an effective manner to improve the performance of a context-aware recommender system.

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Notes

  1. For simplicity, we will use the terms “utility” and “rating” interchangeably in this paper, despite the fact that there are certain differences between these two notions.

  2. Note that the MAE and RMSE measures are not applicable to the “top-k” strategy because these measures are calculated on the whole matrix of predicted ratings.

Abbreviations

RSs:

Recommender systems

CARS:

Context-aware recommender systems

2D:

2-dimensional

PreF:

Contextual pre-filtering

PoF:

Contextual post-filtering

CM:

Contextual modeling

CTRs:

Click-through rates

EPF:

Exact pre-filtering;

CF:

Collaborative filtering

MAE:

Mean absolute error

RMSE:

Root mean square error

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Panniello, U., Gorgoglione, M. Incorporating context into recommender systems: an empirical comparison of context-based approaches. Electron Commer Res 12, 1–30 (2012). https://doi.org/10.1007/s10660-012-9087-7

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Keywords

  • Recommender systems
  • Context-aware
  • Collaborative filtering
  • Algorithms