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STATISTICAL RELATIONAL LEARNING GROUP @ UMD



 

User Preferences for Hybrid Explanations

Pigi Kouki, James Schaffer, Jay Pujara, John ODonovan, Lise Getoor
11th ACM Conference on Recommender Systems (RecSys) - 2017
Download the publication : kouki-recsys17-sup-mat.pdf [1.4Mo]   kouki-recsys17.pdf [2.7Mo]  
Hybrid recommender systems combine several different sources of information to generate recommendations. These systems have been shown to improve accuracy compared to single-source recommendation strategies. However, hybrid recommendation strategies are inherently more complex than those that use a single source of information, and thus the process of explaining recommendations to consumers becomes more challenging. In this paper we describe a hybrid recommender system built on a probabilistic programming language, and discuss the benefits and challenges of explaining its recommendations to users. We perform a mixed model statistical analysis of user preferences for explanations in this system. We present results of an online user survey that evaluates explanations for hybrid algorithms in a variety of text and visual, graph-based formats, that are either novel designs or derived from existing hybrid recommender systems.

BibTex references

@InProceedings{kouki:recsys17,
  author       = "Kouki, Pigi and Schaffer, James and Pujara, Jay and ODonovan, John and Getoor, Lise",
  title        = "User Preferences for Hybrid Explanations",
  booktitle    = "11th ACM Conference on Recommender Systems (RecSys)",
  year         = "2017",
}

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