Evaluating recommender systems from the user’s perspective: survey of the state of the art

By Pearl Huan Z. Pu, Li Chen, Rong Hu. User modeling and user-adapted interaction, 2012

400 citations recorded by the source corpus.

Subfields: Computer Science Applications, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/bc0d61fd1a4e301b08ce7fff76884715d9aa9b85

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In Computer Science Applications

In Information Systems

In Management Science and Operations Research

Citations in the corpus

Current challenges and visions in music recommender systems research (2017, background): “into account a more realistic scenario when studying the active learning techniques in recommender systems, which can be−er picture how the system evolves over time when ratings are provided by users [143, 146]. 2.3 Challenge 2: Automatic playlist continuation Problem de•nition: In its most generic ”
Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities (2016, background): “The intent of proper user control is to increase recommendations accuracy (Pu et al., 2012) by incorporating user input and feedback.”
Recommender systems: from algorithms to user experience (2012, background): “Pu et al. (2012) provides a more detailed review of preference elicitation and refinement research to improve recommender system user experience.”
Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning (2017, background): “In Pu et al. (2012) review of recommender systems from the user’s perspective, they focused on three key interactions between the user and the system: the initial preference elicitation process, the preference refinement process, and the presentation of the system’s recommendation results.”
Transparent, Scrutable and Explainable User Models for Personalized Recommendation (2019, background): “Explanations can also help to make a system scrutable, that is, allow users to correct the system’s reasoning or modify preferences in the user model [36].”
A survey of active learning in collaborative filtering recommender systems (2016, background): “It has been shown that a proper design and development of the user interface for rating elicitation, namely, by giving to the users more process control, could result in a higher motivation for rating and ultimately can lead to more accurate recommendations [53].”
Music Recommendation (2016, background): “More details about state-of-the-art user-centric evaluation procedures can be found in [30].”
Explaining the user experience of recommender systems (2012, background): “An integrated view on the user experience of recommender systems can be obtained by means of user-centric development (McNee et al. 2006b) and evaluation (Pu and Chen 2010; Pu et al. 2012).”
Evaluating Recommender Systems with User Experiments (2015, background): “[96] provide an overview of the state-of-the-art of user-centric recommender systems studies.”
Evaluating the effectiveness of explanations for recommender systems (2012, background): “3 Parallels can be drawn with the distinction made between perceived and actual accuracy of recommendations as discussed in the usability framework proposed by Pu et al. (2012).”
Research-paper recommender systems: a literature survey (2015, background): “For instance, users might become dissatisfied with accurate recommendations when they have no trust in the recommender system’s operator [342], their privacy is not ensured [300], they need to wait too long for recommendations [300], or they find the user interfaces unappealing [343].”
Automatic Generation of Natural Language Explanations (2017, background): “rmation about items, since they describe personal usage experience from users. Furthermore, this source plays an important role on the user side, since he/she tend to trust the opinion of other users [5, 13, 22]. arXiv:1707.01561v1 [cs.CL] 4 Jul 2017 DLRS’17, August 2017, Como, Italy Felipe Costa, S”