Individual and Group Decision Making and Recommender Systems

Jameson, Anthony; Willemsen, Martijn C.; Felfernig, Alexander

doi:10.1007/978-1-0716-2197-4_21

Cited by 5 publications

(1 citation statement)

References 70 publications

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“…Nearly any popular programming language has a library or framework for making single recommendations. Despite the amount of papers regarding the problem of generating recommendations for groups of users (group recommender systems, GRSs) [4,5], a firm ground for GRSs does not exist. This is exacerbated by the difficulty of accessing to or generating datasets that gather information of actual group recommender systems [6,7,8,9].…”

Section: Introductionmentioning

confidence: 99%

Enabling Reproducibility in Group Recommender Systems

Silveira

Salamó

Boratto

2022

Frontiers in Artificial Intelligence and Applications

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Reproducibility is a challenging aspect that considerably affects the quality of most scientific papers. To deal with this, many open frameworks allow to build, test, and benchmark recommender systems for single users. Group recommender systems involve additional tasks w.r.t. those for single users, such as the identification of the groups, or their modeling. While this clearly amplifies the possible reproducibility issues, to date, no framework to benchmark group recommender systems exists. In this work, we enable reproducibility in group recommender systems by extending the LibRec library, which stands out as one of the richest, with more than 70 different recommender algorithms, good performance and several evaluation metrics. Specifically, we include several approaches for all the stages of group recommender systems: group formation, group modeling strategies, and evaluation. To validate our framework, we consider a use-case that compares several group building, recommendation, and group modeling approaches.

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Section: Introductionmentioning

confidence: 99%

Enabling Reproducibility in Group Recommender Systems

Silveira

Salamó

Boratto

2022

Frontiers in Artificial Intelligence and Applications

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Decision Tasks and Basic Algorithms

Felfernig,

Atas,

Stettinger

2023

Signals and Communication Technology

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Harnessing distributional semantics to build context-aware justifications for recommender systems

Musto,

Spillo,

Semeraro

2023

User Model User-Adap Inter

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This paper introduces a methodology to generate review-based natural language justifications supporting personalized suggestions returned by a recommender system. The hallmark of our strategy lies in the fact that natural language justifications are adapted to the different contextual situations in which the items will be consumed. In particular, our strategy relies on the following intuition: Just like the selection of the most suitable item is influenced by the contexts of usage, a justification that supports a recommendation should vary as well. As an example, depending on whether a person is going out with her friends or her family, a justification that supports a restaurant recommendation should include different concepts and aspects. Accordingly, we designed a pipeline based on distributional semantics models to generate a vector space representation of each context. Such a representation, which relies on a term-context matrix, is used to identify the most suitable review excerpts that discuss aspects that are particularly relevant for a certain context. The methodology was validated by means of two user studies, carried out in two different domains (i.e., movies and restaurants). Moreover, we also analyzed whether and how our justifications impact on the perceived transparency of the recommendation process and allow the user to make more informed choices. As shown by the results, our intuitions were supported by the user studies.

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Individual and Group Decision Making and Recommender Systems

Cited by 5 publications

References 70 publications

Enabling Reproducibility in Group Recommender Systems

Enabling Reproducibility in Group Recommender Systems

Decision Tasks and Basic Algorithms

Harnessing distributional semantics to build context-aware justifications for recommender systems

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