Fair multi-stakeholder news recommender system with hypergraph ranking

Gharahighehi, Alireza; Vens, Celine; Pliakos, Konstantinos

doi:10.1016/j.ipm.2021.102663

Cited by 47 publications

(19 citation statements)

References 35 publications

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“…In these domains there are other types of metadata such as lyrics, genres, artists, item descriptions or a hierarchy of item categories that should be used to diversify recommendations. Finally, we will investigate how the performance of neighborhood-based SBRSs can be enhanced in other performance criteria such as fairness [5] and serendipity.…”

Section: Discussionmentioning

confidence: 99%

Diversification in Session-based News Recommender Systems

Gharahighehi,

Vens

2021

Preprint

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Recommender systems are widely applied in digital platforms such as news websites to personalize services based on user preferences. In news websites most of users are anonymous and the only available data is sequences of items in anonymous sessions. Due to this, typical collaborative filtering methods, which are highly applied in many applications, are not effective in news recommendations. In this context, session-based recommenders are able to recommend next items given the sequence of previous items in the active session. Neighborhood-based session-based recommenders has been shown to be highly effective compared to more sophisticated approaches. In this study we propose scenarios to make these session-based recommender systems diversity-aware and to address the filter bubble phenomenon. The filter bubble phenomenon is a common concern in news recommendation systems and it occurs when the system narrows the information and deprives users of diverse information. The results of applying the proposed scenarios show that these diversification scenarios improve the diversity measures in these session-based recommender systems based on four news datasets.

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Section: Discussionmentioning

confidence: 99%

Diversification in Session-based News Recommender Systems

Gharahighehi,

Vens

2021

Preprint

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“…• News author [66], music artist [67], movie director [68] Target Producer's demographic or general information -sensitive attribute based on to which demographic group the item producer belongs, e.g., male vs. female artists.…”

Section: Notions Of Fairnessmentioning

confidence: 99%

Fairness in Recommender Systems: Research Landscape and Future Directions

Deldjoo¹,

Jannach²,

Bellogín³

et al. 2022

Preprint

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Recommender systems can strongly influence which information we see online, e.g, on social media, and thus impact our beliefs, decisions, and actions. At the same time, these systems can create substantial business value for different stakeholders. Given the growing potential impact of such AI-based systems on individuals, organizations, and society, questions of fairness have gained increased attention in recent years. However, research on fairness in recommender systems is still a developing area. In this survey, we first review the fundamental concepts and notions of fairness that were put forward in the area in the recent past. Afterwards, we provide a survey of how research in this area is currently operationalized, for example, in terms of the general research methodology, fairness metrics, and algorithmic approaches. Overall, our analysis of recent works points to certain research gaps. In particular, we find that in many research works in computer science very abstract problem operationalizations are prevalent, which circumvent the fundamental and important question of what represents a fair recommendation in the context of a given application.

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“…Therefore the feedback elicitation should be adapted to capture this implicit feedback from users. -Multiple stakeholders: In some contexts, such as job finding or news aggregator websites [10,11], there are multiple stakeholders in the system and therefore in the rating elicitation procedure the preferences of different stakeholders should be considered.…”

Section: Free Ratingsmentioning

confidence: 99%

An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering

Gharahighehi¹,

Nakano²,

Vens³

2022

Preprint

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Recommender systems are information retrieval methods that predict user preferences to personalize services. These systems use the feedback and the ratings provided by users to model the behavior of users and to generate recommendations. Typically, the ratings are quite sparse, i.e., only a small fraction of items are rated by each user. To address this issue and enhance the performance, active learning strategies can be used to select the most informative items to be rated. This rating elicitation procedure enriches the interaction matrix with informative ratings and therefore assists the recommender system to better model the preferences of the users. In this paper, we evaluate various non-personalized and personalized rating elicitation strategies. We also propose a hybrid strategy that adaptively combines a non-personalized and a personalized strategy. Furthermore, we propose a new procedure to obtain free ratings based on the side information of the items. We evaluate these ideas on the MovieLens dataset. The experiments reveal that our proposed hybrid strategy outperforms the strategies from the literature. We also propose the extent to which free ratings are obtained, improving further the performance and also the user experience.

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Fair multi-stakeholder news recommender system with hypergraph ranking

Cited by 47 publications

References 35 publications

Diversification in Session-based News Recommender Systems

Diversification in Session-based News Recommender Systems

Fairness in Recommender Systems: Research Landscape and Future Directions

An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering

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