Exploring User Concerns about Disclosing Location and Emotion Information in Group Recommendations

Najafian, Sharareh; Draws, Tim; Barile, Francesco; Tkalčič, Marko; Ye, Jie; Tintarev, Nava

doi:10.1145/3465336.3475104

Cited by 5 publications

(3 citation statements)

References 22 publications

(16 reference statements)

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“…When providing explanations for groups, another goal that should be considered is the privacy-preservation [26][27][28]. Privacy protection is a challenging issue, as a simple strategy to improve the explanation of recommended results is to inform group members about other people's preferences.…”

Section: Privacy In Group Recommender Systems Explanationsmentioning

confidence: 99%

“…We plan to perform an online evaluation, through a set if user studies. We aim at evaluating the quality of explanations generated by the proposed approach for the entire group or a specific user in terms of transparency, effectiveness, trust, and privacy perception [26][27][28][29]41], using social chioce-based explanations as baseline [2,43].…”

Section: Catch Me If You Canmentioning

confidence: 99%

“…In the context of GRS, another aspect to consider when providing explanations is the necessity of preserving the user's privacy, as the information included in the explanations are shared with all the group members [26][27][28]. Privacy in GRS refers to protecting the sensitive information of individual members within the group.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Argumentative Framework for Generating Explainable Group Recommendations

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Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization

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Section: Privacy In Group Recommender Systems Explanationsmentioning

confidence: 99%

Section: Catch Me If You Canmentioning

confidence: 99%

See 1 more Smart Citation

An Argumentative Framework for Generating Explainable Group Recommendations

Kuijk

Mahmoudi

Wen

et al. 2023

Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization

Self Cite

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KGR: A Kernel-Mapping Based Group Recommender System Using Trust Relations

Bukhari,

Maqsood,

Aadil

2024

Neural Process Lett

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A massive amount of information explosion over the internet has caused a possible difficulty of information overload. To overcome this, Recommender systems are systematic tools that are rapidly being employed in several domains such as movies, travel, E-commerce, and music. In the existing research, several methods have been proposed for single-user modeling, however, the massive rise of social connections potentially increases the significance of group recommender systems (GRS). A GRS is one that jointly recommends a list of items to a collection of individuals based on their interests. Moreover, the single-user model poses several challenges to recommender systems such as data sparsity, cold start, and long tail problems. On the contrary hand, another hotspot for group-based recommendation is the modeling of user preferences and interests based on the groups to which they belong using effective aggregation strategies. To address such issues, a novel “KGR” group recommender system based on user-trust relations is proposed in this study using kernel mapping techniques. In the proposed model, user-trust networks or relations are exploited to generate trust-based groups of users which is one of the important behavioral and social aspects. More precisely, in KGR the group kernels and group residual matrices are exploited as well as seeking a multi-linear mapping between encoded vectors of group-item interactions and probability density function indicating how groups will rate the items. Moreover, to emphasize the relevance of individual preferences of users in a group to which they belong, a hybrid approach is also suggested in which group kernels and individual user kernels are merged as additive and multiplicative models. Furthermore, the proposed KGR is validated on two different trust-based datasets including Film Trust and CiaoDVD. In addition, KGR outperforms with an RMSE value of 0.3306 and 0.3013 on FilmTrust and CiaoDVD datasets which are lower than the 1.8176 and 1.1092 observed with the original KMR.

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Evaluating explainable social choice-based aggregation strategies for group recommendation

Barile

Draws

Inel

et al. 2023

User Model User-Adap Inter

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Social choice aggregation strategies have been proposed as an explainable way to generate recommendations to groups of users. However, it is not trivial to determine the best strategy to apply for a specific group. Previous work highlighted that the performance of a group recommender system is affected by the internal diversity of the group members’ preferences. However, few of them have empirically evaluated how the specific distribution of preferences in a group determines which strategy is the most effective. Furthermore, only a few studies evaluated the impact of providing explanations for the recommendations generated with social choice aggregation strategies, by evaluating explanations and aggregation strategies in a coupled way. To fill these gaps, we present two user studies (N=399 and N=288) examining the effectiveness of social choice aggregation strategies in terms of users’ fairness perception, consensus perception, and satisfaction. We study the impact of the level of (dis-)agreement within the group on the performance of these strategies. Furthermore, we investigate the added value of textual explanations of the underlying social choice aggregation strategy used to generate the recommendation. The results of both user studies show no benefits in using social choice-based explanations for group recommendations. However, we find significant differences in the effectiveness of the social choice-based aggregation strategies in both studies. Furthermore, the specific group configuration (i.e., various scenarios of internal diversity) seems to determine the most effective aggregation strategy. These results provide useful insights on how to select the appropriate aggregation strategy for a specific group based on the level of (dis-)agreement within the group members’ preferences.

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Exploring User Concerns about Disclosing Location and Emotion Information in Group Recommendations

Cited by 5 publications

References 22 publications

An Argumentative Framework for Generating Explainable Group Recommendations

An Argumentative Framework for Generating Explainable Group Recommendations

KGR: A Kernel-Mapping Based Group Recommender System Using Trust Relations

Evaluating explainable social choice-based aggregation strategies for group recommendation

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