Group recommendations with rank aggregation and collaborative filtering

By Linas Baltrunas, Tadas Makcinskas, Francesco Ricci. ACM Conference on Recommender Systems, 2010

547 citations recorded by the source corpus.

Subfields: Information Systems, Management Science and Operations Research, Signal Processing

View Paper: https://www.semanticscholar.org/paper/02e573d518abc7fa29d5120468cde1fcc97b170f

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In Information Systems

In Management Science and Operations Research

In Signal Processing

Citations in the corpus

An Efficient Group Recommendation Model With Multiattention-Based Neural Networks (2020, result): “Similar to the previous works [28], [33], [48], we mainly consider two types of groups in our experimental evaluation (i.”
Collaborative Filtering beyond the User-Item Matrix (2014, methodology): “The effectiveness of the two strategies for group recommendation has been investigated in recent studies by using either simulated data of user groups [Baltrunas et al. 2010] or real data about families of users [Berkovsky and Freyne 2010].”
Spectral Method and Regularized MLE Are Both Optimal for Top-$K$ Ranking (2017, unknown): “This problem, which is called top-K rank aggregation, finds applications in numerous contexts, including web search (Dwork et al., 2001), recommendation systems (Baltrunas et al., 2010; Park et al., 2015), sports competition (Masse, 1997), and so on.”
Optimal full ranking from pairwise comparisons (2021, background): “This highdimensional statistical estimation problem has important applications in many areas such as recommendation systems [1, 7], sports and gaming [2, 17, 23, 40, 41, 49], web search [16, 19], social choices [33, 36, 38, 39, 47], psychology [13, 34, 52], information retrieval [8, 32], etc.”
Make it personal: A social explanation system applied to group recommendations (2017, background): “These systems commonly aggregate real or predicted ratings for group members (Jameson and Smyth, 2007; Baltrunas et al., 2010; Berkovsky and Freyne, 2010; Pessemier et al., 2013).”
Recommending Groups to Users Using User-Group Engagement and Time-Dependent Matrix Factorization (2016, background): “For completeness, in literature group recommendation also refers to the problem of recommending items to a group of users, which is not our focus in this paper and we refer readers to (Ali and Kim 2015; Baltrunas, Makcinskas, and Ricci 2010) for more detailed information if interested.”