2015
DOI: 10.1007/s13042-015-0371-4
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ART: group recommendation approaches for automatically detected groups

Abstract: Group recommender systems provide suggestions when more than a person is involved in the recommendation process. A particular context in which group recommendation is useful is when the number of recommendation lists that can be generated is limited (i.e., it is not possible to suggest a list of items to each user). In such a case, grouping users and producing recommendations to groups becomes necessary. None of the approaches in the literature is able to automatically group the users in order to overcome the … Show more

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Cited by 32 publications
(17 citation statements)
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“…Most of the research has focused on established and heterogeneous groups [48], [49], [50], [51]. Our proposed approach is designed to make recommendations on homogeneous and automatically identified groups [46], [52], [53]. Homogeneous groups are particularly relevant for marketing processes, where companies want to recommend products or services to a broad target of similar users.…”
Section: Recommendation To Groups Of Users and Proposed Approachmentioning
confidence: 99%
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“…Most of the research has focused on established and heterogeneous groups [48], [49], [50], [51]. Our proposed approach is designed to make recommendations on homogeneous and automatically identified groups [46], [52], [53]. Homogeneous groups are particularly relevant for marketing processes, where companies want to recommend products or services to a broad target of similar users.…”
Section: Recommendation To Groups Of Users and Proposed Approachmentioning
confidence: 99%
“…We call it VUR (Virtual User based Recommendation) [45], [51], [55] and we use it as a baseline. Method d) [56], [52] makes predictions before clustering, it performs aggregation post-clustering and it does not use dimensionality reduction. We will use it as a baseline using the name PC (Predict & Cluster).…”
Section: Recommendation To Groups Of Users and Proposed Approachmentioning
confidence: 99%
See 1 more Smart Citation
“…In this case, only one low rating is enough to penalize an item, because the group rating is the minimum of the whole set of ratings. For large groups, it is likely that every item has at least one low rating and, therefore, the group profile associated with a large group would be mainly composed by negative ratings, leading to low‐quality recommendations …”
Section: Preliminariesmentioning
confidence: 99%
“…For large groups, it is likely that every item has at least one low rating and, therefore, the group profile associated with a large group would be mainly composed by negative ratings, leading to low-quality recommendations. 23 Our aim is to propose a new group recommender model that keeps all group ratings (n ratings for each item, being n the size of the user group) instead of only one aggregated rating for each item, removing the aggregation stage to avoid the loss of information. To do so, we will use the concept of HFSs, which is introduced in the coming section.…”
Section: Group Recommender Systemsmentioning
confidence: 99%