@article{
 title = {TasteWeights},
 type = {article},
 year = {2012},
 identifiers = {[object Object]},
 pages = {35},
 websites = {http://dl.acm.org/citation.cfm?doid=2365952.2365964},
 id = {96a5accd-5a73-3d5e-8c87-6e5ccb4f5bdc},
 created = {2018-12-04T13:17:58.836Z},
 file_attached = {false},
 profile_id = {2ed0fe69-06a2-3e8b-9bc9-5bdb197f1120},
 group_id = {e795dbfa-5576-3499-9c01-6574f19bf7aa},
 last_modified = {2018-12-14T12:16:32.818Z},
 read = {false},
 starred = {false},
 authored = {false},
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 hidden = {false},
 citation_key = {Bostandjiev2012},
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 abstract = {This paper presents an interactive hybrid recommendation system that generates item predictions from multiple social and semantic web resources, such as Wikipedia, Facebook, and Twitter. The system employs hybrid techniques from traditional recommender system literature, in addition to a novel interactive interface which serves to explain the recom- mendation process and elicit preferences from the end user. We present an evaluation that compares different interactive and non-interactive hybrid strategies for computing recom- mendations across diverse social and semantic web APIs. Results of the study indicate that explanation and interac- tion with a visual representation of the hybrid system in- crease user satisfaction and relevance of predicted content.},
 bibtype = {article},
 author = {Bostandjiev, Svetlin and O'Donovan, John and Höllerer, Tobias},
 journal = {Proceedings of the sixth ACM conference on Recommender systems - RecSys '12}
}

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