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dc.contributor.authorLiu, Peng
dc.contributor.authorZhang, Lemei
dc.contributor.authorGulla, Jon Atle
dc.date.accessioned2020-01-20T09:10:15Z
dc.date.available2020-01-20T09:10:15Z
dc.date.created2019-09-17T11:39:41Z
dc.date.issued2019
dc.identifier.issn0306-4573
dc.identifier.urihttp://hdl.handle.net/11250/2636934
dc.description.abstractExplainable recommendation, which provides explanations about why an item is recommended, has attracted growing attention in both research and industry communities. However, most existing explainable recommendation methods cannot provide multi-model explanations consisting of both textual and visual modalities or adaptive explanations tailored for the user’s dynamic preference, potentially leading to the degradation of customers’ satisfaction, confidence and trust for the recommender system. On the technical side, Recurrent Neural Network (RNN) has become the most prevalent technique to model dynamic user preferences. Benefit from the natural characteristics of RNN, the hidden state is a combination of long-term dependency and short-term interest to some degrees. But it works like a black-box and the monotonic temporal dependency of RNN is not sufficient to capture the user’s short-term interest. In this paper, to deal with the above issues, we propose a novel Attentive Recurrent Neural Network (Ante-RNN) with textual and visual fusion for the dynamic explainable recommendation. Specifically, our model jointly learns image representations with textual alignment and text representations with topical attention mechanism in a parallel way. Then a novel dynamic contextual attention mechanism is incorporated into Ante-RNN for modelling the complicated correlations among recent items and strengthening the user’s short-term interests. By combining the full latent visual-semantic alignments and a hybrid attention mechanism including topical and contextual attentions, Ante-RNN makes the recommendation process more transparent and explainable. Extensive experimental results on two real world datasets demonstrate the superior performance and explainability of our model.nb_NO
dc.language.isoengnb_NO
dc.publisherElseviernb_NO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleDynamic attention-based explainable recommendation with textual and visual fusionnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.journalInformation Processing & Managementnb_NO
dc.identifier.doi10.1016/j.ipm.2019.102099
dc.identifier.cristin1725557
dc.description.localcode© 2019. This is the authors’ accepted and refereed manuscript to the article. Locked until 20.8.2021 due to copyright restrictions. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/nb_NO
cristin.unitcode194,63,10,0
cristin.unitnameInstitutt for datateknologi og informatikk
cristin.ispublishedtrue
cristin.fulltextpreprint
cristin.qualitycode2


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal