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- W2894978526 abstract "Recommendation systems create personalized list of items that might interest the userby analyzing the user’s history of past purchases and/or consumption. Generally onlya small subset of the items are assessed by each user, and from the large subset ofunseen items, the systems need to produce an accurate list of recommendations.For rating based systems, most of the traditional methods for recommendationfocus on the absolute ratings provided by the users to the items. In this work,we extend the traditional Matrix Factorization approach for recommendation andpropose pairwise relation based factor modeling. We propose the method based onthe pairwise preferences between the items to capture the relative tendency of userselecting one item over the other.While modeling the items in the system, the use of pairwise preferences allowinformation flow between the items through the preference relations as an additionalinformation. Item feedbacks are available in the form of reviews apart from therating information. The reviews have textual information that can be really helpfulto represent the item’s latent feature vector appropriately. We perform topic modelingof the item reviews and use the topic vectors to guide the joint factor modeling of theusers and items and learn their final representations. The proposed methods showspromising results in comparison to the state-of-the-art methods in our experiments.vi" @default.
- W2894978526 created "2018-10-12" @default.
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- W2894978526 date "2018-01-01" @default.
- W2894978526 modified "2023-09-23" @default.
- W2894978526 title "PReFacTO: Preference Relations BasedFactor Model with Topic Awareness andOffset" @default.
- W2894978526 hasPublicationYear "2018" @default.
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