NDLI: Improving Collaborative Filtering Based Recommenders Using Topic Modelling

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Improving Collaborative Filtering Based Recommenders Using Topic Modelling

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Improving Collaborative Filtering Based Recommenders Using Topic Modelling

Content Provider	ACM Digital Library
Author	Chaudhury, Santanu Wilson, Jobin Lall, Brejesh
Abstract	Standard Collaborative Filtering (CF) algorithms make use of interactions between users and items in the form of implicit or explicit ratings alone for generating recommendations. Similarity among users or items is calculated purely based on rating overlap in this case, without considering explicit properties of users or items involved, limiting their applicability in domains with very sparse rating spaces. In many domains such as movies, news or electronic commerce recommenders, considerable contextual data in text form describing item properties is available along with the rating data, which could be utilized to improve recommendation quality. In this paper, we propose a novel approach to improve standard CF based recommenders by utilizing latent Dirichlet allocation (LDA) to learn latent properties of items, expressed in terms of topic proportions, derived from their textual description. We infer user's topic preferences or user profile in the same latent space, based on her historical ratings. While computing similarity between users, we make use of a combined similarity measure involving rating overlap as well as similarity in the latent topic space. This approach alleviates sparsity problem as it allows calculation of similarity between users even if they have not rated any items in common. Our experiments on multiple public datasets indicate that the proposed hybrid approach significantly outperforms standard User Based and Item Based CF recommenders in terms of classification accuracy metrics such as precision, recall and F-measure.
Starting Page	340
Ending Page	346
Page Count	7
File Format	PDF
ISBN	9781479941438
DOI	10.1109/WI-IAT.2014.54
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-08-11
Access Restriction	Subscribed
Subject Keyword	Recommender systems collaborative filtering latent dirichlet allocation (lda)
Content Type	Text
Resource Type	Article

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