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TagEmbedSVD: Leveraging Tag Embeddings for Cross-Domain Collaborative Filtering

Vijaikumar, M and Shevade, S and Murty, MN (2019) TagEmbedSVD: Leveraging Tag Embeddings for Cross-Domain Collaborative Filtering. In: 8th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2019, 17 - 20 December 2019, Tezpur, pp. 240-248.

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Official URL: https://doi.org/10.1007/978-3-030-34872-4_27


Cross-Domain Collaborative Filtering (CDCF) mitigates data sparsity and cold-start issues present in conventional recommendation systems by exploiting and transferring knowledge from related domains. Leveraging user-generated tags (e.g. ancient-literature, military-history) for bridging the related domains is becoming a popular way for enhancing personalized recommendations. However, existing tag based models bridge the domains based on common tags between domains and their co-occurrence frequencies. This results in capturing the syntax similarities between the tags and ignoring the semantic similarities between them. In this work, to address these, we propose TagEmbedSVD, a tag-based CDCF model to cross-domain setting. TagEmbedSVD makes use of the pre-trained word embeddings (word2vec) for tags to enhance personalized recommendations in the cross-domain setting. Empirical evaluation on two real-world datasets demonstrates that our proposed model performs better than the existing tag based CDCF models. © 2019, Springer Nature Switzerland AG.

Item Type: Conference Paper
Publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher: Springer
Additional Information: The copyright for this article belongs to Springer.
Keywords: Artificial intelligence; Embeddings; Pattern recognition; Semantics, Cross-domain; Data sparsity; Empirical evaluations; Personalized recommendation; Real-world datasets; Semantic similarity; Tag based models; User-generated, Collaborative filtering
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 05 Dec 2022 06:26
Last Modified: 05 Dec 2022 06:26
URI: https://eprints.iisc.ac.in/id/eprint/78224

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