Cross-domain Learning Framework for Book-Movie Recommendation with RoBERTa and DistilBERT in Action

Cross-domain Learning Framework for Book-Movie Recommendation with RoBERTa and DistilBERT in Action | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 1 April 2025 V1 Latest version Share on Cross-domain Learning Framework for Book-Movie Recommendation with RoBERTa and DistilBERT in Action Authors : Shubhanshi Singhal 0000-0002-5813-7646 [email protected] and Vikram Singh 0000-0001-6315-0872 Authors Info & Affiliations https://doi.org/10.22541/au.174351635.54917844/v1 325 views 183 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Recommender systems (RSs) aim to streamline navigation through vast product repositories, with personalization as a critical development. However, modeling user preferences remains challenging due to their dynamic and complexity. Cross-domain learning (CDL) has emerged as a promising approach to enhance personalization by leveraging inter-domain knowledge. Despite advancements, modeling inter-domain knowledge is difficult due to the semantic heterogeneity of participating domains. This paper presents a cross-domain recommendation (CDR) framework and evaluates its effectiveness on ’Book-Movie’ item pairs, leveraging books as auxiliary data to enhance movie recommendations. Books offer richer contextual insights into users’ cognitive states, thereby addressing legacy challenges such as data sparsity and the cold-start problem. Using pre-trained models such as RoBERTa and DistilBERT, we propose a novel approach for inter-domain knowledge modeling, leveraging the capability of generative models to effectively capture inter-domain knowledge and transfer it to achieve a higher level of personalization in the target domain. RoBERTa, fine-tuned on book data, effectively captures contextual relationships and bridging semantic gaps between domains, whereas DistilBERT captures deep semantic relationships between the textual content of the book and movie domains. Evaluation metrics include LRAP, cross-entropy loss, and precision. RoBERTa outperformed DistilBERT, achieving LRAP (0.908), cross-entropy (0.269), and precision (0.96). Similarity measures, including domain overlap (0.992), domain generalization (0.7895), pairwise difference on Genre (1.418), and Exact Match Ratio (0.60) highlight strong alignment between book and movie genres. This work emphasizes a multi-label classification strategy and novel algorithm for cross-domain knowledge modeling, offering a robust solution for CDR challenges with effective trade-off metrics. Supplementary Material File (roberta_expert_systems.pdf) Download 886.54 KB Information & Authors Information Version history V1 Version 1 01 April 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords cold start cross-domain learning cross-domain pre-training cross-domain recommendation system data sparsity semantic heterogeneity Authors Affiliations Shubhanshi Singhal 0000-0002-5813-7646 [email protected] Central University of Haryana View all articles by this author Vikram Singh 0000-0001-6315-0872 National Institute of Technology Kurukshetra View all articles by this author Metrics & Citations Metrics Article Usage 325 views 183 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Shubhanshi Singhal, Vikram Singh. Cross-domain Learning Framework for Book-Movie Recommendation with RoBERTa and DistilBERT in Action. Authorea . 01 April 2025. DOI: https://doi.org/10.22541/au.174351635.54917844/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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Kamala Kumari, CNN-RNN: A Hybrid Convolutional and Recurrent Neural Network Approach for Cross-Domain Sentiment Analysis Using the Webemo Dataset, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 34 , 04, (465-491), (2026). https://doi.org/10.1142/S0218488526500194 Crossref Ajeet Kumar, Kumar Abhishek, Ahamed Shafeeq B M, SentXFormer: a transformer-enhanced hybrid deep learning framework for cross-domain sentiment analysis of customer reviews, Scientific Reports, 16 , 1, (2025). https://doi.org/10.1038/s41598-025-33526-1 Crossref Loading... View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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