تقرير
Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering
| العنوان: | Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering |
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| المؤلفون: | Duricic, Tomislav, Hussain, Hussain, Lacic, Emanuel, Kowald, Dominik, Helic, Denis, Lex, Elisabeth |
| المصدر: | Lecture Notes in Computer Science, vol 12117. Springer, Cham. 2020 |
| سنة النشر: | 2020 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Social and Information Networks, Computer Science - Information Retrieval, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing |
| الوصف: | In this work, we study the utility of graph embeddings to generate latent user representations for trust-based collaborative filtering. In a cold-start setting, on three publicly available datasets, we evaluate approaches from four method families: (i) factorization-based, (ii) random walk-based, (iii) deep learning-based, and (iv) the Large-scale Information Network Embedding (LINE) approach. We find that across the four families, random-walk-based approaches consistently achieve the best accuracy. Besides, they result in highly novel and diverse recommendations. Furthermore, our results show that the use of graph embeddings in trust-based collaborative filtering significantly improves user coverage. Comment: 10 pages, Accepted as a full paper on the 25th International Symposium on Methodologies for Intelligent Systems (ISMIS'20) |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1007/978-3-030-59491-6_17 |
| URL الوصول: | http://arxiv.org/abs/2003.13345 |
| رقم الأكسشن: | edsarx.2003.13345 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1007/978-3-030-59491-6_17 |
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