Giuseppe Spillo
ABSTRACT
In the last few years, Knowledge-Aware Recommender Systems (KARSs) got an increasing interest in the community thanks to their ability at encoding diverse and heterogeneous data sources, both structured (such as knowledge graphs) and unstructured (such as plain text). Indeed, as shown by several shreds of evidence, thanks to the combination of such information, KARSs are able to provide competitive performances in several scenarios. In particular, state-of-the-art KARSs leverage the current wave of deep learning and are able to process and exploit large corpora of information that provide complementary and useful characteristics of the items, including knowledge graphs, descriptive properties, reviews, text, and multimedia content. The objective of my Ph.D. is to investigate methods to design and develop knowledge-aware recommendation models based on the merging of heterogeneous embeddings. Based on the combination of diverse information sources, I plan to develop novel models able to provide accurate, fair, and explainable recommendations.
- Massimiliano Albanese, Antonio d’Acierno, Vincenzo Moscato, Fabio Persia, and Antonio Picariello. 2013. A multimedia recommender system. ACM Transactions on Internet Technology (TOIT) 13, 1 (2013), 1–32.Google Scholar
Digital Library
- Ivana Andjelkovic, Denis Parra, and John O’Donovan. 2019. Moodplay: interactive music recommendation based on artists’ mood similarity. International Journal of Human-Computer Studies 121 (2019), 142–159.Google ScholarCross Ref
- Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco Maria Donini, and Tommaso Di Noia. 2021. Elliot: a comprehensive and rigorous framework for reproducible recommender systems evaluation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2405–2414.Google ScholarDigital Library
- A. Borchers, J. Herlocker, J. Konstan, and J. Reidl. 1998. Ganging up on information overload. Computer 31, 4 (1998), 106–108. https://doi.org/10.1109/2.666847Google ScholarDigital Library
- Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. Advances in neural information processing systems 26 (2013), 2787–2795.Google Scholar
- Robin Burke. 2002. Hybrid recommender systems: Survey and experiments. User modeling and user-adapted interaction 12 (2002), 331–370.Google Scholar
- Joao Carreira and Andrew Zisserman. 2017. Quo vadis, action recognition? a new model and the kinetics dataset. In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 6299–6308.Google ScholarCross Ref
- Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, 2018. Universal sentence encoder. arXiv preprint arXiv:1803.11175 (2018).Google Scholar
- Li Chen, Marco De Gemmis, Alexander Felfernig, Pasquale Lops, Francesco Ricci, and Giovanni Semeraro. 2013. Human decision making and recommender systems. ACM Transactions on Interactive Intelligent Systems (TiiS) 3, 3 (2013), 1–7.Google ScholarDigital Library
- Janneth Chicaiza and Priscila Valdiviezo-Diaz. 2021. A comprehensive survey of knowledge graph-based recommender systems: Technologies, development, and contributions. Information 12, 6 (2021), 232.Google ScholarCross Ref
- Kenneth Ward Church. 2017. Word2Vec. Natural Language Engineering 23, 1 (2017), 155–162.Google ScholarCross Ref
- Yuanfei Dai, Shiping Wang, Neal N Xiong, and Wenzhong Guo. 2020. A survey on knowledge graph embedding: Approaches, applications and benchmarks. Electronics 9, 5 (2020), 750.Google ScholarCross Ref
- Marco de Gemmis, Pasquale Lops, Cataldo Musto, Fedelucio Narducci, and Giovanni Semeraro. 2015. Semantics-Aware Content-Based Recommender Systems. Springer US, Boston, MA, 119–159. https://doi.org/10.1007/978-1-4899-7637-6_4Google Scholar
- Yashar Deldjoo, Mehdi Elahi, Paolo Cremonesi, Franca Garzotto, Pietro Piazzolla, and Massimo Quadrana. 2016. Content-based video recommendation system based on stylistic visual features. Journal on Data Semantics 5, 2 (2016), 99–113.Google ScholarCross Ref
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. (2019).Google Scholar
- Charles-Emmanuel Dias, Vincent Guigue, and Patrick Gallinari. 2017. Text-based collaborative filtering for cold-start soothing and recommendation enrichment. In AISR2017.Google Scholar
- David Goldberg, David Nichols, Brian M. Oki, and Douglas Terry. 1992. Using Collaborative Filtering to Weave an Information Tapestry. Commun. ACM 35, 12 (dec 1992), 61–70. https://doi.org/10.1145/138859.138867Google ScholarDigital Library
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770–778.Google ScholarCross Ref
- Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval. 639–648.Google ScholarDigital Library
- Xuan Nhat Lam, Thuc Vu, Trong Duc Le, and Anh Duc Duong. 2008. Addressing Cold-Start Problem in Recommendation Systems(ICUIMC ’08). Association for Computing Machinery, New York, NY, USA, 208–211. https://doi.org/10.1145/1352793.1352837Google ScholarDigital Library
- Blerina Lika, Kostas Kolomvatsos, and Stathes Hadjiefthymiades. 2014. Facing the cold start problem in recommender systems. Expert systems with applications 41, 4 (2014), 2065–2073.Google Scholar
- Tianyang Lin, Yuxin Wang, Xiangyang Liu, and Xipeng Qiu. 2022. A survey of transformers. AI Open (2022).Google Scholar
- Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning Entity and Relation Embeddings for Knowledge Graph Completion. Proceedings of the AAAI Conference on Artificial Intelligence 29, 1 (Feb. 2015). https://doi.org/10.1609/aaai.v29i1.9491Google ScholarCross Ref
- Pasquale Lops, Marco De Gemmis, and Giovanni Semeraro. 2011. Content-based recommender systems: State of the art and trends. Recommender systems handbook (2011), 73–105.Google Scholar
- Pasquale Lops, Cataldo Musto, and Marco Polignano. 2022. Semantics-aware Content Representations for Reproducible Recommender Systems (SCoRe). In Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization. 354–356.Google ScholarDigital Library
- Pankaj Malhotra, Vishnu TV, Lovekesh Vig, Puneet Agarwal, and Gautam Shroff. 2017. TimeNet: Pre-trained deep recurrent neural network for time series classification. https://doi.org/10.48550/ARXIV.1706.08838Google Scholar
- Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. 2015. Image-based recommendations on styles and substitutes. In Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. 43–52.Google ScholarDigital Library
- Minju Park and Kyogu Lee. 2022. Exploiting Negative Preference in Content based Music Recommendation with Contrastive Learning. In Sixteenth ACM Conference on Recommender Systems. ACM. https://doi.org/10.1145/3523227.3546768Google ScholarDigital Library
- Marco Polignano, Cataldo Musto, Marco de Gemmis, Pasquale Lops, and Giovanni Semeraro. 2021. Together is Better: Hybrid Recommendations Combining Graph Embeddings and Contextualized Word Representations. In Fifteenth ACM Conference on Recommender Systems. 187–198.Google ScholarDigital Library
- Nils Reimers and Iryna Gurevych. 2019. Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084 (2019).Google Scholar
- Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).Google Scholar
- Giuseppe Spillo, Cataldo Musto, Marco De Gemmis, Pasquale Lops, and Giovanni Semeraro. 2022. Knowledge-aware Recommendations Based on Neuro-Symbolic Graph Embeddings and First-Order Logical Rules. In Proceedings of the 16th ACM Conference on Recommender Systems. 616–621.Google ScholarDigital Library
- Giuseppe Spillo, Cataldo Musto, Marco Polignano, Pasquale Lops, Marco de Gemmis, and Giovanni Semeraro. 2023. Combining GNNs and Sentence Encoders for Knowledge-aware Recommendations. In Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization (UMAP ’23), June 26–29, 2023, Limassol, Cyprus. ACM. https://doi.org/10.1145/3565472.3592965Google ScholarDigital Library
- Du Tran, Heng Wang, Lorenzo Torresani, Jamie Ray, Yann LeCun, and Manohar Paluri. 2018. A closer look at spatiotemporal convolutions for action recognition. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. 6450–6459.Google ScholarCross Ref
- Aaron Van den Oord, Sander Dieleman, and Benjamin Schrauwen. 2013. Deep content-based music recommendation. Advances in neural information processing systems 26 (2013).Google Scholar
- Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, and Partha Talukdar. 2019. Composition-based Multi-Relational Graph Convolutional Networks. https://doi.org/10.48550/ARXIV.1911.03082Google Scholar
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems. 5998–6008.Google Scholar
- Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017).Google Scholar
- Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, and Minyi Guo. 2019. Knowledge graph convolutional networks for recommender systems. In The world wide web conference. 3307–3313.Google Scholar
- Meihong Wang, Linling Qiu, and Xiaoli Wang. 2021. A Survey on Knowledge Graph Embeddings for Link Prediction. Symmetry 13, 3 (2021). https://doi.org/10.3390/sym13030485Google Scholar
- Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. 2019. Kgat: Knowledge graph attention network for recommendation. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. 950–958.Google ScholarDigital Library
- Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014. Knowledge Graph Embedding by Translating on Hyperplanes. Proceedings of the AAAI Conference on Artificial Intelligence 28, 1 (Jun. 2014). https://doi.org/10.1609/aaai.v28i1.8870Google ScholarCross Ref
- Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014. Knowledge graph embedding by translating on hyperplanes. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 28.Google ScholarCross Ref
- Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. 2020. A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32, 1 (2020), 4–24.Google ScholarCross Ref
- Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. Advances in neural information processing systems 33 (2020), 5812–5823.Google Scholar
- Si Zhang, Hanghang Tong, Jiejun Xu, and Ross Maciejewski. 2019. Graph convolutional networks: a comprehensive review. Computational Social Networks 6, 1 (2019), 1–23.Google ScholarCross Ref
- Ding Zou, Wei Wei, Xian-Ling Mao, Ziyang Wang, Minghui Qiu, Feida Zhu, and Xin Cao. 2022. Multi-level cross-view contrastive learning for knowledge-aware recommender system. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1358–1368.Google ScholarDigital Library
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