Enrico
Palumbo

I'm Enrico Palumbo and I am research scientist (PhD). I currently work for Amazon Alexa in the Natural Language Understanding field.

My research interests lie at the intersection among Recommender Systems, Natural Language Understanding and Semantics on topics such as:

• knowledge graph embeddings for recommender systems
• sequence-aware recommender systems
• machine learning approaches to entity matching
• measures of entity relatedness for entity linking
• sentiment analysis on social media data

In my free time, I play the guitar and sing in a band called Alephant.

2020

• Alexa: is a voice-first digital assistant created by Amazon. I work in the research and development of the Natural Language Understanding model.

2019

• Tinderbook: is a book recommendation application based on my knowledge graph embeddings algorithm entity2rec. It provides book suggestions given a single book that you like!

2018

• CEDUS: is a smart city project that generates a set of software components to effectively collect, integrate and visualize city data to support decision making processes. The team is in charge of the impact assessment and go-to-market activities, i.e. of the evaluation of the societal, economic and environmental impacts of the CEDUS technologies.

2017

• PasTime: creation and development of a recommender system that provides personalized suggestions of places and events in a city. We learn to recommend tourist paths by feeding sequences of tourist check-ins in Points-of-Interests to a Recurrent Neural Network.

2016

• 3cixty: creation and development of a machine learning based approach to automatically identify duplicates during the integration of places and events data from heterogeneous sources for the creation of city knowledge bases.

2015 - 2017

• Snowball: creation and development of a data-driven Decision Support System based on Multi-Criteria Decision Making to support crisis managers in dealing with cascading effects

• entity2rec: provides top-N item recommendations from knowledge graphs.
• entity2vec: creates property-specific embeddings of knowledge graphs applying node2vec on property-specific subgraphs
• Stacked Threshold Based Entity Matching (STEM): performs entity matching by stacking a supervised learner on top of an ensemble of threshold-based classifiers. Stacking enhances both precision and recall at the same time.
• SentiMe++: stacking on top of an ensemble of state-of-the-art sentiment classifiers to enhance performance.

2020

Palumbo, Enrico, et al. Semantic Diversity for Natural Language Understanding Evaluation in Dialog Systems. Proceedings of the 28th International Conference on Computational Linguistics: Industry Track, 2020

Palumbo, Enrico. Knowledge Graph Embeddings for Recommender Systems (PhD thesis dissertation)

Palumbo, Enrico. Knowledge Graph Embeddings for Recommender Systems (PhD thesis dissertation - main contributions)

Palumbo, Enrico, et al. entity2rec: Property-specific knowledge graph embeddings for item recommendation. Expert Systems with Applications (2020): 113235.

2019

Palumbo, Enrico, et al. Tinderbook: Fall in Love with Culture. European Semantic Web Conference. Springer, Cham, 2019.

2018

Monti, Diego, et al. Sequeval: An offline evaluation framework for sequence-based recommender systems. Information 10.5 (2019): 174.

Monti, Diego, et al. Semantic Trails of City Explorations: How Do We Live a City. arXiv preprint arXiv:1812.04367 (2018).

Monti, Diego, et al. An ensemble approach of recurrent neural networks using pre-trained embeddings for playlist completion." Proceedings of the ACM Recommender Systems Challenge 2018. 2018. 1-6.

Giammusso Sara, Guerriero Mario, Lisena Pasquale, Palumbo Enrico & Troncy Raphaël. Predicting The Emotion of Playlists Using Track Lyrics. In 19th International Society for Music Information Retrieval Conference (ISMIR), Late-Breaking Demo Track, Paris, France, September 23-27, 2018.

Monti D., Palumbo E., Rizzo G., Lisena P., Troncy R., Fell M., Cabrio E., Morisio M. An Ensemble Approach of Recurrent Neural Networks using Pre-Trained Embeddings for Playlist Completion RecSys Challenge, Vancouver, 2018

Carducci G., Rizzo G., Monti D., Palumbo E., Morisio M. TwitPersonality: Computing Personality Traits from Tweets Using Word Embeddings and Supervised Learning. Information. 2018 May 18;9(5):127.

Caroleo B.R.D., Palumbo E., Osella M. Lotito A., Rizzo G., Ferro E.G., Attanasio A., Chiusano S.A., Zuccaro, G., Leone M., De Gregorio D. A Knowledge-Based Multi-Criteria Decision Support System Encompassing Cascading Effects For Disaster Management, International Journal of Information Technology & Decision Making, 2018

Palumbo E., Rizzo G., Troncy R., Baralis E., Osella M., Ferro E. Translational Models for Item Recommendation, ESWC2018, Heraklion, Greece, 2018

Palumbo E., Rizzo G., Troncy R., Baralis E., Osella M., Ferro E. Knowledge Graph Embeddings with node2vec for Item Recommendation, Poster & Demo sessions, ESWC2018, Heraklion, Greece, 2018 (best poster paper award)

Palumbo E., Rizzo G., Troncy R., Baralis E., Osella M., Ferro E. An Empirical Comparison of Knowledge Graph Embeddings for Item Recommendation , Deep Learning for Knowledge Graphs and Semantics Workshop at ESWC2018, Heraklion, Greece, 2018

Palumbo E., Rizzo G., Troncy R. STEM: Stacked Threshold-based Entity Matching for Knowledge Base Generation , Semantic Web Journal, Special Issue on Machine Learning for Knowledge Base Generation, 2018

2017

Palumbo E., Rizzo G., Troncy R., Baralis E. Predicting Your Next Stop-over from Location-based Social Network Data with Recurrent Neural Networks , RecTour Workshop at RecSys2017, Como, Italy, 2017

Palumbo E., Rizzo G., Troncy R. entity2rec: Learning User-Item Relatedness from Knowledge Graphs for Top-N Item Recommendation. In 11th ACM Conference on Recommender Systems (RecSys) , Como, Italy, 2017

Troncy, Raphaël, et al. 3cixty: Building Comprehensive Knowledge Bases For City Exploration." Journal of Web Semantics (2017).

Palumbo E., Sygkounas E., Troncy R., Rizzo G. (2017) SentiME++ at SemEval-2017 Task 4A: Stacking State-of-the-Art Classifiers to Enhance Sentiment Classification. In International Workshop on Semantic Evaluation (SemEval), Vancouver, Canada

2016

Palumbo, Enrico, Giuseppe Rizzo, and Raphael Troncy. "An Ensemble Approach to Financial Entity Matching for the FEIII 2016 Challenge." Proceedings of the Second International Workshop on Data Science for Macro-Modeling (DSMM'16). No. 14. ACM, 2016.

Palumbo, Enrico, et al. "ICDSST 2016 on Decision Support Systems Addressing Sustainability & Societal Challenges SnowBall DSS: an ensemble Multi-Criteria Decision Support System encompassing cascading effects for disaster management." (2016).

2015

Palumbo, Enrico, and Walter Allasia. "Semantic Similarity Between Images: A Novel Approach Based on a Complex Network of Free Word Associations." International Conference on Similarity Search and Applications. Springer, Cham, 2015.

Allasia, Walter, and Enrico Palumbo. "A complex network model of semantic memory impairments." Multimedia & Expo Workshops (ICMEW), 2015 IEEE International Conference on. IEEE, 2015.