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Paolo Cremonesi
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- affiliation: Politecnico di Milano, Italy
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2020 – today
- 2024
- [c148]Andrea Pasin, Maurizio Ferrari Dacrema, Paolo Cremonesi, Nicola Ferro:
Overview of QuantumCLEF 2024: The Quantum Computing Challenge for Information Retrieval and Recommender Systems at CLEF. CLEF (2) 2024: 260-282 - [c147]Andrea Pasin, Maurizio Ferrari Dacrema, Paolo Cremonesi, Nicola Ferro:
QuantumCLEF 2024: Overview of the Quantum Computing Challenge for Information Retrieval and Recommender Systems at CLEF. CLEF (Working Notes) 2024: 3032-3053 - [c146]Maurizio Ferrari Dacrema
, Andrea Pasin, Paolo Cremonesi, Nicola Ferro:
Quantum Computing for Information Retrieval and Recommender Systems. ECIR (5) 2024: 358-362 - [c145]Andrea Pasin, Maurizio Ferrari Dacrema
, Paolo Cremonesi, Nicola Ferro:
QuantumCLEF - Quantum Computing at CLEF. ECIR (5) 2024: 482-489 - [c144]Nicola Cecere, Andrea Pisani, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Leveraging Semantic Embeddings of User Reviews with Off-the-Shelf LLMs for Recommender Systems. IIR 2024: 87-90 - [c143]Andrea Pisani, Nicola Cecere, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Pre-Trained LLM Embeddings of Product Reviews for Recommendation. IIR 2024: 91-94 - [c142]Costantino Carugno, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Adaptive Learning for Quantum Linear Regression. QCE 2024: 1595-1599 - [c141]Maurizio Ferrari Dacrema
, Andrea Pasin
, Paolo Cremonesi
, Nicola Ferro
:
Using and Evaluating Quantum Computing for Information Retrieval and Recommender Systems. SIGIR 2024: 3017-3020 - [i30]Costantino Carugno, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Adaptive Learning for Quantum Linear Regression. CoRR abs/2408.02833 (2024) - [i29]Simone Foderà, Gloria Turati, Riccardo Nembrini, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Reinforcement Learning for Variational Quantum Circuits Design. CoRR abs/2409.05475 (2024) - 2023
- [j34]Luca Benedetto
, Paolo Cremonesi
, Andrew Caines
, Paula Buttery
, Andrea Cappelli
, Andrea Giussani
, Roberto Turrin
:
A Survey on Recent Approaches to Question Difficulty Estimation from Text. ACM Comput. Surv. 55(9): 178:1-178:37 (2023) - [j33]Maurizio Ferrari Dacrema, Pablo Castells, Justin Basilico, Paolo Cremonesi:
Report on the Workshop on Learning and Evaluating Recommendations with Impressions (LERI) at RecSys 2023. SIGIR Forum 57(2): 19:1-19:8 (2023) - [c140]Andrea Pasin, Maurizio Ferrari Dacrema
, Paolo Cremonesi, Nicola Ferro:
qCLEF: A Proposal to Evaluate Quantum Annealing for Information Retrieval and Recommender Systems. CLEF 2023: 97-108 - [c139]Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Pablo Castells, Paolo Cremonesi:
Impressions in Recommender Systems: Present and Future. IIR 2023: 97-104 - [c138]Riccardo Pellini, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Towards Improved QUBO Formulations of IR Tasks for Quantum Annealers. IIR 2023: 137-142 - [c137]Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Pablo Castells, Paolo Cremonesi:
Characterizing Impression-Aware Recommender Systems. LERI@RecSys 2023: 22-33 - [c136]Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Pablo Castells, Paolo Cremonesi:
Incorporating Impressions to Graph-Based Recommenders. LERI@RecSys 2023: 62-67 - [c135]Gloria Turati, Maurizio Ferrari Dacrema
, Paolo Cremonesi:
Benchmarking Adaptative Variational Quantum Algorithms on QUBO Instances. QCE 2023: 407-413 - [c134]Maurizio Ferrari Dacrema
, Pablo Castells
, Justin Basilico
, Paolo Cremonesi
:
Workshop on Learning and Evaluating Recommendations with Impressions (LERI). RecSys 2023: 1248-1251 - [c133]Alessandro Barenghi
, Paolo Cremonesi
, Gerardo Pelosi
:
Quantum Computing Research Lines in the Italian Center for Supercomputing. SAMOS 2023: 423-434 - [e4]Maurizio Ferrari Dacrema, Pablo Castells, Justin Basilico, Paolo Cremonesi:
Proceedings of the Workshop on Learning and Evaluating Recommendations with Impressions co-located with the 17th ACM Conference on Recommender Systems (RecSys 2023), Singapore, September 19th, 2023. CEUR Workshop Proceedings 3590, CEUR-WS.org 2023 [contents] - [i28]Gloria Turati, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Benchmarking Adaptative Variational Quantum Algorithms on QUBO Instances. CoRR abs/2308.01789 (2023) - [i27]Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Pablo Castells, Paolo Cremonesi:
Impression-Aware Recommender Systems. CoRR abs/2308.07857 (2023) - 2022
- [j32]Cesare Bernardis
, Maurizio Ferrari Dacrema
, Fernando Benjamín Pérez Maurera
, Massimo Quadrana, Mario Scriminaci, Paolo Cremonesi
:
From Data Analysis to Intent-Based Recommendation: An Industrial Case Study in the Video Domain. IEEE Access 10: 14779-14796 (2022) - [j31]Maurizio Ferrari Dacrema
, Nicolò Felicioni
, Paolo Cremonesi:
Offline Evaluation of Recommender Systems in a User Interface With Multiple Carousels. Frontiers Big Data 5: 910030 (2022) - [j30]Edoardo D'Amico, Giovanni Gabbolini, Cesare Bernardis
, Paolo Cremonesi:
Analyzing and improving stability of matrix factorization for recommender systems. J. Intell. Inf. Syst. 58(2): 255-285 (2022) - [j29]João Vinagre
, Alípio Mário Jorge
, Marie Al-Ghossein, Albert Bifet
, Paolo Cremonesi:
Preface to the special issue on dynamic recommender systems and user models. User Model. User Adapt. Interact. 32(4): 503-507 (2022) - [j28]Cesare Bernardis
, Paolo Cremonesi:
NFC: a deep and hybrid item-based model for item cold-start recommendation. User Model. User Adapt. Interact. 32(4): 747-780 (2022) - [c132]Fernando Benjamín Pérez Maurera
, Maurizio Ferrari Dacrema
, Paolo Cremonesi
:
An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering. ECIR (1) 2022: 671-685 - [c131]Maurizio Ferrari Dacrema, Nicolò Felicioni, Paolo Cremonesi:
Evaluating Recommendations in a User Interface With Multiple Carousels. IIR 2022 - [c130]Maurizio Ferrari Dacrema, Fabio Moroni, Riccardo Nembrini, Nicola Ferro, Guglielmo Faggioli, Paolo Cremonesi:
Feature Selection via Quantum Annealers for Ranking and Classification Tasks. IIR 2022 - [c129]Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Replication of Collaborative Filtering Generative Adversarial Networks on Recommender Systems. IIR 2022 - [c128]Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Replication of Recommender Systems with Impressions. IIR 2022 - [c127]Nicolò Felicioni, Maurizio Ferrari Dacrema, Marcello Restelli, Paolo Cremonesi:
Off-Policy Evaluation with Deficient Support Using Side Information. NeurIPS 2022 - [c126]Pietro Chiavassa, Andrea Marchesin
, Ignazio Pedone, Maurizio Ferrari Dacrema
, Paolo Cremonesi:
Virtual Network Function Embedding with Quantum Annealing. QCE 2022: 282-291 - [c125]Gloria Turati, Maurizio Ferrari Dacrema
, Paolo Cremonesi:
Feature Selection for Classification with QAOA. QCE 2022: 782-785 - [c124]Riccardo Nembrini, Costantino Carugno, Maurizio Ferrari Dacrema
, Paolo Cremonesi:
Towards Recommender Systems with Community Detection and Quantum Computing. RecSys 2022: 579-585 - [c123]Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema
, Paolo Cremonesi:
Towards the Evaluation of Recommender Systems with Impressions. RecSys 2022: 610-615 - [c122]Ervin Dervishaj
, Paolo Cremonesi:
GAN-based matrix factorization for recommender systems. SAC 2022: 1373-1381 - [c121]Matteo Montanari, Cesare Bernardis, Paolo Cremonesi:
On the impact of data sampling on hyper-parameter optimisation of recommendation algorithms. SAC 2022: 1399-1402 - [c120]Maurizio Ferrari Dacrema
, Fabio Moroni, Riccardo Nembrini, Nicola Ferro, Guglielmo Faggioli
, Paolo Cremonesi:
Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers. SIGIR 2022: 2814-2824 - [c119]Federico Rios, Paolo Rizzo, Francesco Puddu, Federico Romeo, Andrea Lentini, Giuseppe Asaro, Filippo Rescalli, Cristiana Bolchini, Paolo Cremonesi:
Recommending Relevant Papers to Conference Participants: a Deep Learning Driven Content-based Approach. UMAP (Adjunct Publication) 2022: 52-57 - [e3]Gabriella Pasi, Paolo Cremonesi, Salvatore Orlando, Markus Zanker, David Massimo, Gloria Turati:
Proceedings of the 12th Italian Information Retrieval Workshop 2022, Milan, Italy, June 29-30, 2022. CEUR Workshop Proceedings 3177, CEUR-WS.org 2022 [contents] - [r3]Dietmar Jannach, Massimo Quadrana, Paolo Cremonesi:
Session-Based Recommender Systems. Recommender Systems Handbook 2022: 301-334 - [r2]Maurizio Ferrari Dacrema
, Iván Cantador, Ignacio Fernández-Tobías, Shlomo Berkovsky
, Paolo Cremonesi:
Design and Evaluation of Cross-Domain Recommender Systems. Recommender Systems Handbook 2022: 485-516 - [d2]Fernando Benjamín Pérez Maurera
, Maurizio Ferrari Dacrema
, Paolo Cremonesi
:
An Evaluation of Generative Adversarial Networks for Collaborative Filtering - Supplemental Material. Version v1.0.1-ecir-2022-camera-ready-prep. Zenodo, 2022 [all versions] - [d1]Fernando Benjamín Pérez Maurera
, Maurizio Ferrari Dacrema
, Paolo Cremonesi
:
An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering - Supplemental Material. Version v1.1.1-ecir-2022-camera-ready. Zenodo, 2022 [all versions] - [i26]Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Paolo Cremonesi:
An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering. CoRR abs/2201.01815 (2022) - [i25]Ervin Dervishaj, Paolo Cremonesi:
GAN-based Matrix Factorization for Recommender Systems. CoRR abs/2201.08042 (2022) - [i24]Maurizio Ferrari Dacrema, Fabio Moroni, Riccardo Nembrini, Nicola Ferro, Guglielmo Faggioli
, Paolo Cremonesi:
Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers. CoRR abs/2205.04346 (2022) - [i23]Gloria Turati, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Feature Selection for Classification with QAOA. CoRR abs/2211.02861 (2022) - 2021
- [j27]Paolo Cremonesi, Dietmar Jannach:
Progress in Recommender Systems Research: Crisis? What Crisis? AI Mag. 42(3): 43-54 (2021) - [j26]Yashar Deldjoo
, Markus Schedl, Paolo Cremonesi, Gabriella Pasi:
Recommender Systems Leveraging Multimedia Content. ACM Comput. Surv. 53(5): 106:1-106:38 (2021) - [j25]Riccardo Nembrini
, Maurizio Ferrari Dacrema
, Paolo Cremonesi
:
Feature Selection for Recommender Systems with Quantum Computing. Entropy 23(8): 970 (2021) - [j24]Stefano Cereda, Stefano Valladares, Paolo Cremonesi, Stefano Doni:
CGPTuner: a Contextual Gaussian Process Bandit Approach for the Automatic Tuning of IT Configurations Under Varying Workload Conditions. Proc. VLDB Endow. 14(8): 1401-1413 (2021) - [j23]Maurizio Ferrari Dacrema
, Simone Boglio, Paolo Cremonesi, Dietmar Jannach:
A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research. ACM Trans. Inf. Syst. 39(2): 20:1-20:49 (2021) - [c118]Luca Benedetto, Giovanni Aradelli, Paolo Cremonesi, Andrea Cappelli, Andrea Giussani, Roberto Turrin:
On the application of Transformers for estimating the difficulty of Multiple-Choice Questions from text. BEA@EACL 2021: 147-157 - [c117]Nicolò Felicioni, Maurizio Ferrari Dacrema, Fernando Benjamín Pérez Maurera, Paolo Cremonesi:
Measuring the Ranking Quality of Recommendations in a Two-Dimensional Carousel Setting. IIR 2021 - [c116]Ekaterina Loginova, Luca Benedetto, Dries F. Benoit, Paolo Cremonesi:
Towards the Application of Calibrated Transformers to the Unsupervised Estimation of Question Difficulty from Text. RANLP 2021: 846-855 - [c115]Cesare Bernardis, Paolo Cremonesi:
Eigenvalue Perturbation for Item-based Recommender Systems. RecSys 2021: 656-660 - [c114]Maurizio Ferrari Dacrema
, Nicolò Felicioni
, Paolo Cremonesi:
Optimizing the Selection of Recommendation Carousels with Quantum Computing. RecSys 2021: 691-696 - [c113]Giovanni Gabbolini, Edoardo D'Amico, Cesare Bernardis, Paolo Cremonesi:
On the instability of embeddings for recommender systems: the case of matrix factorization. SAC 2021: 1363-1370 - [c112]Nicolò Felicioni
, Maurizio Ferrari Dacrema
, Paolo Cremonesi:
Measuring the User Satisfaction in a Recommendation Interface with Multiple Carousels. IMX 2021: 212-217 - [c111]Nicolò Felicioni
, Maurizio Ferrari Dacrema
, Paolo Cremonesi:
A Methodology for the Offline Evaluation of Recommender Systems in a User Interface with Multiple Carousels. UMAP (Adjunct Publication) 2021: 10-15 - [i22]Giovanni Gabbolini, Edoardo D'Amico, Cesare Bernardis, Paolo Cremonesi:
On the instability of embeddings for recommender systems: the case of Matrix Factorization. CoRR abs/2104.05796 (2021) - [i21]Nicolò Felicioni, Maurizio Ferrari Dacrema, Paolo Cremonesi:
A Methodology for the Offline Evaluation of Recommender Systems in a User Interface with Multiple Carousels. CoRR abs/2105.06275 (2021) - [i20]Nicolò Felicioni, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Measuring the User Satisfaction in a Recommendation Interface with Multiple Carousels. CoRR abs/2105.07062 (2021) - [i19]Riccardo Nembrini, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Feature Selection for Recommender Systems with Quantum Computing. CoRR abs/2110.05089 (2021) - 2020
- [c110]Luca Benedetto
, Andrea Cappelli, Roberto Turrin, Paolo Cremonesi
:
Introducing a Framework to Assess Newly Created Questions with Natural Language Processing. AIED (1) 2020: 43-54 - [c109]Gabriele Prato, Federico Sallemi, Paolo Cremonesi, Mario Scriminaci, Stefan Gudmundsson, Silvio Palumbo:
Outfit Completion and Clothes Recommendation. CHI Extended Abstracts 2020: 1-7 - [c108]Maurizio Ferrari Dacrema
, Federico Parroni, Paolo Cremonesi, Dietmar Jannach:
Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems. CIKM 2020: 355-363 - [c107]Fernando Benjamín Pérez Maurera
, Maurizio Ferrari Dacrema
, Lorenzo Saule, Mario Scriminaci, Paolo Cremonesi:
ContentWise Impressions: An Industrial Dataset with Impressions Included. CIKM 2020: 3093-3100 - [c106]Maurizio Ferrari Dacrema
, Paolo Cremonesi, Dietmar Jannach:
Methodological Issues in Recommender Systems Research (Extended Abstract). IJCAI 2020: 4706-4710 - [c105]Luca Benedetto
, Andrea Cappelli, Roberto Turrin, Paolo Cremonesi:
R2DE: a NLP approach to estimating IRT parameters of newly generated questions. LAK 2020: 412-421 - [c104]Stefano Cereda
, Gianluca Palermo, Paolo Cremonesi, Stefano Doni:
A Collaborative Filtering Approach for the Automatic Tuning of Compiler Optimisations. LCTES 2020: 15-25 - [i18]Luca Benedetto, Andrea Cappelli, Roberto Turrin, Paolo Cremonesi:
R2DE: a NLP approach to estimating IRT parameters of newly generated questions. CoRR abs/2001.07569 (2020) - [i17]Luca Benedetto, Andrea Cappelli, Roberto Turrin, Paolo Cremonesi:
Introducing a framework to assess newly created questions with Natural Language Processing. CoRR abs/2004.13530 (2020) - [i16]Stefano Cereda, Gianluca Palermo, Paolo Cremonesi, Stefano Doni:
A Collaborative Filtering Approach for the Automatic Tuning of Compiler Optimisations. CoRR abs/2005.04092 (2020) - [i15]Maurizio Ferrari Dacrema
, Federico Parroni, Paolo Cremonesi, Dietmar Jannach:
Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems. CoRR abs/2007.11893 (2020) - [i14]Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema
, Lorenzo Saule, Mario Scriminaci, Paolo Cremonesi:
ContentWise Impressions: An industrial dataset with impressions included. CoRR abs/2008.01212 (2020)
2010 – 2019
- 2019
- [j22]Yashar Deldjoo
, Maurizio Ferrari Dacrema
, Mihai Gabriel Constantin
, Hamid Eghbal-zadeh
, Stefano Cereda
, Markus Schedl
, Bogdan Ionescu, Paolo Cremonesi
:
Movie genome: alleviating new item cold start in movie recommendation. User Model. User Adapt. Interact. 29(2): 291-343 (2019) - [c103]Luca Benedetto
, Paolo Cremonesi:
Rexy, A Configurable Application for Building Virtual Teaching Assistants. INTERACT (2) 2019: 233-241 - [c102]Paolo Cremonesi:
A pragmatic and industry-aware approach toward the design of on-line recommender systems. ORSUM@RecSys 2019: 1 - [c101]Luca Luciano Costanzo, Yashar Deldjoo, Maurizio Ferrari Dacrema, Markus Schedl, Paolo Cremonesi:
Towards Evaluating User Profiling Methods Based on Explicit Ratings on Item Features. IntRS@RecSys 2019: 72-76 - [c100]Maurizio Ferrari Dacrema
, Paolo Cremonesi, Dietmar Jannach:
Are we really making much progress? A worrying analysis of recent neural recommendation approaches. RecSys 2019: 101-109 - [c99]Cesare Bernardis, Maurizio Ferrari Dacrema
, Paolo Cremonesi:
Estimating Confidence of Individual User Predictions in Item-based Recommender Systems. UMAP 2019: 149-156 - [c98]Massimo Quadrana, Dietmar Jannach, Paolo Cremonesi:
Tutorial: Sequence-Aware Recommender Systems. WWW (Companion Volume) 2019: 1316 - [i13]Luca Benedetto
, Paolo Cremonesi, Manuel Parenti:
A Virtual Teaching Assistant for Personalized Learning. CoRR abs/1902.09289 (2019) - [i12]Maurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach:
Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches. CoRR abs/1907.06902 (2019) - [i11]Luca Luciano Costanzo, Yashar Deldjoo, Maurizio Ferrari Dacrema, Markus Schedl, Paolo Cremonesi:
Towards Evaluating User Profiling Methods Based on Explicit Ratings on Item Features. CoRR abs/1908.11055 (2019) - [i10]Maurizio Ferrari Dacrema
, Simone Boglio, Paolo Cremonesi, Dietmar Jannach:
A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research. CoRR abs/1911.07698 (2019) - 2018
- [j21]Massimo Quadrana
, Paolo Cremonesi
, Dietmar Jannach:
Sequence-Aware Recommender Systems. ACM Comput. Surv. 51(4): 66:1-66:36 (2018) - [j20]Yashar Deldjoo
, Mehdi Elahi
, Massimo Quadrana, Paolo Cremonesi
:
Using visual features based on MPEG-7 and deep learning for movie recommendation. Int. J. Multim. Inf. Retr. 7(4): 207-219 (2018) - [c97]Luca Benedetto, Paolo Cremonesi, Manuel Parenti:
A Virtual Teaching Assistant for Personalized Learning. CIKM Workshops 2018 - [c96]Yashar Deldjoo, Markus Schedl, Paolo Cremonesi, Gabriella Pasi:
Content-Based Multimedia Recommendation Systems: Definition and Application Domains. IIR 2018 - [c95]Yashar Deldjoo
, Mihai Gabriel Constantin
, Bogdan Ionescu, Markus Schedl, Paolo Cremonesi
:
MMTF-14K: a multifaceted movie trailer feature dataset for recommendation and retrieval. MMSys 2018: 450-455 - [c94]Maurizio Ferrari Dacrema, Alberto Gasparin, Paolo Cremonesi:
Deriving Item Features Relevance from Collaborative Domain Knowledge. KaRS@RecSys 2018: 1-4 - [c93]Yashar Deldjoo
, Mihai Gabriel Constantin
, Hamid Eghbal-Zadeh, Bogdan Ionescu, Markus Schedl, Paolo Cremonesi
:
Audio-visual encoding of multimedia content for enhancing movie recommendations. RecSys 2018: 455-459 - [c92]Massimo Quadrana, Paolo Cremonesi
:
Sequence-aware recommendation. RecSys 2018: 539-540 - [c91]Massimo Quadrana, Paolo Cremonesi
, Dietmar Jannach:
Sequence-aware Recommender Systems. UMAP 2018: 373-374 - [p4]Paolo Cremonesi, Franca Garzotto, Maurizio Ferrari Dacrema
:
User Preference Sources: Explicit vs. Implicit Feedback. Collaborative Recommendations 2018: 233-252 - [i9]Paolo Cremonesi, Chiara Francalanci, Alessandro Poli, Roberto Pagano, Luca Mazzoni, Alberto Maggioni, Mehdi Elahi:
Social Network based Short-Term Stock Trading System. CoRR abs/1801.05295 (2018) - [i8]Massimo Quadrana, Paolo Cremonesi, Dietmar Jannach:
Sequence-Aware Recommender Systems. CoRR abs/1802.08452 (2018) - [i7]Cesare Bernardis, Maurizio Ferrari Dacrema
, Paolo Cremonesi:
A novel graph-based model for hybrid recommendations in cold-start scenarios. CoRR abs/1808.10664 (2018) - [i6]Maurizio Ferrari Dacrema
, Paolo Cremonesi:
Eigenvalue analogy for confidence estimation in item-based recommender systems. CoRR abs/1809.02052 (2018) - [i5]Maurizio Ferrari Dacrema
, Alberto Gasparin, Paolo Cremonesi:
Deriving item features relevance from collaborative domain knowledge. CoRR abs/1811.01905 (2018) - 2017
- [j19]Paolo Cremonesi
, Mehdi Elahi
, Franca Garzotto:
User interface patterns in recommendation-empowered content intensive multimedia applications. Multim. Tools Appl. 76(4): 5275-5309 (2017) - [c90]Yashar Deldjoo
, Paolo Cremonesi
, Markus Schedl, Massimo Quadrana:
The effect of different video summarization models on the quality of video recommendation based on low-level visual features. CBMI 2017: 20:1-20:6 - [c89]Yashar Deldjoo, Cristina Frà, Massimo Valla, Paolo Cremonesi:
Letting Users Assist What to Watch: An Interactive Query-by-Example Movie Recommendation System. IIR 2017: 63-66 - [c88]Stefano Cereda, Leonardo Cella, Paolo Cremonesi:
Estimate Features Relevance for Groups of Users. IIR 2017: 80-83 - [c87]Leonardo Cella, Romaric Gaudel, Paolo Cremonesi:
Kernalized Collaborative Contextual Bandits. RecSys Posters 2017 - [c86]Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi
, Paolo Cremonesi
:
Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks. RecSys 2017: 130-137 - [c85]Mehdi Elahi
, Yashar Deldjoo
, Farshad Bakhshandegan Moghaddam, Leonardo Cella, Stefano Cereda
, Paolo Cremonesi
:
Exploring the Semantic Gap for Movie Recommendations. RecSys 2017: 326-330 - [c84]Andreu Vall, Massimo Quadrana, Markus Schedl, Gerhard Widmer, Paolo Cremonesi:
The Importance of Song Context in Music Playlists. RecSys Posters 2017 - [c83]Leonardo Cella, Stefano Cereda
, Massimo Quadrana, Paolo Cremonesi
:
Deriving Item Features Relevance from Past User Interactions. UMAP 2017: 275-279 - [e2]Paolo Cremonesi, Francesco Ricci, Shlomo Berkovsky, Alexander Tuzhilin:
Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys 2017, Como, Italy, August 27-31, 2017. ACM 2017, ISBN 978-1-4503-4652-8 [contents] - [i4]