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- affiliation: Politecnico di Milano, Italy
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2020 – today
- 2023
[j33]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)- 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 - [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 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]Roberto Pagano, Massimo Quadrana, Mehdi Elahi, Paolo Cremonesi:
Toward Active Learning in Cross-domain Recommender Systems. CoRR abs/1701.02021 (2017) - [i3]Yashar Deldjoo, Massimo Quadrana, Mehdi Elahi, Paolo Cremonesi:
Using Mise-En-Scène Visual Features based on MPEG-7 and Deep Learning for Movie Recommendation. CoRR abs/1704.06109 (2017) - [i2]Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, Paolo Cremonesi:
Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks. CoRR abs/1706.04148 (2017) - 2016
- [j18]Giuliano Casale, Andrea Sansottera, Paolo Cremonesi:
Compact Markov-modulated models for multiclass trace fitting. Eur. J. Oper. Res. 255(3): 822-833 (2016) - [j17]Yashar Deldjoo, Mehdi Elahi, Paolo Cremonesi, Franca Garzotto, Pietro Piazzolla, Massimo Quadrana:
Content-Based Video Recommendation System Based on Stylistic Visual Features. J. Data Semant. 5(2): 99-113 (2016) - [j16]Paolo Cremonesi, Alan Said, Domonkos Tikk, Michelle X. Zhou:
Introduction to the Special Issue on Recommender System Benchmarking. ACM Trans. Intell. Syst. Technol. 7(3): 38:1-38:4 (2016) - [c82]Paolo Cremonesi, Antonella Di Rienzo, Franca Garzotto, Luigi Oliveto, Pietro Piazzolla:
Smart Lighting for Fashion Store Windows. AVI 2016: 13-20 - [c81]Antonella Di Rienzo, Paolo Tagliaferri, Francesco Arenella, Franca Garzotto, Cristina Frà, Paolo Cremonesi, Massimo Valla:
Bridging Physical Space and Digital Landscape to Drive Retail Innovation. AVI 2016: 356-357 - [c80]Yashar Deldjoo, Mehdi Elahi, Paolo Cremonesi, Franca Garzotto, Pietro Piazzolla:
Recommending Movies Based on Mise-en-Scene Design. CHI Extended Abstracts 2016: 1540-1547 - [c79]Paolo Cremonesi, Antonella Di Rienzo, Franca Garzotto, Luigi Oliveto, Pietro Piazzolla:
Dynamic and Interactive Lighting for Fashion Store Windows. CHI Extended Abstracts 2016: 2257-2263 - [c78]Paolo Cremonesi, Franca Garzotto, Marco Gribaudo, Pietro Piazzolla, Mauro Iacono:
vMannequin: A Fashion Store Concept Design Tool. ECMS 2016: 527-533 - [c77]Yashar Deldjoo, Mehdi Elahi, Paolo Cremonesi, Farshad Bakhshandegan Moghaddam, Andrea Luigi Edoardo Caielli:
How to Combine Visual Features with Tags to Improve Movie Recommendation Accuracy? EC-Web 2016: 34-45 - [c76]Mattia Brusamento, Roberto Pagano, Martha A. Larson, Paolo Cremonesi:
Explicit Elimination of Similarity Blocking for Session-based Recommendation. RecSys Posters 2016 - [c75]Tommaso Carpi, Marco Edemanti, Ervin Kamberoski, Elena Sacchi, Paolo Cremonesi, Roberto Pagano, Massimo Quadrana:
Multi-stack ensemble for job recommendation. RecSys Challenge 2016: 8:1-8:4 - [c74]Yashar Deldjoo, Mehdi Elahi, Paolo Cremonesi:
Using Visual Features and Latent Factors for Movie Recommendation. CBRecSys@RecSys 2016: 15-18 - [c73]Tamas Motajcsek, Jean-Yves Le Moine, Martha A. Larson, Daniel Kohlsdorf, Andreas Lommatzsch, Domonkos Tikk, Omar Alonso, Paolo Cremonesi, Andrew M. Demetriou, Kristaps Dobrajs, Franca Garzotto, Ayse Göker, Frank Hopfgartner, Davide Malagoli, Thuy Ngoc Nguyen, Jasminko Novak, Francesco Ricci, Mario Scriminaci, Marko Tkalcic, Anna Zacchi:
Algorithms Aside: Recommendation As The Lens Of Life. RecSys 2016: 215-219 - [c72]Roberto Pagano, Paolo Cremonesi, Martha A. Larson, Balázs Hidasi, Domonkos Tikk, Alexandros Karatzoglou, Massimo Quadrana:
The Contextual Turn: from Context-Aware to Context-Driven Recommender Systems. RecSys 2016: 249-252 - [i1]Yashar Deldjoo, Shengping Zhang, Bahman Zanj, Paolo Cremonesi, Matteo Matteucci:
Sparse vs. Non-sparse: Which One Is Better for Practical Visual Tracking? CoRR abs/1608.00168 (2016) - 2015
- [c71]Paolo Cremonesi, Mehdi Elahi, Franca Garzotto:
Interaction Design Patterns in Recommender Systems. CHItaly 2015: 66-73 - [c70]Yashar Deldjoo, Mehdi Elahi, Massimo Quadrana, Paolo Cremonesi, Franca Garzotto:
Toward Effective Movie Recommendations Based on Mise-en-Scène Film Styles. CHItaly 2015: 162-165 - [c69]Paolo Cremonesi, Franca Garzotto, Matteo Guarnerio, Francesco Gusmeroli, Roberto Pagano:
Decision Making through Polarized Summarization of User Reviews. DMRS 2015: 37-40 - [c68]Mona Naseri, Mehdi Elahi, Paolo Cremonesi:
Investigating the Decision Making Process of Users based on the PoliMovie Dataset. DMRS 2015: 41-44 - [c67]Yashar Deldjoo, Mehdi Elahi, Massimo Quadrana, Paolo Cremonesi:
Toward Building a Content-Based Video Recommendation System Based on Low-Level Features. EC-Web 2015: 45-56 - [c66]Paolo Cremonesi, Primo Modica, Roberto Pagano, Emanuele Rabosio, Letizia Tanca:
Personalized and Context-Aware TV Program Recommendations Based on Implicit Feedback. EC-Web 2015: 57-68 - [c65]