Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Palumbo, Enrico"'
Generative retrieval for search and recommendation is a promising paradigm for retrieving items, offering an alternative to traditional methods that depend on external indexes and nearest-neighbor searches. Instead, generative models directly associa
Externí odkaz:
http://arxiv.org/abs/2410.16823
Query recommendation systems are ubiquitous in modern search engines, assisting users in producing effective queries to meet their information needs. However, these systems require a large amount of data to produce good recommendations, such as a lar
Externí odkaz:
http://arxiv.org/abs/2405.19749
Autor:
Damianou, Andreas, Fabbri, Francesco, Gigioli, Paul, De Nadai, Marco, Wang, Alice, Palumbo, Enrico, Lalmas, Mounia
In the realm of personalization, integrating diverse information sources such as consumption signals and content-based representations is becoming increasingly critical to build state-of-the-art solutions. In this regard, two of the biggest trends in
Externí odkaz:
http://arxiv.org/abs/2403.07478
An important goal of online platforms is to enable content discovery, i.e. allow users to find a catalog entity they were not familiar with. A pre-requisite to discover an entity, e.g. a book, with a search engine is that the entity is retrievable, i
Externí odkaz:
http://arxiv.org/abs/2303.11648
Autor:
FitzGerald, Jack, Ananthakrishnan, Shankar, Arkoudas, Konstantine, Bernardi, Davide, Bhagia, Abhishek, Bovi, Claudio Delli, Cao, Jin, Chada, Rakesh, Chauhan, Amit, Chen, Luoxin, Dwarakanath, Anurag, Dwivedi, Satyam, Gojayev, Turan, Gopalakrishnan, Karthik, Gueudre, Thomas, Hakkani-Tur, Dilek, Hamza, Wael, Hueser, Jonathan, Jose, Kevin Martin, Khan, Haidar, Liu, Beiye, Lu, Jianhua, Manzotti, Alessandro, Natarajan, Pradeep, Owczarzak, Karolina, Oz, Gokmen, Palumbo, Enrico, Peris, Charith, Prakash, Chandana Satya, Rawls, Stephen, Rosenbaum, Andy, Shenoy, Anjali, Soltan, Saleh, Sridhar, Mukund Harakere, Tan, Liz, Triefenbach, Fabian, Wei, Pan, Yu, Haiyang, Zheng, Shuai, Tur, Gokhan, Natarajan, Prem
Publikováno v:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA
We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9.3B, their subsequent distillation into smaller models ranging from 17M-170M parameters, and their application to the N
Externí odkaz:
http://arxiv.org/abs/2206.07808
Autor:
Monti, Diego, Palumbo, Enrico, Rizzo, Giuseppe, Troncy, Raphaël, Ehrhart, Thibault, Morisio, Maurizio
The knowledge of city exploration trails of people is in short supply because of the complexity in defining meaningful trails representative of individual behaviours and in the access to actionable data. Existing datasets have only recorded isolated
Externí odkaz:
http://arxiv.org/abs/1812.04367
In this paper, we present sequeval, a software tool capable of performing the offline evaluation of a recommender system designed to suggest a sequence of items. A sequence-based recommender is trained considering the sequences already available in t
Externí odkaz:
http://arxiv.org/abs/1810.04956
Autor:
Palumbo, Enrico (AUTHOR)
Publikováno v:
Israel Studies. Fall2023, Vol. 28 Issue 3, p50-75. 26p.
Publikováno v:
In Expert Systems With Applications 1 August 2020 151