Concept
Filtrage collaboratif
Publications associées (117)
Unified and Scalable Incremental Recommenders with Consumed Item Packs
Rachid Guerraoui, Rhicheek Patra
Recommenders personalize the web content using collaborative filtering to relate users (or items). This work proposes to unify user-based, item-based and neural word embeddings types of recommenders under a single abstraction for their input, we name Consu ...
Springer2019Context-Tree Recommendation vs Matrix-Factorization: Algorithm Selection and Live Users Evaluation
Boi Faltings, Vincent Jean Fabrice Schickel, Stéphane Bernard Martin
We describe the selection, implementation and online evaluation of two e-commerce recommender systems developed with our partner company, Prediggo. The first one is based on the novel method of Bayesian Variable-order Markov Modeling (BVMM). The second, SS ...
ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2019Collaborative Filtering Under a Sybil Attack: Similarity Metrics do Matter!
Rachid Guerraoui, Anne-Marie Kermarrec, Davide Frey, Antoine Rault, Florestan Laurentin Marie De Moor
Recommendation systems help users identify interesting content, but they also open new privacy threats. In this paper, we deeply analyze the effect of a Sybil attack that tries to infer information on users from a user-based collaborative-filtering recomme ...
IEEE2018Structuring Wikipedia Articles with Section Recommendations
Robert West, Tiziano Piccardi, Michele Catasta
Sections are the building blocks of Wikipedia articles. They enhance readability and can be used as a structured entry point for creating and expanding articles. Structuring a new or already existing Wikipedia article with sections is a hard task for human ...
ASSOC COMPUTING MACHINERY2018Towards Scalable Personalization
The ever-growing amount of online information calls for Personalization. Among the various personalization systems, recommenders have become increasingly popular in recent years. Recommenders typically use collaborative filtering to suggest the most releva ...
EPFL2018Diversifying Group Recommendation
Quoc Viet Hung Nguyen, Thành Tâm Nguyên
Recommender-systems has been a significant research direction in both literature and practice. The core of recommender systems are the recommendation mechanisms, which suggest to a user a selected set of items supposed to match user true intent, based on e ...
2018Enhancing Session-Based Recommendations through Sequential Modeling
Boi Faltings, Vincent Jean Fabrice Schickel, Stéphane Bernard Martin
Recommender systems typically determine the items they should recommend by learning models of user-preferences. Most often, those preferences are modeled as static and independent of context. In real life however, users consider items in sequence: TV serie ...
ASSOC COMPUTING MACHINERY2018Enhancing Session-Based Recommendations through Sequential Modeling
Boi Faltings, Vincent Jean Fabrice Schickel, Stéphane Bernard Martin
Recommender systems typically determine the items they should recommend by learning models of user-preferences. Most often, those preferences are modeled as static and independent of context. In real life however, users consider items in sequence: TV serie ...
ACM2018Applications of Approximate Learning and Inference for Probabilistic Models
We develop approximate inference and learning methods for facilitating the use of probabilistic modeling techniques motivated by applications in two different areas. First, we consider the ill-posed inverse problem of recovering an image from an underdeter ...
EPFL2017Detecting Trends in Job Advertisements
Pierre Dillenbourg, Kshitij Sharma, Khalil Mrini
We present an automatic method for trend detection in job ads. From a job-posting website, we collect job ads from 16 countries and in 8 languages and 6 job domains. We pre-process them by removing stop words, lemmatising and performing cross-domain filter ...
2017