Publications related to Collaborative filtering

Unified and Scalable Incremental Recommenders with Consumed Item Packs

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 ...

Springer2019

Context-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 INTELLIGENCE2019

Collaborative 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 ...

IEEE2018

Structuring 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 MACHINERY2018

Diversifying 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 ...

2018

Enhancing 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 MACHINERY2018

Enhancing Session-Based Recommendations through Sequential Modeling

Boi Faltings, Vincent Jean Fabrice Schickel, Stéphane Bernard Martin

ACM2018

Detecting 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

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Collaborative filtering

Unified and Scalable Incremental Recommenders with Consumed Item Packs

Context-Tree Recommendation vs Matrix-Factorization: Algorithm Selection and Live Users Evaluation

Collaborative Filtering Under a Sybil Attack: Similarity Metrics do Matter!

Structuring Wikipedia Articles with Section Recommendations

Towards Scalable Personalization

Diversifying Group Recommendation

Enhancing Session-Based Recommendations through Sequential Modeling

Enhancing Session-Based Recommendations through Sequential Modeling

Applications of Approximate Learning and Inference for Probabilistic Models

Detecting Trends in Job Advertisements

Graph Chatbot

Chat with Graph Search

Diversifying Group Recommendation

Context-Tree Recommendation vs Matrix-Factorization: Algorithm Selection and Live Users Evaluation

Structuring Wikipedia Articles with Section Recommendations

Towards Scalable Personalization

Applications of Approximate Learning and Inference for Probabilistic Models

Detecting Trends in Job Advertisements

Enhancing Session-Based Recommendations through Sequential Modeling

Unified and Scalable Incremental Recommenders with Consumed Item Packs

Collaborative Filtering Under a Sybil Attack: Similarity Metrics do Matter!

Enhancing Session-Based Recommendations through Sequential Modeling