SEARCH AND RECOMMENDATION
Human-centric, multimodal strategies and modules built on accountable algorithms for transparency and trust
The ubiquity of sensors and the apparition of new user interfaces has profoundly changed the nature of search and recommendation engines and how we interact with them.
Our research is carried out in collaboration with the NAVER search group, responsible for the world's 5th biggest search engine.
This provides research opportunities that go far beyond the traditional information retrieval and recommendation frameworks.
We have become more reluctant to express our needs or preferences in an explicit manner as previously done through primarily text-based interfaces. The way we give feedback to search and recommendation (S&R) systems is noisier, more biased and more heterogeneous. Users, service providers and regulators have greater requirements whereby they want more accountable, transparent and ethical technologies that respect the privacy-utility trade-off, whilst also offering some kind of guarantee on the fairness of the results they provide.
This change brings new exciting research opportunities that our research group enjoys initiating and experimenting with in the real world.
We firmly believe that search and recommendation processes must be considered as multi-stakeholder, multi-round games (continuous learning loops), where the engine and the user interplay collaboratively with the different types of feedback they each provide. Facilitating communication between the system and the user naturally leads to the design of multimodal strategies where text in any language, images, sound and speech can be consistently handled together. With the same communication objective in mind, and in particular to establish mutual confidence, we focus on developing unbiased ranking algorithms that can explain the advice they provide in a transparent and understandable form, while preserving fairness and diversity. The technology we use to pursue our research relies on Deep-IR/Deep-matching architectures, context-aware sequential recommendation models, causal and counter-factual learning-to-rank algorithms, conversational S&R and reinforcement learning.
- Adaptive pointwise-pairwise learning-to-rank for content-based personalized recommendation, Yagmur Gizem Cinar, Jean-Michel Renders | ACM Conference on Recommender Systems (RecSys), 22-26 September, 2020
- Guided exploration of user groups, Mariia Seleznova, Bernard Omidvar-Tehrani, Sihem Amer-Yahia, Eric Simon | 46th International Conference on Very Large Data Bases (VLDB), 31 August-4 September, 2020
- Cohort analytics: efficiency and applicability, Bernard Omidvar-Tehrani, Sihem Amer-Yahia, Laks V.S. Lakshmanan | The International Journal on Very Large Data Bases (VLDBJ), Springer, August 2020
- Multi-grouping robust fair ranking, Thibaut Thonet, Jean-Michel Renders | International ACM SIGIR Conference on Research and Development in Information Retrieval, 25-30 July, 2020
- Designing ambient wanderer: mobile recommendations for urban exploration, Sruthi Viswanathan, Bernard Omidvar-Tehrani, Adrien Bruyat, Frédéric Roulland, Antonietta Grasso | ACM conference on Designing Interactive Systems (DIS), 6-11 July, 2020
- Hybrid wizard of Oz: concept testing a recommender system, Sruthi Viswanathan, Bernard Omidvar-Tehrani, Adrien Bruyat, Frédéric Roulland, Antonietta Grasso | Computer Human Interaction conference (CHI), 25 April-1 May, 2020
- Learning to rank images with cross-modal graph convolutions, Thibault Formal, Stéphane Clinchant, Jean-Michel Renders, Sooyeol Lee, Geun Hee Cho | European Conference on Information Retrieval (ECIR), 14-17 April, 2020
- Seed-guided deep document clustering, Maziar Moradi Fard, Thibaut Thonet, Eric Gaussier | European Conference on Information Retrieval (ECIR), 14-17 April, 2020
- SAGE: interactive state-aware point-of-interest recommendation, Bernard Omidvar-Tehrani, Sruthi Viswanathan, Frédéric Roulland, Jean-Michel Renders | Workshop of State-based User Modelling, co-located with ACM International Conference on Web Search and Data Mining (WSDM) Conference, 4-7 February, 2020