Publication

Recommender systems: Trends and frontiers

Pearl Pu Faltings
2022
Journal paper
Abstract

Recommender systems (RSs), as used by Netflix, YouTube, or Amazon, are one of the most compelling success stories of AI. Enduring research activity in this area has led to a continuous improvement of recommendation techniques over the years, and today's RSs are indeed often capable to make astonishingly good suggestions. With countless papers being published on the topic each year, one might think the recommendation problem is almost solved. In reality, however, the large majority of published works focuses on algorithmic improvements and relies on data-based evaluation procedures which may sometimes tell us little regarding the effects new algorithms will have in practice. This special issue contains a set of papers which address some of the open challenges and frontiers in RSs research: (i) building interactive and conversational solutions, (ii) understanding recommender systems as socio-technical systems with longitudinal dynamics, (iii) avoiding abstraction traps, and (iv) finding better ways of assessing the impact and value of recommender systems without field tests.

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Related concepts (11)
Recommender system
A recommender system, or a recommendation system (sometimes replacing 'system' with a synonym such as platform or engine), is a subclass of information filtering system that provide suggestions for items that are most pertinent to a particular user. Typically, the suggestions refer to various decision-making processes, such as what product to purchase, what music to listen to, or what online news to read. Recommender systems are particularly useful when an individual needs to choose an item from a potentially overwhelming number of items that a service may offer.
Collaborative filtering
Collaborative filtering (CF) is a technique used by recommender systems. Collaborative filtering has two senses, a narrow one and a more general one. In the newer, narrower sense, collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating). The underlying assumption of the collaborative filtering approach is that if a person A has the same opinion as a person B on an issue, A is more likely to have B's opinion on a different issue than that of a randomly chosen person.
Majority
A majority, also called a simple majority or absolute majority to distinguish it from related terms, is more than half of the total. It is a subset of a set consisting of more than half of the set's elements. For example, if a group consists of 20 individuals, a majority would be 11 or more individuals, while having 10 or fewer individuals would not constitute a majority. "Majority" can be used to specify the voting requirement, as in a "majority vote", which means more than half of the votes cast.
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Related publications (4)

Modurec: Recommender Systems with Feature and Time Modulation

Pascal Frossard, Clément Arthur Yvon Vignac, Javier Alejandro Maroto Morales

Current state of the art algorithms for recommender systems are mainly based on collaborative filtering, which exploits user ratings to discover latent factors in the data. These algorithms unfortunately do not make effective use of other features, which c ...
2020

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

Recommender Systems for Healthy Behavior Change

Onur Yürüten

Sedentary lifestyles and bad eating habits influence the onset of many serious health problems. Healthy behavior change is an arduous task, and requires a careful planning. In this thesis, we propose that behavior recommenders can help their users achieve ...
EPFL2017
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