5,297 research outputs found
Recommender systems in industrial contexts
This thesis consists of four parts: - An analysis of the core functions and
the prerequisites for recommender systems in an industrial context: we identify
four core functions for recommendation systems: Help do Decide, Help to
Compare, Help to Explore, Help to Discover. The implementation of these
functions has implications for the choices at the heart of algorithmic
recommender systems. - A state of the art, which deals with the main techniques
used in automated recommendation system: the two most commonly used algorithmic
methods, the K-Nearest-Neighbor methods (KNN) and the fast factorization
methods are detailed. The state of the art presents also purely content-based
methods, hybridization techniques, and the classical performance metrics used
to evaluate the recommender systems. This state of the art then gives an
overview of several systems, both from academia and industry (Amazon, Google
...). - An analysis of the performances and implications of a recommendation
system developed during this thesis: this system, Reperio, is a hybrid
recommender engine using KNN methods. We study the performance of the KNN
methods, including the impact of similarity functions used. Then we study the
performance of the KNN method in critical uses cases in cold start situation. -
A methodology for analyzing the performance of recommender systems in
industrial context: this methodology assesses the added value of algorithmic
strategies and recommendation systems according to its core functions.Comment: version 3.30, May 201
Replication of recommender systems with impressions
Impressions are a novel data type in Recommender Systems containing the previously-exposed items, i.e., what was shown on-screen. Due to their novelty, the current literature lacks a characterization of impressions, and replications of previous experiments. Also, previous research works have mainly used impressions in industrial contexts or recommender systems competitions, such as the ACM RecSys Challenges. This work is part of an ongoing study about impressions in recommender systems. It presents an evaluation of impressions recommenders on current open datasets, comparing not only the recommendation quality of impressions recommenders against strong baselines, but also determining if previous progress claims can be replicated
Recommender systems and their ethical challenges
This article presents the first, systematic analysis of the ethical challenges posed by recommender systems through a literature review. The article identifies six areas of concern, and maps them onto a proposed taxonomy of different kinds of ethical impact. The analysis uncovers a gap in the literature: currently user-centred approaches do not consider the interests of a variety of other stakeholders—as opposed to just the receivers of a recommendation—in assessing the ethical impacts of a recommender system
Benchmarking news recommendations: the CLEF NewsREEL use case
The CLEF NewsREEL challenge is a campaign-style evaluation lab allowing participants to evaluate and optimize news recommender algorithms. The goal is to create an algorithm that is able to generate news items that users would click, respecting a strict time constraint. The lab challenges participants to compete in either a "living lab" (Task 1) or perform an evaluation that replays recorded streams (Task 2). In this report, we discuss the objectives and challenges of the NewsREEL lab, summarize last year's campaign and outline the main research challenges that can be addressed by participating in NewsREEL 2016
The Mediation Effect of Trusting Beliefs on the Relationship Between Expectation-Confirmation and Satisfaction with the Usage of Online Product Recommendation
Online Product Recommendations (OPRs) are increasingly available to onlinecustomers as a value-added self-service in evaluating and choosing a product.Research has highlighted several advantages that customers can gain from usingOPRs. However, the realization of these advantages depends on whether and towhat extent customers embrace and fully utilise them. The relatively low OPR USAgerate indicates that customers have not yet developed trust in OPRs’ performance.Past studies also have established that satisfaction is a valid measure of systemperformance and a consistent significant determinant of users’ continuous systemusage. Therefore, this study aimed to examine the mediation effect of trustingbeliefs on the relationship between expectation-confirmation and satisfaction. Theproposed research model is tested using data collected via an online survey from626 existing users of OPRs. The empirical results revealed that social-psychologicalbeliefs (perceived confirmation and trust) are significant contributors to customersatisfaction with OPRs. Additionally, trusting beliefs partially mediate the impactof perceived confirmation on customer satisfaction. Moreover, this study validatesthe extensions of the interpersonal trust construct to trust in OPRs and examinesthe nomological validity of trust in terms of competence, benevolence, andintegrity. The findings provide a number of theoretical and practical implications. 
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