3,239 research outputs found
Innovative online platforms: Research opportunities
Economic growth in many countries is increasingly driven by successful startups that operate as online platforms. These success stories have motivated us to define and classify various online platforms according to their business models. This study discusses strategic and operational issues arising from five types of online platforms (resource sharing, matching, crowdsourcing, review, and crowdfunding) and presents some research opportunities for operations management scholars to explore
User-Generated Data Network Effects and Market Competition Dynamics
This Article defines User-Generated Data (“UGD”) network effects, distinguishes them from the more familiar concept of traditional network effects, and explores their implications for market competition dynamics. It explains that UGD network effects produce various efficiencies for digital service providers (“data platforms”) by empowering their services’ optimization, personalization, and continuous diversification. In light of these efficiencies, competition dynamics in UGD-driven markets tend to be unstable and lead to the formation of dominant multi-industry conglomerates. These processes will enhance social welfare because they are natural and efficient. Conversely, countervailing UGD network effects also empower data platforms to detect and neutralize competitive threats, price discriminate among users, and manipulate users’ behaviors. The realization of these effects will result in inefficiencies, which will undermine social welfare. After a comprehensive analysis of conflicting economic forces, this Article sets the ground for informed policymaking. It suggests that emerging calls to aggravate antitrust enforcement and to “break up” Big Tech are ill-advised. Instead, this Article calls for policymakers to draw inspiration from traditional network industries’ public utility and open-access regulations
Self-Organizing Teams in Online Work Settings
As the volume and complexity of distributed online work increases, the
collaboration among people who have never worked together in the past is
becoming increasingly necessary. Recent research has proposed algorithms to
maximize the performance of such teams by grouping workers according to a set
of predefined decision criteria. This approach micro-manages workers, who have
no say in the team formation process. Depriving users of control over who they
will work with stifles creativity, causes psychological discomfort and results
in less-than-optimal collaboration results. In this work, we propose an
alternative model, called Self-Organizing Teams (SOTs), which relies on the
crowd of online workers itself to organize into effective teams. Supported but
not guided by an algorithm, SOTs are a new human-centered computational
structure, which enables participants to control, correct and guide the output
of their collaboration as a collective. Experimental results, comparing SOTs to
two benchmarks that do not offer user agency over the collaboration, reveal
that participants in the SOTs condition produce results of higher quality and
report higher teamwork satisfaction. We also find that, similarly to machine
learning-based self-organization, human SOTs exhibit emergent collective
properties, including the presence of an objective function and the tendency to
form more distinct clusters of compatible teammates
A scalable recommender system : using latent topics and alternating least squares techniques
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsA recommender system is one of the major techniques that handles information overload problem of Information Retrieval. Improves access and proactively recommends relevant information to each user, based on preferences and objectives. During the implementation and planning phases, designers have to cope with several issues and challenges that need proper attention. This thesis aims to show the issues and challenges in developing high-quality recommender systems.
A paper solves a current research problem in the field of job recommendations using a distributed algorithmic framework built on top of Spark for parallel computation which allows the algorithm to scale linearly with the growing number of users.
The final solution consists of two different recommenders which could be utilised for different purposes. The first method is mainly driven by latent topics among users, meanwhile the second technique utilises a latent factor algorithm that directly addresses the preference-confidence paradigm
Unlocking digital competition, Report of the Digital Competition Expert Panel
This is the final report of the Digital Competition Expert Panel. Appointed by the Chancellor in 2018, and chaired by former Chief Economist to President Obama, Professor Jason Furman, the Panel makes recommendations for changes to the UK’s competition framework that are needed to face the economic challenges posed by digital markets, in the UK and internationally. Their report recommends updating the rules governing merger and antitrust enforcement, as well as proposing a bold set of pro-competition measures to open up digital markets
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
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