11,984 research outputs found
User evaluation of a market-based recommender system
Recommender systems have been developed for a wide variety of applications (ranging from books, to holidays, to web pages). These systems have used a number of different approaches, since no one technique is best for all users in all situations. Given this, we believe that to be effective, systems should incorporate a wide variety of such techniques and then some form of overarching framework should be put in place to coordinate them so that only the best recommendations (from whatever source) are presented to the user. To this end, in our previous work, we detailed a market-based approach in which various recommender agents competed with one another to present their recommendations to the user. We showed through theoretical analysis and empirical evaluation with simulated users that an appropriately designed marketplace should be able to provide effective coordination. Building on this, we now report on the development of this multi-agent system and its evaluation with real users. Specifically, we show that our system is capable of consistently giving high quality recommendations, that the best recommendations that could be put forward are actually put forward, and that the combination of recommenders performs better than any constituent recommende
Algorithmic Collusion or Competition: the Role of Platforms' Recommender Systems
Recent academic research has extensively examined algorithmic collusion
resulting from the utilization of artificial intelligence (AI)-based dynamic
pricing algorithms. Nevertheless, e-commerce platforms employ recommendation
algorithms to allocate exposure to various products, and this important aspect
has been largely overlooked in previous studies on algorithmic collusion. Our
study bridges this important gap in the literature and examines how
recommendation algorithms can determine the competitive or collusive dynamics
of AI-based pricing algorithms. Specifically, two commonly deployed
recommendation algorithms are examined: (i) a recommender system that aims to
maximize the sellers' total profit (profit-based recommender system) and (ii) a
recommender system that aims to maximize the demand for products sold on the
platform (demand-based recommender system). We construct a repeated game
framework that incorporates both pricing algorithms adopted by sellers and the
platform's recommender system. Subsequently, we conduct experiments to observe
price dynamics and ascertain the final equilibrium. Experimental results reveal
that a profit-based recommender system intensifies algorithmic collusion among
sellers due to its congruence with sellers' profit-maximizing objectives.
Conversely, a demand-based recommender system fosters price competition among
sellers and results in a lower price, owing to its misalignment with sellers'
goals. Extended analyses suggest the robustness of our findings in various
market scenarios. Overall, we highlight the importance of platforms'
recommender systems in delineating the competitive structure of the digital
marketplace, providing important insights for market participants and
corresponding policymakers.Comment: 33 pages, 5 figures, 4 table
Learning Usersā Interests in a Market-Based Recommender System
Recommender systems are widely used to cope with the problem of information overload and, consequently, many recommendation methods have been developed. However, no one technique is best for all users in all situations. To combat this, we have previously developed a market-based recommender system that allows multiple agents (each representing a different recommendation method or system) to compete with one another to present their best recommendations to the user. Our marketplace thus coordinates multiple recommender agents and ensures only the best recommendations are presented. To do this effectively, however, each agent needs to learn the usersā interests and adapt its recommending behaviour accordingly. To this end, in this paper, we develop a reinforcement learning and Boltzmann exploration strategy that the recommender agents can use for these tasks. We then demonstrate that this strategy helps the agents to effectively obtain information about the usersā interests which, in turn, speeds up the market convergence and enables the system to rapidly highlight the best recommendations
Labour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners
In this paper, we suggest a novel method to aid lifelong learners to access
relevant OER based learning content to master skills demanded on the labour
market. Our software prototype 1) applies Text Classification and Text Mining
methods on vacancy announcements to decompose jobs into meaningful skills
components, which lifelong learners should target; and 2) creates a hybrid OER
Recommender System to suggest personalized learning content for learners to
progress towards their skill targets. For the first evaluation of this
prototype we focused on two job areas: Data Scientist, and Mechanical Engineer.
We applied our skill extractor approach and provided OER recommendations for
learners targeting these jobs. We conducted in-depth, semi-structured
interviews with 12 subject matter experts to learn how our prototype performs
in terms of its objectives, logic, and contribution to learning. More than 150
recommendations were generated, and 76.9% of these recommendations were treated
as useful by the interviewees. Interviews revealed that a personalized OER
recommender system, based on skills demanded by labour market, has the
potential to improve the learning experience of lifelong learners.Comment: This paper has been accepted to be published in the proceedings of
CSEDU 2020 by SciTePres
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Study on Recommender Systems for Business-To-Business Electronic Commerce
Recommender systems have become a popular technique and strategy for helping users select desirable products or services. Most research in this area focused on applying the method to help the customers in Business-to-Customer (B2C) electronic commerce (e-commerce), however, the participants in Business- to-Business (B2B) market can also get useful assistance from the recommender system. In this article we discuss the application of recommender system to B2B e-commerce. First, we examine how recommender system help B2B participants do transactions easier, then we design an effective system framework for the B2B e-commerce\u27s recommender system based on B2B business practices and business intelligence; and then, we define the model components and processes; in the end, the ongoing challenges of the application will be discussed
Learning users' interests by quality classification in market-based recommender systems
Recommender systems are widely used to cope with the problem of information overload and, to date, many recommendation methods have been developed. However, no one technique is best for all users in all situations. To combat this, we have previously developed a market-based recommender system that allows multiple agents (each representing a different recommendation method or system) to compete with one another to present their best recommendations to the user. In our system, the marketplace encourages good recommendations by rewarding the corresponding agents who supplied them according to the usersā ratings of their suggestions. Moreover, we have theoretically shown how our system incentivises the agents to bid in a manner that ensures only the best recommendations are presented. To do this effectively in practice, however, each agent needs to be able to classify its recommendations into different internal quality levels, learn the usersā interests for these different levels, and then adapt its bidding behaviour for the various levels accordingly. To this end, in this paper we develop a reinforcement learning and Boltzmann exploration strategy that the recommending agents can exploit for these tasks. We then demonstrate that this strategy does indeed help the agents to effectively obtain information about the usersā interests which, in turn, speeds up the market convergence and enables the system to rapidly highlight the best recommendations
The Limits of Popularity-Based Recommendations, and the Role of Social Ties
In this paper we introduce a mathematical model that captures some of the
salient features of recommender systems that are based on popularity and that
try to exploit social ties among the users. We show that, under very general
conditions, the market always converges to a steady state, for which we are
able to give an explicit form. Thanks to this we can tell rather precisely how
much a market is altered by a recommendation system, and determine the power of
users to influence others. Our theoretical results are complemented by
experiments with real world social networks showing that social graphs prevent
large market distortions in spite of the presence of highly influential users.Comment: 10 pages, 9 figures, KDD 201
Extraction of User Navigation Pattern Based on Particle Swarm Optimization
With current projections regarding the growth of Internet sales, online retailing raises many questions about how to market on the Net. A Recommender System (RS) is a composition of software tools that provides valuable piece of advice for items or services chosen by a user. Recommender systems are currently useful in both the research and in the commercial areas. Recommender systems are a means of personalizing a site and a solution to the customer?s information overload problem. Recommender Systems (RS) are software tools and techniques providing suggestions for items and/or services to be of use to a user. These systems are achieving widespread success in e-commerce applications nowadays, with the advent of internet. This paper presents a categorical review of the field of recommender systems and describes the state-of-the-art of the recommendation methods that are usually classified into four categories: Content based Collaborative, Demographic and Hybrid systems. To build our recommender system we will use fuzzy logic and Markov chain algorithm
PrivateJobMatch: A Privacy-Oriented Deferred Multi-Match Recommender System for Stable Employment
Coordination failure reduces match quality among employers and candidates in
the job market, resulting in a large number of unfilled positions and/or
unstable, short-term employment. Centralized job search engines provide a
platform that connects directly employers with job-seekers. However, they
require users to disclose a significant amount of personal data, i.e., build a
user profile, in order to provide meaningful recommendations. In this paper, we
present PrivateJobMatch -- a privacy-oriented deferred multi-match recommender
system -- which generates stable pairings while requiring users to provide only
a partial ranking of their preferences. PrivateJobMatch explores a series of
adaptations of the game-theoretic Gale-Shapley deferred-acceptance algorithm
which combine the flexibility of decentralized markets with the intelligence of
centralized matching. We identify the shortcomings of the original algorithm
when applied to a job market and propose novel solutions that rely on machine
learning techniques. Experimental results on real and synthetic data confirm
the benefits of the proposed algorithms across several quality measures. Over
the past year, we have implemented a PrivateJobMatch prototype and deployed it
in an active job market economy. Using the gathered real-user preference data,
we find that the match-recommendations are superior to a typical decentralized
job market---while requiring only a partial ranking of the user preferences.Comment: 45 pages, 28 figures, RecSys 201
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