2,490 research outputs found
Optimal Pricing with Recommender Systems
We study optimal pricing in the presence of recommender systems. A recommender system affects the market in two ways: (i) it creates value by reducing product uncertainty for the customers and hence (ii) its recommendations can be offered as add-ons which generate informational externalities. The quality of the recommendation add-on is endogenously determined by sales. We investigate the impact of these factors on the optimal pricing by a seller with a recommender system against a competitive fringe without such a system. If the recommender system is sufficiently effective in reducing uncertainty, then the seller prices otherwise symmetric products differently to have some products experienced more aggressively. Moreover, the seller segments the market so that customers with more inflexible tastes pay higher prices to get better recommendations.Recommender system, Collaborative filtering, Add-ons, Pricing, Information externality
Efficient Recommender Systems
We study the efficient allocation of buyers in the presence of recommender systems. A recommender system affects the market in two ways: (i) it creates value by reducing product uncertainty for the customers and hence (ii) its recommendations can be offered as add-ons, which generates informational externalities. We investigate the impact of these factors on the efficient allocation of buyers across different products. We find that the efficient allocation requires that the seller with the recommender system has full market share. If the recommender system is sufficiently effective in reducing uncertainty, it is optimal to have some products to be purchased by a larger group of people than others. The large group consists of customers with flexible tastes.Recommender system, Collaborative filtering, Add-ons, Pricing, Information externality
In pursuit of satisfaction and the prevention of embarrassment : affective state in group recommender systems
Peer reviewedPostprin
Show Me the Money: Dynamic Recommendations for Revenue Maximization
Recommender Systems (RS) play a vital role in applications such as e-commerce
and on-demand content streaming. Research on RS has mainly focused on the
customer perspective, i.e., accurate prediction of user preferences and
maximization of user utilities. As a result, most existing techniques are not
explicitly built for revenue maximization, the primary business goal of
enterprises. In this work, we explore and exploit a novel connection between RS
and the profitability of a business. As recommendations can be seen as an
information channel between a business and its customers, it is interesting and
important to investigate how to make strategic dynamic recommendations leading
to maximum possible revenue. To this end, we propose a novel \model that takes
into account a variety of factors including prices, valuations, saturation
effects, and competition amongst products. Under this model, we study the
problem of finding revenue-maximizing recommendation strategies over a finite
time horizon. We show that this problem is NP-hard, but approximation
guarantees can be obtained for a slightly relaxed version, by establishing an
elegant connection to matroid theory. Given the prohibitively high complexity
of the approximation algorithm, we also design intelligent heuristics for the
original problem. Finally, we conduct extensive experiments on two real and
synthetic datasets and demonstrate the efficiency, scalability, and
effectiveness our algorithms, and that they significantly outperform several
intuitive baselines.Comment: Conference version published in PVLDB 7(14). To be presented in the
VLDB Conference 2015, in Hawaii. This version gives a detailed submodularity
proo
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
Exploration vs. Exploitation in the Information Filtering Problem
We consider information filtering, in which we face a stream of items too
voluminous to process by hand (e.g., scientific articles, blog posts, emails),
and must rely on a computer system to automatically filter out irrelevant
items. Such systems face the exploration vs. exploitation tradeoff, in which it
may be beneficial to present an item despite a low probability of relevance,
just to learn about future items with similar content. We present a Bayesian
sequential decision-making model of this problem, show how it may be solved to
optimality using a decomposition to a collection of two-armed bandit problems,
and show structural results for the optimal policy. We show that the resulting
method is especially useful when facing the cold start problem, i.e., when
filtering items for new users without a long history of past interactions. We
then present an application of this information filtering method to a
historical dataset from the arXiv.org repository of scientific articles.Comment: 36 pages, 5 figure
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