1,951 research outputs found
Utilizing Online Social Network and Location-Based Data to Recommend Products and Categories in Online Marketplaces
Recent research has unveiled the importance of online social networks for
improving the quality of recommender systems and encouraged the research
community to investigate better ways of exploiting the social information for
recommendations. To contribute to this sparse field of research, in this paper
we exploit users' interactions along three data sources (marketplace, social
network and location-based) to assess their performance in a barely studied
domain: recommending products and domains of interests (i.e., product
categories) to people in an online marketplace environment. To that end we
defined sets of content- and network-based user similarity features for each
data source and studied them isolated using an user-based Collaborative
Filtering (CF) approach and in combination via a hybrid recommender algorithm,
to assess which one provides the best recommendation performance.
Interestingly, in our experiments conducted on a rich dataset collected from
SecondLife, a popular online virtual world, we found that recommenders relying
on user similarity features obtained from the social network data clearly
yielded the best results in terms of accuracy in case of predicting products,
whereas the features obtained from the marketplace and location-based data
sources also obtained very good results in case of predicting categories. This
finding indicates that all three types of data sources are important and should
be taken into account depending on the level of specialization of the
recommendation task.Comment: 20 pages book chapte
Learning User Preferences to Incentivize Exploration in the Sharing Economy
We study platforms in the sharing economy and discuss the need for
incentivizing users to explore options that otherwise would not be chosen. For
instance, rental platforms such as Airbnb typically rely on customer reviews to
provide users with relevant information about different options. Yet, often a
large fraction of options does not have any reviews available. Such options are
frequently neglected as viable choices, and in turn are unlikely to be
evaluated, creating a vicious cycle. Platforms can engage users to deviate from
their preferred choice by offering monetary incentives for choosing a different
option instead. To efficiently learn the optimal incentives to offer, we
consider structural information in user preferences and introduce a novel
algorithm - Coordinated Online Learning (CoOL) - for learning with structural
information modeled as convex constraints. We provide formal guarantees on the
performance of our algorithm and test the viability of our approach in a user
study with data of apartments on Airbnb. Our findings suggest that our approach
is well-suited to learn appropriate incentives and increase exploration on the
investigated platform.Comment: Longer version of AAAI'18 paper. arXiv admin note: text overlap with
arXiv:1702.0284
Optimizing Long-term Value for Auction-Based Recommender Systems via On-Policy Reinforcement Learning
Auction-based recommender systems are prevalent in online advertising
platforms, but they are typically optimized to allocate recommendation slots
based on immediate expected return metrics, neglecting the downstream effects
of recommendations on user behavior. In this study, we employ reinforcement
learning to optimize for long-term return metrics in an auction-based
recommender system. Utilizing temporal difference learning, a fundamental
reinforcement learning algorithm, we implement an one-step policy improvement
approach that biases the system towards recommendations with higher long-term
user engagement metrics. This optimizes value over long horizons while
maintaining compatibility with the auction framework. Our approach is grounded
in dynamic programming ideas which show that our method provably improves upon
the existing auction-based base policy. Through an online A/B test conducted on
an auction-based recommender system which handles billions of impressions and
users daily, we empirically establish that our proposed method outperforms the
current production system in terms of long-term user engagement metrics
Benchmarking: A methodology for ensuring the relative quality of recommendation systems in software engineering
This chapter describes the concepts involved in the process of benchmarking of recommendation systems. Benchmarking of recommendation systems is used to ensure the quality of a research system or production system in comparison to other systems, whether algorithmically, infrastructurally, or according to any sought-after quality. Specifically, the chapter presents evaluation of recommendation systems according to recommendation accuracy, technical constraints, and business values in the context of a multi-dimensional benchmarking and evaluation model encompassing any number of qualities into a final comparable metric. The focus is put on quality measures related to recommendation accuracy, technical factors, and business values. The chapter first introduces concepts related to evaluation and benchmarking of recommendation systems, continues with an overview of the current state of the art, then presents the multi-dimensional approach in detail. The chapter concludes with a brief discussion of the introduced concepts and a summary
Work stream on differentiated treatment:Final report
This report analyses practices of differentiated treatment, whereby a platform applies dissimilar conditions to business users in equivalent situations, and explores the extent to which such practices constitute a potential source of “unfairness” in the relationship between platforms and businesses in the online platform economy. A distinction is made between practices of self-favouring, whereby a platform gives preferential treatment to its own vertically integrated activities over those of rivals, and more general practices of differentiated treatment where one or more business users are treated more favourably than one or more others. The report aims to provide guidance on how to assess the impact of differentiated treatment by online platforms from a technical, economic and legal perspective, and identifies areas requiring further scrutiny because of the especially problematic nature of certain practices implemented by platforms. Special scrutiny seems particularly needed for practices of differentiated treatment by vertically integrated platforms that are dominant or whose consumers single-home and have high switching costs. In these circumstances, the relevant harms can outweigh the efficiencies of differentiated treatment. Another area of attention is differentiated treatment that significantly harms business users and for which the platform does not have a legitimate justification. The extent to which legitimate reasons invoked by a platform can justify harm to businesses is a key issue for future consideration. Significant harm to business users may translate into consumer detriment through less choice and diversity of offerings. Specific future prohibitions of certain problematic practices of differentiated treatment to promote diversity, fairness or equality of opportunities for businesses should be coupled with effective monitoring and enforcement mechanisms. Two areas are of particular concern: 1) the observability of differentiated treatment by platforms arising from techniques such as personalisation and localisation; (2) the availability of effective redress for businesses against the restriction, suspension or termination of service by platforms
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