The success of digital content platforms such as YouTube depends on the creativity of independent content creators and the efficiency of content distribution. These platforms share advertising revenue with content creators to induce creative effort. Most platforms also use recommendation systems...

Top-k personalized recommendations are ubiquitous, but are they stable? We study whether, given complete information, buyers and sellers prefer to participate in matches formed by top-k recommendations rather than pursuing offline matches among themselves. When there are no constraints on the...

Lately, online users have become avid content creators and consumers of brands and firms through reviews. These serve as sources to peers and reflect reviewers’ satisfaction levels with different elements of the experiences. Thus, online reviews are considered reliable sources of information...

Recommender systems are prevalent across digital platforms. They use machine learning techniques to help consumers make choices by predicting their preferred items. If RS had perfect information about consumer preferences and item attributes, they could recommend the most suitable item for each...

A tourist recommendation system has been implemented based on python, Django framework and MySQL database. Firstly, crawler technology is used to crawl the ratings(reviews of customer experiences) information of TripAdvisor, and then the crawled ratings data is stored in MySQL. In the system,...

The development of an intelligent medication recommendation system can greatly benefit the healthcare industry by providing personalized and targeted treatment plans using artificial intelligence (AI). This system analyzes patient data, including medical history, symptoms, genetic information,...

FlixTime is a movie recommendation web application that recommends a group of friends having different interests in movie genres, some movies that most of them will enjoy. The system uses Hybrid approach to recommend movies due to the downsides of using only content based approach or...

Online Appendix of “Steering via Algorithmic Recommendations” Chen and Tsai (2019)This paper studies whether self-preferencing affects algorithmic recommendations on dominant platforms. We focus on the dual role of Amazon.com as a platform owner and retailer. We find that products sold by...

Subscription-based platforms offer consumers access to a large selection of content at a fixed subscription fee. Recommender systems (RS) can help consumers by reducing the size of this choice set by predicting consumers' preferences. However, because the prediction is based on limited...

Online retailers widely use prices and reviews, as additional information cues along with thumbnail pictures, when making product recommendations to consumers. They may use both prices and reviews (“dual cues”), either one (“single cue”), or simply display pictures of recommended...

Online platforms employ recommendation systems to enhance customer engagement and drive revenue. However, in a multi-sided platform where the platform interacts with diverse stakeholders such as sellers (items) and customers (users), each with their own desired outcomes, finding an appropriate...

There has been much concern that social media, in particular YouTube, may facilitate radicalisation and polarisation of online audiences. This systematic review aimed to determine whether the YouTube recommender system facilitates pathways to problematic content such as extremist or radicalising...

Based on consumers' preferences, AI (artificial intelligence) recommendation automatically filters information, which provokes scholars' debate. Supporters believe that by analyzing the consumers' preferences, AI recommendation enables consumers to choose products more quickly and at a lower...

We formulate the problem of designing a personalized recommendation system for an online business-to-business (B2B) marketplace, propose a method to solve it, and evaluate results using field experiments. In this problem, buyers place requests for quotation (RFQs) to the platform, sellers...

Purpose - In user-oriented websites, e.g. in news websites or in seller websites, it is important to take the user's preferences into account when deciding which items to place in higher-exposure locations. The traditional approach to solving this problem, based on maximizing the average user...

With the advancement in technologies, the opportunities for new integrated solutions are also unlocked. Internet of Things by connecting cyber and physical world introduce enormous paradigm change in computing and information technologies. Recommender system helps in filtering the data by taking...

Although personalized recommender systems are vital for many online platforms, they lead to controversial societal issues such as privacy breaches. In response to such concerns, there is a clear regulatory trend in calling for stringent privacy protection mechanisms implanted in such algorithms....

The pervasive use of personal data by modern algorithmic recommender systems has sparked public outcry for tighter privacy regulations. Personal preferences over different product offerings are a basic constituent of consumer privacy. We study how a profit-driven online platform, who has...

How do recommender systems induce consumers to buy? Extant research neglects to examine the causal paths through which the use of recommender systems leads to consumer purchases. In this study, we conduct a randomized controlled field experiment on the website of an online book retailer and...

Advances in data collection and algorithms help advertisers to better target individual consumers by predicting each consumer's preferences. We first show that when consumers have uncertainties about their preferences, an ad targeted to a consumer carries an implicit message: the algorithm...

Early consumption of a product often benefits later consumers by revealing quality information. Inefficiency arises, however, because early consumers do not internalize the social value of their consumption. How could a platform that intermediates information between early and late consumers...

In current days, the recommendation system is the most popular thing which is used in almost all websites and apps like Internshala, LinkedIn, Instagram, Facebook, Amazon, Netflix, and many more. All of them have used the recommendation system to create practical solutions for the users. A...

Many successful recommendation approaches rely on the optimization of a single objective function for a single stakeholder (i.e., the customer) based on customer-product interaction records. In this paper, we argue that for two-sided marketplaces such as music streaming platforms, it is more...

We conduct a field experiment on a movie-recommendation platform to identify if and how recommendations affect consumption. We use within-consumer randomization at the good level and elicit beliefs about unconsumed goods to disentangle exposure from informational effects. We find recommendations...

We conduct a field experiment on a movie-recommendation platform to identify if and how recommendations affect consumption. We use within-consumer randomization at the good level and elicit beliefs about unconsumed goods to disentangle exposure from informational effects. We find recommendations...

Algorithm-based decision support systems play an increasingly important role in decisions involving exploration tasks, such as product searches, portfolio choices, and human resource procurement. These tasks often involve a trade-off between exploration and exploitation, which can be highly...

An increasing number of consumption decisions happen on two (or more) sided platforms that carry goods and services from a large number of suppliers. In principle, such easy access to a multiplicity of products should lower consumers' search costs and thereby enhance their decision-making. In...

We contribute to the literature on recommendation systems by proposing the Dynamic Nonparametric (DNP) model that incorporates a fully Bayesian approach into the traditional dynamic collaborative filtering and hence captures the change of customer preferences in the mobile game industry with...

I study a cheap talk model between two players: a buyer/decision maker and a seller/expert. There is two-sided private information and I allow for sequential, twoway communication. The buyer faces a decision about purchasing one of a number of goods, and the buyer’s valuation depends on: her...