Kartik Hosanagar

Bio: Kartik Hosanagar is an academic researcher from University of Pennsylvania. The author has contributed to research in topics: Recommender system & Personalization. The author has an hindex of 27, co-authored 83 publications receiving 3885 citations. Previous affiliations of Kartik Hosanagar include Carnegie Mellon University.

Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity

Abstract: This paper examines the effect of recommender systems on the diversity of sales. Two anecdotal views exist about such effects. Some believe recommenders help consumers discover new products and thus increase sales diversity. Others believe recommenders only reinforce the popularity of already popular products. This paper seeks to reconcile these seemingly incompatible views. We explore the question in two ways. First, modeling recommender systems analytically allows us to explore their path dependent effects. Second, turning to simulation, we increase the realism of our results by combining choice models with actual implementations of recommender systems. We arrive at three main results. First, some well known recommenders can lead to a reduction in sales diversity. Because common recommenders (e.g., collaborative filters) recommend products based on sales and ratings, they cannot recommend products with limited historical data, even if they would be rated favorably. In turn, these recommenders can create a rich-get-richer effect for popular products and vice-versa for unpopular ones. This bias toward popularity can prevent what may otherwise be better consumer-product matches. That diversity can decrease is surprising to consumers who express that recommendations have helped them discover new products. In line with this, result two shows that it is possible for individual-level diversity to increase but aggregate diversity to decrease. Recommenders can push each person to new products, but they often push users toward the same products.. Third, we show how basic design choices affect the outcome, and thus managers can choose recommender designs that are more consistent with their sales goals and consumers’ preferences.

Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity

Abstract: This paper examines the effect of recommender systems on the diversity of sales. Two anecdotal views exist about such effects. Some believe recommenders help consumers discover new products and thus increase sales diversity. Others believe recommenders only reinforce the popularity of already-popular products. This paper seeks to reconcile these seemingly incompatible views. We explore the question in two ways. First, modeling recommender systems analytically allows us to explore their path-dependent effects. Second, turning to simulation, we increase the realism of our results by combining choice models with actual implementations of recommender systems. We arrive at three main results. First, some well-known recommenders can lead to a reduction in sales diversity. Because common recommenders (e.g., collaborative filters) recommend products based on sales and ratings, they cannot recommend products with limited historical data, even if they would be rated favorably. In turn, these recommenders can create a rich-get-richer effect for popular products and vice versa for unpopular ones. This bias toward popularity can prevent what may otherwise be better consumer-product matches. That diversity can decrease is surprising to consumers who express that recommendations have helped them discover new products. In line with this, result two shows that it is possible for individual-level diversity to increase but aggregate diversity to decrease. Recommenders can push each person to new products, but they often push users toward the same products. Third, we show how basic design choices affect the outcome, and thus managers can choose recommender designs that are more consistent with their sales goals and consumers' preferences.

Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook

Abstract: We describe the effects of social media advertising content on customer engagement using Facebook data. We content-code more than 100,000 messages across 800 companies using a combination of Amazon Mechanical Turk and state-of-the-art Natural Language Processing and machine learning algorithms. We use this large-scale dataset of content attributes to describe the association of various kinds of social media marketing content with user engagement - defined as Likes, comments, shares, and click-throughs - with the messages. We find that inclusion of widely used content related to brand-personality - like humor, emotion and brand’s philanthropic positioning - is associated with higher levels of consumer engagement (like, comment, share) with a message. We find that directly informative content - like mentions of prices and availability - is associated with lower levels of engagement when included in messages in isolation, but higher engagement levels when provided in combination with brand-personality content. We also find certain directly informative content such as the mention of deals and promotions drive consumers’ path-to-conversion (click-throughs). These results hold after correcting for the non-random targeting of Facebook’s EdgeRank (News Feed) algorithm, so reflect more closely user reaction to content, rather than Facebook’s behavioral targeting. Our results suggest therefore that there may be substantial gains from content engineering by combining informative characteristics associated with immediate leads (via improved click-throughs) with brand-personality related content that help maintain future reach and branding on the social media site (via improved engagement). These results inform content design strategies in social media. Separately, the methodology we apply to content-code large-scale textual data provides a framework for future studies on unstructured data such as advertising content or product reviews.

Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook

Abstract: We describe the effect of social media advertising content on customer engagement using data from Facebook. We content-code 106,316 Facebook messages across 782 companies, using a combination of Amazon Mechanical Turk and natural language processing algorithms. We use this data set to study the association of various kinds of social media marketing content with user engagement—defined as Likes, comments, shares, and click-throughs—with the messages. We find that inclusion of widely used content related to brand personality—like humor and emotion—is associated with higher levels of consumer engagement (Likes, comments, shares) with a message. We find that directly informative content—like mentions of price and deals—is associated with lower levels of engagement when included in messages in isolation, but higher engagement levels when provided in combination with brand personality–related attributes. Also, certain directly informative content, such as deals and promotions, drive consumers’ path to conversio...

Location, Location, Location: An Analysis of Profitability of Position in Online Advertising Markets

Abstract: Sponsored search accounts for 40% of the total online advertising market. These ads appear as ordered lists along with the regular search results in search engine results pages. The conventional wisdom in the industry is that the top position is the most desirable position for advertisers. This has led to intense competition among advertisers to secure the top positions in the results pages. We evaluate the impact of ad placement on revenues and profits generated from sponsored search using data from for several hundred keywords from the ad campaign of an online retailer. Using a hierarchical Bayesian model, we measure the impact of ad placement on both click-through rate and conversion rate for these keywords. We find that while click through rate decreases with position, conversion rate first increases and then decreases with position. The net effect is that, contrary to conventional wisdom, the topmost position in sponsored search advertisements is not necessarily the revenue- or profit-maximizing position. Using a theoretical model we show that one potential driver of these results is the heterogeneity in search costs across consumers and the additional browsing cost incurred in evaluating products across multiple websites.Our results inform the advertising strategies of firms participating in sponsored search auctions and provide insight into consumer behavior in these environments. Specifically, they help correct a significant misunderstanding among advertisers regarding the value of the top position. Further, they reveal potential inefficiencies in present auction mechanisms used by the search engines.

Above the Clouds: A Berkeley View of Cloud Computing

Abstract: Cloud Computing, the long-held dream of computing as a utility, has the potential to transform a large part of the IT industry, making software even more attractive as a service and shaping the way IT hardware is designed and purchased. Developers with innovative ideas for new Internet services no longer require the large capital outlays in hardware to deploy their service or the human expense to operate it. They need not be concerned about overprovisioning for a service whose popularity does not meet their predictions, thus wasting costly resources, or underprovisioning for one that becomes wildly popular, thus missing potential customers and revenue. Moreover, companies with large batch-oriented tasks can get results as quickly as their programs can scale, since using 1000 servers for one hour costs no more than using one server for 1000 hours. This elasticity of resources, without paying a premium for large scale, is unprecedented in the history of IT. Cloud Computing refers to both the applications delivered as services over the Internet and the hardware and systems software in the datacenters that provide those services. The services themselves have long been referred to as Software as a Service (SaaS). The datacenter hardware and software is what we will call a Cloud. When a Cloud is made available in a pay-as-you-go manner to the general public, we call it a Public Cloud; the service being sold is Utility Computing. We use the term Private Cloud to refer to internal datacenters of a business or other organization, not made available to the general public. Thus, Cloud Computing is the sum of SaaS and Utility Computing, but does not include Private Clouds. People can be users or providers of SaaS, or users or providers of Utility Computing. We focus on SaaS Providers (Cloud Users) and Cloud Providers, which have received less attention than SaaS Users. From a hardware point of view, three aspects are new in Cloud Computing.

Economics of Information

Abstract: The author systematically examines one of the important issues of information — establishing the market price. He introduces the concept of «search» — where a buyer wanting to get a better price, is forced to question sellers. The article deals with various aspects of finding the necessary information.

Interactive home shopping: Consumer, retailer, and manufacturer incentives to participate in

Abstract: The authors examine the implications of electronic shopping for consumers, retailers, and manufacturers. They assume that near-term technological developments will offer consumers unparalleled opportunities to locate and compare product offerings. They examine these advantages as a function of typical consumer goals and the types of products and services being sought and offer conclusions regarding consumer incentives and disincentives to purchase through interactive home shopping vis-à-vis traditional retail formats. The authors discuss implications for industry structure as they pertain to competition among retailers, competition among manufacturers, and retailer-manufacturer relationships.

Internet Advertising and the Generalized Second-Price Auction: Selling Billions of Dollars Worth of Keywords

Abstract: We investigate the "generalized second-price" (GSP) auction, a new mechanism used by search engines to sell online advertising. Although GSP looks similar to the Vickrey-Clarke-Groves (VCG) mechanism, its properties are very different. Unlike the VCG mechanism, GSP generally does not have an equilibrium in dominant strategies, and truth-telling is not an equilibrium of GSP. To analyze the properties of GSP, we describe the generalized English auction that corresponds to GSP and show that it has a unique equilibrium. This is an ex post equilibrium, with the same payoffs to all players as the dominant strategy equilibrium of VCG.