Usercentric Operational Decision Making in Distributed Information Retrieval

Information specialists in enterprises regularly use distributed information retrieval (DIR) systems that query a large number of information retrieval (IR) systems, merge the retrieved results, and display them to users. There can be considerable heterogeneity in the quality of results returned by different IR servers. Further, because different servers handle collections of different sizes and have different processing and bandwidth capacities, there can be considerable heterogeneity in their response times. The broker in the DIR system has to decide which servers to query, how long to wait for responses, and which retrieved results to display based on the benefits and costs imposed on users. The benefit of querying more servers and waiting longer is the ability to retrieve more documents. The costs may be in the form of access fees charged by IR servers or user’s cost associated with waiting for the servers to respond. We formulate the broker’s decision problem as a stochastic mixed-integer program and present analytical solutions for the problem. Using data gathered from FedStats—a system that queries IR engines of several U.S. federal agencies—we demonstrate that the technique can significantly increase the utility from DIR systems. Finally, simulations suggest that the technique can be applied to solve the broker’s decision problem under more complex decision environments

How Do Recommender Systems Affect Sales Diversity? A Cross-Category Investigation via Randomized Field Experiment

We investigate the impact of collaborative filtering recommender algorithms (e.g., Amazon\u27s “Customers who bought this item also bought”) commonly used in e-commerce on sales diversity. We use data from a randomized field experiment run on a top retailer in North America across 82,290 SKUs and 1,138,238 users. We report four main findings. First, we demonstrate across a wide range of product categories that the use of traditional collaborative filters (or CFs) is associated with a decrease in sales diversity relative to a world without product recommendations. Further, the design of the CF matters. CFs based on purchase data are associated with a greater effect size than those based on product views. Second, the decrease in aggregate sales diversity may not always be accompanied by a corresponding decrease in individual-level consumption diversity. In fact, it is even possible for individual consumption diversity to increase while aggregate sales diversity decreases. Third, co-purchase network analysis shows that recommenders can help individuals explore new products but similar users end up exploring the same kinds of products resulting in the concentration bias at the aggregate level. Fourth and finally, there is a difference between absolute and relative impact on niche items. Specifically, absolute sales and views for niche items in fact increase, but their gains are smaller compared to the gains in views and sales for popular items. Thus, while niche items gain in absolute terms, they lose out in terms of market shares

Fostering IPv6 Migration Through Network Quality Differentials

Although IPv6 has been the next generation Internet protocol for nearly 15 years, new evidences indicate that transitioning from IPv4 to IPv6 is about to become a more pressing issue. This paper attempts to quantify if and how such a transition may unfold. The focus is on connectivity quality, e.g., as measured by users\u27 experience when accessing content, as a possible incentive (or disincentive) for migrating to IPv6, and on translation costs (between IPv6 and IPv4) that Internet Service Providers will incur during this transition. The paper develops a simple model that captures some of the underlying interactions, and highlights the ambiguous role of translation gateways that can either help or discourage IPv6 adoption. The paper is an initial foray in the complex and often puzzling issue of migrating the current Internet to a new version with which it is incompatible

Fostering IPv6 Migration Through Network Quality Differentials

Although IPv6 has been the next generation Internet protocol for nearly 15 years, new evidences indicate that transitioning from IPv4 to IPv6 is about to become a more pressing issue. This paper attempts to quantify if and how such a transition may unfold. The focus is on ``connectivity quality,\u27\u27 e.g., as measured by users\u27 experience when accessing content, as a possible incentive (or disincentive) for migrating to IPv6, and on ``translation costs\u27\u27 (between IPv6 and IPv4) that Internet Service Providers will incur during this transition. The paper develops a simple model that captures some of the underlying interactions, and highlights the ambiguous role of translation gateways that can either help or discourage IPv6 adoption. The paper is an initial foray in the complex and often puzzling issue of migrating the current Internet to a new version with which it is incompatible

Cooperative Cashing? An Economic Analysis of Document Duplication in Cooperative Web Caching

Cooperative caching is a popular mechanism to allow an array of distributed caches to cooperate and serve each others\u27 Web requests. Controlling duplication of documents across cooperating caches is a challenging problem faced by cache managers. In this paper, we study the economics of document duplication in strategic and nonstrategic settings. We have three primary findings. First, we find that the optimum level of duplication at a cache is nondecreasing in intercache latency, cache size, and extent of request locality. Second, in situations in which cache peering spans organizations, we find that the interaction between caches is a game of strategic substitutes wherein a cache employs lesser resources towards eliminating duplicate documents when the other caches employs more resources towards eliminating duplicate documents at that cache. Thus, a significant challenge will be to simultaneously induce multiple caches to contribute more resources towards reducing duplicate documents in the system. Finally, centralized decision making, which as expected provides improvements in average latency over a decentralized setup, can entail highly asymmetric duplication levels at the caches. This in turn can benefit one set of users at the expense of the other, and thus will be challenging to implement

A Model of Product Design and Information Disclosure Investments

As information availability for products and services is increasing and as consumers engage in more online search prior to purchase decisions, it is becoming more important for firms to know when to invest to reduce consumer uncertainty. We argue that today’s firms should view product design and investments to reduce consumer uncertainty as an integrated process, which is in turn heavily influenced by how much information consumers can obtain independently, for example, by reading product reviews or through third party infomediaries. Using a game-theoretic model, we explain how product quality decisions influence future investments to reduce consumer uncertainty, and demonstrate how firms should take this dependency into account to avoid over-investing in quality. We also show that firms can free ride on the product information already available in the market by third-party infomediaries, and reduce their own disclosure investments. We show that this is especially true for lower quality firms

A Model of Product Design and Information Disclosure Investments

New technologies such as product simulators and virtual reality now allow firms to provide realistic product usage experiences and reduce buyer uncertainty about product quality. We argue that today’s firms should view product design and investments to reduce buyer uncertainty as an integrated process, which is in turn influenced by how much information buyers can obtain from third-party infomediaries. We introduce a game-theoretic model of a competitive market where both quality production and quality disclosure are endogenous decisions, affected by information made available by third parties. We show that quality investment under uncertainty never exceeds the level of quality investment under perfect information. Furthermore, we show that information availability by third parties allows firms to free ride, and it especially favors lower quality firms, who can reduce their information disclosure investments more so than higher-quality firms. Finally, we show that the intuitive argument that firms must improve their product quality when overall information availability in the market improves does not always hold. Instead, improved information availability may enable firms to reduce their quality in some situations

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

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 conversion (click-throughs). These results persist after incorporating corrections for the nonrandom targeting of Facebook’s EdgeRank (News Feed) algorithm and so reflect more closely user reaction to content than Facebook’s behavioral targeting. Our results suggest that there are benefits to content engineering that combines informative characteristics that help in obtaining immediate leads (via improved click-throughs) with brand personality–related content that helps in maintaining future reach and branding on the social media site (via improved engagement). These results inform content design strategies. Separately, the methodology we apply to content-code text is useful for future studies utilizing unstructured data such as advertising content or product reviews

Blockbuster Culture\u27s Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity

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\u27 preferences

Recomended for You: The Impact of Profit Incentives on the Relevance of Online Recommendations

Recommender systems are commonly used by Internet firms to improve consumers’ shopping experience and increase firm sales and profits. A large stream of work on recommender design has studied the problem of identifying the most relevant items to recommend to users. In parallel, recent empirical work has started to provide evidence that real-world recommenders contribute to increased sales and profitability for the firms. However, maximizing consumer welfare and firm profit are not the same. Given that recommenders impact sales and profits, a natural question is what is the impact of firm’s profit incentives on recommender design? This paper studies optimal recommender design in a profit-maximizing framework to answer the question and identifies the conditions under which a profit-maximizing recommender recommends the item with highest margins and those under which it recommends the most relevant item. We further elaborate on the social cost of the mismatch between consumer and firm incentives