Robin Burke

• PhD Northwestern University
• Professor (Full) at University of Colorado Boulder

As recommender systems have become more widespread and moved into areas with greater social impact, such as employment and housing , researchers have begun to seek ways to ensure fairness in the results that such systems produce. This work has primarily focused on developing recommendation approaches in which fairness metrics are jointly optimized...

Recommender systems are personalized: we expect the results given to a particular user to reflect that user's preferences. Some researchers have studied the notion of calibration, how well recommendations match users' stated preferences, and bias disparity the extent to which mis-calibration affects different user groups. In this paper, we examine...

In research practice, recommender systems are typically evaluated on their ability to provide items that satisfy the needs and interests of the end user. However, in many recommendation domains, the user for whom recommendations are generated is not the only stakeholder in the recommendation outcome. For example, fairness and balance across stakeho...

Microlending can lead to improved access to capital in impoverished countries. Recommender systems could be used in microlending to provide efficient and personalized service to lenders. However, increasing concerns about discrimination in machine learning hinder the application of recommender systems to the microfinance industry. Most previous rec...

Fairness and related concerns have become of increasing importance in a variety of AI and machine learning contexts. They are also highly relevant to recommender systems and related problems such as information retrieval, as evidenced by the growing literature in RecSys, FAT*, SIGIR, and special sessions such as the FATREC and FACTS-IR workshops an...

There is growing research interest in recommendation as a multi-stakeholder problem, one where the interests of multiple parties should be taken into account. This category subsumes some existing well-established areas of recommendation research including reciprocal and group recommendation, but a detailed taxonomy of different classes of multi-sta...

Recommender systems are known to suffer from the popularity bias problem: popular (i.e. frequently rated) items get a lot of exposure while less popular ones are under-represented in the recommendations. Research in this area has been mainly focusing on finding ways to tackle this issue by increasing the number of recommended long-tail items or oth...

Retailing and social media platforms recommend two types of items to their users: sponsored items that generate ad revenue and non-sponsored ones that do not. The platform selects sponsored items to maximize ad revenue, often through some form of programmatic auction, and non-sponsored items to maximize user utility with a recommender system (RS)....

Research on fairness in machine learning has been recently extended to recommender systems. One of the factors that may impact fairness is bias disparity, the degree to which a group's preferences on various item categories fail to be reflected in the recommendations they receive. In some cases biases in the original data may be amplified or revers...

Research on fairness in machine learning has been recently extended to recommender systems. One of the factors that may impact fairness is bias disparity, the degree to which a group's preferences on various item categories fail to be reflected in the recommendations they receive. In some cases biases in the original data may be amplified or revers...

Recommender systems are known to suffer from the popularity bias problem: popular (i.e. frequently rated) items get a lot of exposure while less popular ones are under-represented in the recommendations. Research in this area has been mainly focusing on finding ways to tackle this issue by increasing the number of recommended long-tail items or oth...

There is growing research interest in recommendation as a multi-stakeholder problem, one where the interests of multiple parties should be taken into account. This category subsumes some existing well-established areas of recommendation research including reciprocal and group recommendation, but a detailed taxonomy of different classes of multi-sta...

It is well known that explicit user ratings in recommender systems are biased towards high ratings, and that users differ significantly in their usage of the rating scale. Implementers usually compensate for these issues through rating normalization or the inclusion of a user bias term in factorization models. However, these methods adjust only for...

Many recommendation algorithms suffer from popularity bias: a small number of popular items being recommended too frequently, while other items get insufficient exposure. Research in this area so far has concentrated on a one-shot representation of this bias, and on algorithms to improve the diversity of individual recommendation lists. In this wor...

Many recommendation algorithms suffer from popularity bias: a small number of popular items being recommended too frequently, while other items get insufficient exposure. Research in this area so far has concentrated on a one-shot representation of this bias, and on algorithms to improve the diversity of individual recommendation lists. In this wor...

Recent research in fairness in machine learning has identified situations in which biases in input data can cause harmful or unwanted effects. Researchers in the areas of personalization and recommendation have begun to study similar types of bias. What these lines of research share is a fixed representation of the protected groups relative to whic...

It is our great pleasure to welcome you to the Second FairUMAP workshop at UMAP 2019. This full-day workshop brings together researchers working at the intersection of user modeling, adaptation, and personalization on one hand, and bias, fairness and transparency in algorithmic systems on the other hand. The workshop was motivated by the observatio...

Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes. As the reach of recommender systems has extended, it has become apparent that the single-minded focus on the user common to academic research has obscured other impo...

Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes. As the reach of recommender systems has extended, it has become apparent that the single-minded focus on the user common to academic research has obscured other impo...

Due to the complexity of recommendation algorithms, experimentation on recommender systems has become a challenging task. Current recommendation algorithms, while powerful, involve large numbers of hyperparameters. Tuning hyperparameters for finding the best recommendation outcome often requires execution of large numbers of algorithmic experiments...

Many recommender systems suffer from popularity bias: popular items are recommended frequently while less popular , niche products, are recommended rarely or not at all. However, recommending the ignored products in the "long tail" is critical for businesses as they are less likely to be discovered. In this paper, we introduce a personalized divers...

Many recommender systems suffer from popularity bias: popular items are recommended frequently while less popular, niche products, are recommended rarely or not at all. However, recommending the ignored products in the `long tail' is critical for businesses as they are less likely to be discovered. In this paper, we introduce a personalized diversi...

Recommender systems are typically designed to optimize the utility of the end user. In many settings, however, the end user is not the only stakeholder and this exclusive focus may produce unsatisfactory results for other stakeholders. One such setting is found in multisided platforms, which bring together buyers and sellers. In such platforms, it...

Recommender systems research often requires the creation and execution of large numbers of algorithmic experiments to determine the sensitivity of results to the values of various hyperparameters. Existing recommender systems platforms fail to provide a basis for systematic experimentation of this type. In this paper, we describe librec-auto, a wra...

When evaluating recommender systems for their fairness, it may be necessary to make use of demographic attributes, which are personally sensitive and usually excluded from publicly-available data sets. In addition, these attributes are fixed and therefore it is not possible to experiment with different distributions using the same data. In this pap...

Personalized recommendation brings about novel challenges in ensuring fairness, especially in scenarios in which users are not the only stakeholders involved in the recommender system. For example, the system may want to ensure that items from different providers have a fair chance of being recommended. To solve this problem, we propose a Fairness-...

The FairUMAP Workshop at UMAP 2018 brought together researchers working at the intersection of user modeling, adaptation, and personalization on the one hand, and bias and fairness in machine learning on the other hand.

Suppliers of music streaming services are showing an increasing interest for providing users with reliable personalized recommendations since their practically unlimited offerings make it difficult for users to find the music they like. In this work, we take advantage of social tags that users give to music through streaming platforms for improving...

Many recommender systems suffer from the popularity bias problem: popular items are being recommended frequently while less popular, niche products, are recommended rarely if not at all. However, those ignored products are exactly the products that businesses need to find customers for and their recommendations would be more beneficial. In this pap...

In this paper, we summarize VAMS 2017 - a workshop on value-aware and multistakeholder recommendation co-located with RecSys 2017. The workshop encouraged forward-thinking papers in this new area of recommender systems research and obtained a diverse set of responses ranging from application results to research overviews.

Many recommendation algorithms suffer from popularity bias in their output: popular items are recommended frequently and less popular ones rarely, if at all. However, less popular, long-tail items are precisely those that are often desirable recommendations. In this paper, we introduce a flexible regularization-based framework to enhance the long-t...

Recommender systems are personalized information systems. However, in many settings, the end-user of the recommendations is not the only party whose needs must be represented in recommendation generation. Incorporating this insight gives rise to the notion of multistakeholder recommendation, in which the interests of multiple parties are represente...

Much of the focus of recommender systems research has been on the accurate prediction of users' ratings for unseen items. Recent work has suggested that objectives such as diversity and novelty in recommendations are also important factors in the effectiveness of a recommender system. However, methods that attempt to increase diversity of recommend...

Recommender systems are typically evaluated on their ability to provide items that satisfy the needs and interests of the end user. However, in many real world applications, users are not the only stakeholders involved. There may be a variety of individuals or organizations that benefit in different ways from the delivery of recommendations. In thi...

In the information overloaded web, personalized recommender systems are essential tools to help users find most relevant information. The most heavily-used recommendation frameworks assume user interactions that are characterized by a single relation. However, for many tasks, such as recommendation in social networks, user-item interactions must be...

Recent work on machine learning has begun to consider issues of fairness. In this paper, we extend the concept of fairness to recommendation. In particular, we show that in some recommendation contexts, fairness may be a multisided concept, in which fair outcomes for multiple individuals need to be considered. Based on these considerations, we pres...

Recommender systems are key components in information-seeking contexts where personalization is sought. However, the dominant framework for recommendation is essentially two dimensional, with the interaction between users and items characterized by a single relation. In many cases, such as social networks, users and items are joined in a complex we...

In the information overloaded web, personalized recommender systems are essential tools to help users find most relevant information. The most heavily-used recommendation frameworks assume user interactions that are characterized by a single relation. However, for many tasks, such as recommendation in social networks, user-item interactions must be...

In social networks, users and items are joined in a complex web of relations, which can be modeled as heterogeneous information networks. Such networks include a variety of object types and the rich relations among them. Recent research has shown that a hybrid recommendation approach combining components built from extended meta-paths in the networ...

A core value in recommender systems is personalization, the idea that the recommendations produced are those that match the user's preferences. However, in many real-world recommendation contexts, the concerns of additional stake-holders may come into play, such as the producers of items or those of the system owner. Some researchers have examined...

Recommender systems are decision aids that offer users personalized suggestions for products and other items. Context-aware recommender systems are an important subclass of recommender systems that take into account the context in which an item will be consumed or experienced. In context-aware recommendation research, a number of contextual feature...

Recommender systems have been used in many domains to assist users' decision making by providing item recommendations and thereby reducing information overload. Context-aware recommender systems go further, incorporating the variability of users' preferences across contexts, and suggesting items that are appropriate in different contexts. In this p...

Multi-relational matrix factorization is an effective technique for incorporating heterogeneous data into prediction tasks, such as personalized recommendation. Recent research has extended the set of relations that can be applied within heterogeneous network settings by composing non-local relations using network metapaths. One of the key problems...

Context-aware recommender systems (CARS) take context into consideration when modeling user preferences. There are two general ways to integrate context with recommendation: contextual filtering and contextual mod-eling. Currently, the most effective context-aware recommendation algorithms are based on a contextual modeling approach that estimate d...

Recommender system has been demonstrated as one of the most useful tools to assist users' decision makings. Several recommendation algorithms have been developed and implemented by both commercial and open-source recommendation libraries. Context-aware recommender system (CARS) emerged as a novel research direction during the past decade and many c...

Recommender systems help users find items of interest by tailoring their recommendations to users' personal preferences. The utility of an item for a user, however, may vary greatly depending on that user's specific situation or the context in which the item is used. Without considering these changes in preferences, the recommendations may match th...

Context-aware recommender systems (CARS) go beyond traditional recommender systems, that only consider users' profiles, by adapting their recommendations also to users' contextual situations. Several contextual recommendation algorithms have been developed by incorporating context into recommendation algorithms in different ways. The most effective...

Context-aware recommender systems extend traditional rec-ommender systems by adapting their output to users' specific con-textual situations. Most of the existing approaches to context-aware recommendation involve directly incorporating context into standard recommendation algorithms (e.g., collaborative filtering, matrix fac-torization). In this p...

In contrast to traditional recommender systems, context-aware recommender systems (CARS) additionally take context into consideration and try to adapt their recommendations to users' different contextual situations. Several contextual recommendation algorithms have been developed by incorporating context into recommenders in different ways. Most of...

Complex heterogeneous networks contain many types of relations, both local to a particular entity and distant in the network. Multi-relational factorization schemes that incorporate multiple local relations have shown improved recommendation accuracy. This paper extends this prior work on multi-relational factorization to include extended relations...

Context-aware recommender systems (CARS) help improve the ef-fectiveness of recommendations by adapting to users' preferences in different contextual situations. One approach to CARS that has been shown to be particularly effective is Context-Aware Matrix Factorization (CAMF). CAMF incorporates contextual dependen-cies into the standard matrix fact...

Context-aware recommender systems (CARS) take contextual con-ditions into account when providing item recommendations. In recent years, context-aware matrix factorization (CAMF) has e-merged as an extension of the matrix factorization technique that also incorporates contextual conditions. In this paper, we intro-duce another matrix factorization a...

Contextual factors can greatly influence the utility of recommendations for users. In many recommendation and personalization applications, particularly in domains where user context changes dynamically, it is difficult to represent and model contextual factors directly, but it is often possible to observe their impact on user preferences during th...

Social annotation systems have become a staple of the World Wide Web, enabling users to organize their favorite resources with tags. Resource recommenders that exploit these tags to model users and resources have proven to be effective in assisting users navigate complex information spaces, particularly when combined with other approaches. In previ...

Tag recommendation is a fundamental service in today’s social annotation systems, assisting users as they collect and annotate resources. Our previous work has demonstrated the strengths of a linear weighted hybrid, which weights and combines the results of simple components into a final recommendation. However, these previous efforts treated each...

The social web is characterized by a wide variety of connections between individuals and entities. A challenge for recommendation is to represent and synthesize all useful aspects of a user’s profile. Typically, researchers focus on a limited set of relations (for example, person to person ties for user recommendation or annotations in social taggi...

Contextual factors can greatly influence the users' preferences in listening to music. Although it is hard to capture these factors directly, it is possible to see their effects on the sequence of songs liked by the user in his/her current interaction with the system. In this paper, we present a context-aware music recommender system which infers c...

User and item splitting are well-known approaches to context-aware recommendation. To perform item splitting, multiple copies of an item are created based on the contexts in which it has been rated. User splitting performs a similar treatment with respect to user-s. The combination of user and item splitting: UI splitting, splits both users and ite...

An effective technique for recommendation in social media and other heterogeneous networks is the weighted hybrid of low-dimensional components (WHyLDR). Recent studies have shown this technique is comparable to other in-tegrative approaches while being considerably more flexible. One key issue for the implementation of a WHyLDR system is the choic...

Context-aware recommender systems try to adapt to users' pref-erences across different contexts and have been proven to provide better predictive performance in a number of domains. Emotion is one of the most popular contextual variables, but few researcher-s have explored how emotions take effect in recommendations – especially the usage of the em...

Context aware recommender systems go beyond the traditional personalized recommendation models by incorporating a form of situational awareness. They provide recommendations that not only correspond to a user's preference profile, but that are also tailored to a given situation or context. We consider the setting in which contextual information is...

Context-aware recommender systems (CARS) adapt their recommendations to users’ specific situations. In many recommender systems, particularly those based on collaborative filtering, the contextual constraints may lead to sparsity: fewer matches between the current user context and previous situations. Our earlier work proposed an approach called di...

Context-aware recommender systems (CARS) try to adapt their recommendations to users’ specific contextual situations. In many recommender systems, particularly those based on collaborative filtering (CF), the additional contextual constraints may lead to increased sparsity in the user preference data, thus fewer matches between the current user con...

In this paper, we describe a work-in-progress that involves therapists who use commercial motion-based video games (e.g. Wii) in therapies involving patients who have had a brain injury (BI). We are collecting data to inform a case-based recommender (CBR) system that will help therapists stay current and choose appropriate motion-games for their pa...

We consider the problem of personalized text-based music retrieval where users' history of preferences are taken into account in addition to their issued textual queries. Current retrieval methods mostly rely on songs meta-data. This limits the query vocabulary. Moreover, it is very costly to gather this information in large collections of music. A...

The social web is characterized by a wide variety of connections between individuals and entities. A challenge for social web recommendation is make the most effective use of a diverse set of relations. Typically, researchers focus on a limited set of relations (for example, person to person ties for user recommendation or annotations in social tag...

The Social Web makes visible the ebb and flow of popular interest in topics both newsworthy ("GulfSpill") and trivial ("Lolcat"). Understanding this emergent behavior is a fundamental goal for Social Web research. Key problems include discovering emergent topics from online text sources, modeling burst activity, and predicting the future trajectory...

Contextual factors can greatly influence users' decisions in selecting items, such as songs when listening to music. The goal of a context-aware recommender system is to adapt its recommendations not just to the general preferences of users, but also to the context in which users are seeking those recommendations. In the domain of music recommendat...

Which movies you'd like to choose in such two situations: 1). see a movie with kids and 2). see a movie with your girlfriend. Or, which book will you choose as a gift, for your mother and for your kids separately. Apparently, peo-ple usually make different choices or decisions under different situations. In the domain of recommender systems, recomm...

A recommender system is a Web technology that proactively suggests items of interest to users based on their objective behavior or explicitly stated preferences. Evaluations of recommender systems (RS) have traditionally focused on the performance of ...

Context-aware recommendation (CARS) has been shown to be an effective approach to recommendation in a number of domains. However, the problem of identifying appropriate contextual variables remains: using too many contextual variables risks a drastic increase in dimensionality and a loss of accu- racy in recommendation. In this paper, we propose a...

The aim of the Experience Discovery project is to recommend extra- curricular activities to high school and middle school students in urban areas. In implementing this system, we have been able to make use of both usage data and data drawn from a social network- ing site. Using pilot data, we are able to show that very simple aggregation techniques...

Social annotation systems enable the organization of online resources with user-defined keywords. Collectively these annotations provide a rich information space in which users can discover resources, organize and share their finds, and connect to other users with similar interests. However, the size and complexity of these systems can lead to info...

Securing collaborative filtering systems from malicious attack has become an important issue with in-creasing popularity of recommender Systems. Since recom-mender systems are entirely based on the input provided by the users or customers, they tend to become highly vulnerable to outside attacks. Prior research has shown that attacks can significan...

Research in context-aware recommender systems (CARS) usual- ly requires the identification of the influential contextual variables in advance. In collaborative recommendation, there is a substantial trade-off between applying context very strictly and achieving good coverage and accuracy. Our prior work showed that this tradeoff can be managed by a...

Group recommender systems (RS) are used to support groups in making common decisions when considering a set of alternatives. Current approaches generate group recommendations based on the users' individual preferences models. We believe that members of a group can reach an agreement more effectively by exchanging proposals suggested by a convention...

The aim of the Experience Discovery project is to recommend extracurricular activities to high school and middle school students in urban areas. In implementing this system, we have been able to make use of both usage data and data drawn from a social networking site. Using pilot data, we are able to show that very simple aggregation techniques app...

Recommender systems are tools for interacting with large and complex information spaces. They provide a personalized view of such spaces, prioritizing items likely to be of interest to the user. The field, christened in 1995, has grown enormously in the variety of problems addressed and techniques employed, as well as in its practical applications....

Recommender systems are a means of personalizing the presentation of information to ensure that users see the items most relevant to them. The social web has added new dimensions to the way people interact on the Internet, placing the emphasis on user-generated content. Users in social networks create photos, videos, and other artifacts, collaborat...

Recommendation by example is common in contemporary Internet applications providing resources similar to a user-selected example. In this paper this task is considered as a function available within a social annotation system offering new ways to model both users and resources. Using three real-world datasets we motivate several conclusions. First,...

Social annotation systems enable the organization of online resources with user-defined keywords. The size and complexity of these systems make them excellent platforms for the application of recommender systems, which can provide personalized views of complex information spaces. Many researchers have concentrated on the important problem of tag re...

Recommender systems are important building blocks in many of today's e-commerce applications including targeted advertising, personalized mar-keting and information retrieval. In recent years, the importance of contextual information has moti-vated many researchers to focus on designing sys-tems that produce personalized recommendations in accordan...

Recommender systems form an extremely diverse body of technologies and approaches. The chapter aims to assist researchers and developers to identify the recommendation technologies that are most likely to be applicable to different domains of recommendation. Unlike other taxonomies of recommender systems, our approach is centered on the question of...