Markus Zanker

• Doctor of Engineering
• Professor (Full) at Free University of Bozen-Bolzano

Patients with diabetes struggle to control blood glucose with appropriate insulin doses. Classic prediction methods such as multi-linear regression often lead to ineffective blood’s glucose control, because they are not capable to identify the correct neighborhood (other similar patients) of the target patient, since they do not take under consider...

More than twenty-five years ago, first ideas were developed on how to design a system that can provide recommendations to groups of users instead of individual users. Since then, a rich variety of algorithmic proposals were published, e.g., on how to acquire individual preferences, how to aggregate them, and how to generate recommendations for grou...

In the area of recommender systems, the vast majority of research efforts is spent on developing increasingly sophisticated recommendation models, also using increasingly more computational resources. Unfortunately, most of these research efforts target a very small set of application domains, mostly e-commerce and media recommendation. Furthermore...

In the area of recommender systems, the vast majority of research efforts is spent on developing increasingly sophisticated recommendation models, also using increasingly more computational resources. Unfortunately, most of these research efforts target a very small set of application domains, mostly e-commerce and media recommendation. Furthermore...

Many modern online services feature personalized recommendations. A central challenge when providing such recommendations is that the reason why an individual user accesses the service may change from visit to visit or even during an ongoing usage session. To be effective, a recommender system should therefore aim to take the users’ probable intent...

Causality is receiving increasing attention by the artificial intelligence and machine learning communities. This paper gives an example of modelling a recommender system problem using causal graphs. Specifically, we approached the causal discovery task to learn a causal graph by combining observational data from an open-source dataset with prior k...

This paper addresses the critical challenge of predicting future insulin dosages for diabetic patientsin Intensive Care Units (ICUs), a setting where precise glycemic control is crucial. The work proposes aunique methodology that sidesteps the complications associated with traditional glucose-insulin interactionmodels, due to data sparsity. We intr...

Many modern online services feature personalized recommendations. A central challenge when providing such recommendations is that the reason why an individual user accesses the service may change from visit to visit or even during an ongoing usage session. To be effective, a recommender system should therefore aim to take the users' probable intent...

Causality is gaining more and more attention in the machine learning community and consequently also in recommender systems research. The limitations of learning offline from observed data are widely recognized, however, applying debiasing strategies like Inverse Propensity Weighting does not always solve the problem of making wrong estimates. This...

Recommender systems were created to support users in situations of information overload. However, users are consciously or unconsciously influenced by many factors when making decisions, and the recommender must account for these to be effective. In this work, we use a causal graph to investigate the influence of different factors on the user’s dec...

We approached the causal discovery task in the recommender system domain to learn a causal graph by combining observational data provided by a meta-search booking platform for online hotel search with prior knowledge made available by domain experts. The results show that it is possible to learn a causal graph coherent with previous findings in the...

Reproducibility is a main principle in science and fundamental to ensure scientific progress. However, many recent works point out that there are widespread deficiencies for this aspect in the AI field, making the reproducibility of results impractical or even impossible. We therefore studied the state of reproducibility support on the topic of Rei...

Explainability of recommendation algorithms is becoming an important characteristic in GDPR Europe. There are algorithms that try to provide explanations over graphs along with recommendations, but without focusing in user session information. In this paper, we study the problem of news recommendations using a heterogeneous graph and try to infer s...

Purpose – Many incoming requests for quotation usually compete for the attention of accommodation service provider staff on a daily basis, while some of them might deserve more priority than others. Design – This research is therefore based on the correspondence history of a large booking management system that examines the features of quotation re...

Recommender Systems were created to support users in situations of information overload. However, users are consciously or unconsciously influenced by several factors in their decision-making. We analysed a historical dataset from a meta-search booking platform with the aim of exploring how these factors influence user choices in the context of onl...

Post-harvest diseases are one of the main causes of economical losses in the apple fruit production sector. Therefore, this paper presents an application of a knowledge-based expert system to diagnose post-harvest diseases of apple. Specifically, we detail the process of domain knowledge elicitation for constructing a Bayesian network reasoning sys...

There exist many research works that strive to answer the question “what news article is a user going to click next given his profile”. These works take into account the time dimension to reveal users’ preferences over time. However, few works exploit adequately the information that is hidden inside user sessions. User sessions include a list of us...

Medicine recommendation denotes the task of predicting drug combinations for patients’ therapies in case of complex diseases such as cancer or diabetes. These patients often follow a treatment consisting of multiple drugs simultaneously. Previous research works have already made predictions of next drug combinations based on the integration of the...

Recommender systems (RSs), as used by Netflix, YouTube, or Amazon, are one of the most compelling success stories of AI. Enduring research activity in this area has led to a continuous improvement of recommendation techniques over the years, and today's RSs are indeed often capable to make astonishingly good suggestions. With countless papers being...

Post-harvest diseases of apple can cause considerable economic losses. Thus, we developed DSSApple, an interactive web-based decision support system, that helps users to diagnose post-harvest diseases of domesticated apple based on observed macroscopic symptoms on fruit. Specifically, DSSApple is designed as a two-stream hybrid diagnostic tool, tha...

Recommender systems have been successfully applied to alleviate overloaded information and assist decision making in various domains and applications. Recently, several new research directions emerged and novel techniques were proposed to advance the development of recommender systems. In this special issue, we invited authors to submit the revised...

Since recommender systems play an important role in our online experience today and are involved in a wide range of decisions, multiple stakeholders are requesting explanations for the corresponding algorithmic predictions. These demands—together with the benefits of explanations (e.g., trust, efficiency, and sometimes even persuasion)—have trigger...

In this report, we offer a brief overview of the contributions and takeaways from the Joint KaRS & ComplexRec Workshop, co-located with the 15 th edition of the ACM RecSys in Amsterdam, The Netherlands. With this workshop, we aimed to merge the main objectives envisioned for the 3 rd Edition of the Workshop of Knowledge-aware and Conversational Rec...

This article presents the development of an expert system to support the diagnosis of post-harvest diseases of stored apples. We propose a picture-based and conversational interaction with users, where sampled images depicting symptoms of apples with known diseases are presented to users to elicit their feedback on perceived similarities in order t...

Recommender systems in news industry use the time dimension to reveal users’ preferences over time, but they miss to exploit adequately the information encapsulated inside user sessions. Here, we combine intra- with inter-session item transition probabilities to reveal the short- and long-term intentions of individuals along with the public prefere...

This paper presents a case-study of a knowledge-based recommender system capable to diagnose post-harvest diseases of apples. It describes the process of knowledge elicitation and construction of a Bayesian Network reasoning system as well as its evaluation with three different types of studies involving diseased apples. The ground truth of disease...

The origins of modern recommender systems date back to the early 1990s when they were mainly applied experimentally to personal email and information filtering. Today, 30 years later, personalized recommendations are ubiquitous and research in this highly successful application area of AI is flourishing more than ever. Much of the research in the l...

The global response to the pandemic introduced by COVID-19 is unprecedented. Scientists develop methods, which analyze data to identify an effective treatment that uncovers possible responses to the SARS-COV-2 virus. However, our global response should be based on knowledge exchange and collaboration among countries. In this paper, we present a rec...

Recommender systems are among the most widespread applications of artificial intelligence techniques. For instance, news recommender systems serve users in managing the overload of information they come across when accessing news portals. Obviously, in the news domain time-awareness of recommendation approaches are crucial. However, most of these a...

The Multi-Armed Bandit (MAB) problem has been extensively studied in order to address real-world challenges related to sequential decision making. In this setting, an agent selects the best action to be performed at time-step t, based on the past rewards received by the environment. This formulation implicitly assumes that the expected payoff for e...

Post-harvest diseases of apple are one of the major issues in the economical sector of apple production, causing severe economical losses to producers. Thus, we developed DSSApple, a picture-based decision support system able to help users in the diagnosis of apple diseases. Specifically, this paper addresses the problem of sequentially optimizing...

Traditional news media Web sites usually provide generic recommendations that are not personalized to the preferences of their users. Typically, news recommendation algorithms mainly rely on the long-term preferences of users and do not adjust their model to the continuous stream of short-lived incoming stories to capture short-term intentions reve...

The capability of a recommendation system to justify its proposals becomes an ever more important aspect in light of recent legislation and skeptic users. Answer Set Programming (ASP) is a logic programming paradigm aiming at expressing complex problems in a succinct and declarative manner. Due to its rich set of high level language constructs it t...

This viewpoint article argues that the impacts of the novel coronavirus COVID-19 call for transformative e-Tourism research. We are at a crossroads where one road takes us to e-Tourism as it was before the crisis, whereas the other holds the potential to transform e-Tourism. To realize this potential, e-Tourism research needs to challenge existing...

Rating summary statistics are basic aggregations that reflect users’ assessments of experienced products and services in numerical form. Thus far, scholars primarily investigated textual reviews, but dedicated considerably less time and effort exploring the potential impact of plain rating summary statistics on people’s choice behavior. Notwithstan...

The Workshop on Recommenders in Tourism (RecTour) 2019, which is held in conjunction with the 13th ACM Conference on Recommender Systems (RecSys), addresses specific challenges for recommender systems in the tourism domain. In this overview paper, we summarize our motivations to organize the RecTour workshop and present the main topics of the submi...

Collaborative filtering techniques have been studied extensively during the last decade. Many open source packages (Apache Mahout, LensKit, MyMediaLite, rrecsys etc.) have implemented them, but typically the top-N recommendation lists are only based on a highest predicted ratings approach. However, exploiting frequencies in the user/item neighborho...

Postharvest diseases are a common problem in the cultivation of apples. Since determining the exact disease is often difficult and time-consuming, an ontology-based decision support system is being developed to support this diagnosis process. In this paper a reference ontology is presented that captures the life-cycle of apples from the orchard to...

The search for unfamiliar experiences and novelty is one of the main drivers behind all human activities, equally important with harm avoidance and reward dependence. A recommender system personalizes suggestions to individuals to support and guide them in their exploration tasks. Personalization mechanisms and recommender systems limit serendipit...

Recommendation systems personalise suggestions to individuals to help them in their decision making and exploration tasks. In the ideal case, these recommendations, besides of being accurate, should also be novel and explainable. However, up to now most platforms fail to provide both, novel recommendations that advance users' exploration along with...

Research on understanding, developing and assessing personalisation systems is spread over multiple disciplines and builds on methodologies and findings from several different research fields and traditions, such as Artificial Intelligence (AI), Machine Learning (ML), Human–Computer Interaction (HCI), and User Modelling based on (applied) social an...

Recommendation systems personalise suggestions to individuals to help them in their decision making and exploration tasks. In the ideal case, these recommendations, besides of being accurate, should also be novel and explainable. However, up to now most platforms fail to provide both, novel recommendations that advance users’ exploration along with...

Rating-based summary statistics are ubiquitous in e-commerce, and often are crucial components in personalized recommendation mechanisms. Especially visual rating summarizations have been identified as important means to explain, why an item is presented or proposed to an user. Largely left unexplored, however, is the issue to what extent the descr...

This chapter builds on a dataset where online users of a spa platform participated in an online idea contest providing free-text descriptions of their proposals for spa services. A panel of domain experts annotated these idea descriptions with a score for their innovativeness that serves as ground truth for machine learning experiments. Thus, the c...

Rating-based summary statistics have become ubiquitous, and of key relevance to compare offers on booking platforms. Largely left unexplored, however, is the issue to what extent the descriptives of rating distributions influence the decision making of online consumers. In this work a conjoint experiment was eye-tracked to explore how different att...

The search for unfamiliar experiences and novelty is one of the main drivers behind all human activities, equally important with harm avoidance and reward dependence. A recommender system personalizes suggestions to individuals to help them in their exploration tasks. In the ideal case, these recommendations, except of being accurate, should be als...

We drew on revised Reinforcement Sensitivity Theory to claim that users with an anxiety-related behavioral inhibition would experience proactively delivered recommendations as potential threats. Such users would display higher user engagement especially when they were interrupted by inaccurate (vs. accurate) recommendations, because they ruminate a...

The present research explored to what extent user engagement in proactive recommendation scenarios is influenced by the accuracy of recommendations, concerns with information privacy, and trait personality. We hypothesized that people’s self-reported information privacy concerns would matter more when they received accurate (vs. inaccurate) proacti...

The Workshop on Recommenders in Tourism (RecTour) 2018, which is held in conjunction with the 12th ACM Conference on Recommender Systems (RecSys), addresses specific challenges for recommender systems within the tourism domain. In this paper, we summarize our motivations to organize this workshop and give an overview of the submissions that we rece...

More and more precise and powerful recommendation algorithms and techniques have been proposed over the last years able to effectively assess users' tastes and predict information that would probably be of interest for them. Most of these approaches rely on the collaborative paradigm (often exploiting machine learning techniques) and do not take in...

This demo presents a conversational navigation approach for a diagnostic application of postharvest diseases of apple with the goal to educate users on the diagnosed diseases as well as to recommend consequences for the storage facility and what action to take for the next growing period. It thus builds on earlier works on picture-based navigation...

Collaborative filtering systems heavily depend on user feedback expressed in product ratings to select and rank items to recommend. These summary statistics of rating values carry two important descriptors about the assessed items, namely the total number of ratings and the mean rating value. In this study we explore how these two signals influence...

Collaborative filtering systems heavily depend on user feedback expressed in product ratings to select and rank items to recommend. In this study we explore how users value different collaborative explanation styles following the user-based or item-based paradigm. Furthermore, we explore how the characteristics of these rating summarizations, like...

Collaborative filtering techniques have been studied extensively during the last decade. Many open source packages (Apache Mahout, LensKit, MyMediaLite, rrecsys etc.) have them implemented, but typically the top-N recommendation lists are only based on a highest predicted ratings approach. However, exploiting frequencies in the user/item neighbourh...

Rating-based summary statistics are ubiquitous in e-commerce, and often are crucial components in personalized recommendation mechanisms. Largely left unexplored, however, is the issue to what extent the descriptives of rating distributions influence the decision making of online consumers. We conducted a conjoint experiment to explore how differen...

Recommender systems have shown to be valuable tools for filtering, ranking, and discovery in a variety of application domains such as e-commerce, media repositories or document-based information in general that includes the various scenarios of Social Information Access discussed in this book. One key to the success of such systems lies in the prec...

Collaborative filtering systems heavily depend on user feedback expressed in product ratings to select and rank items to recommend. In this study we explore how users value different collaborative explanation styles following the user-based or item-based paradigm. Furthermore, we explore how the characteristics of these rating summarizations, like...

In the context of news recommendations, many time-aware approaches were proposed. These approaches have tried to capture the recency of news with respect to their short life span, by using either decaying weights on past articles or even forgetting them. However, most of these approaches have missed to consider sessions, which encapsulate inside th...

rrecsys is a novel library in R for developing and assessing recommendation algorithms. In this demo, we extend rrecsys with functions for visual analytics of recommendation performance, that is one of the strong capabilities of the R environment. In particular, we show how the library can be used to depict dataset characteristics, train and test r...

The deep learning wave is propagating through many research areas and communities. In the last years it quickly propagated to Recommendation Systems, a research area which aims to recommend items to users. Indeed, many deep learning models and architectures have been proposed for Recommendation Systems to improve collaborative filtering and content...

Route planning applications are digital companions of many travelers, enabling them to experience different locations in a self-determined way. This work surveys the variety of commercial applications and research prototypes for itinerary planning with the goal of evaluating the interactivity aspect of these tools. For this purpose a framework to c...

We drew on revised Reinforcement Sensitivity Theory to claim that users with an anxiety-related behavioral inhibition would experience proactive-ly delivered recommendations as potential threats. Such users would display higher user engagement especially when they were interrupted by inaccurate (vs. accurate) recommendations, because they ruminate...

Recommender systems (RS) are a real-world application domain for Artificial Intelligence standing at the core of massively used e-commerce and social-media platforms like Amazon, Netflix, Spotify and many more. The research field of recommendation systems now has already a more than 20 years long tradition and issues like replication of results and...

The goal of the present study was to investigate how satisfied individuals are with the final outcome of a group decision-making process on a joint travel destination. Using an experimental paradigm (N total = 200, N groups = 55) it was obvious to hypothesize that individuals would especially be satisfied with the final group decision when it match...

Recommender systems have become a natural part of the user experience in today's online world. These systems are able to deliver value both for users and providers and are one prominent example where the output of academic research has a direct impact on the advancements in industry. In this article, we have briefy reviewed the history of this mult...

Most research on group recommender systems relies on the assumption that individuals have conflicting preferences; in order to generate group recommendations the system should identify a fair way of aggregating these preferences. Both empirical studies and theoretical frameworks have tried to identify the most effective preference aggregation techn...

Most evaluations of novel algorithmic contributions assess their accuracy in predicting what was withheld in an offline evaluation scenario. However, several doubts have been raised that standard offline evaluation practices are not appropriate to select the best algorithm for field deployment. The goal of this work is therefore to compare the offl...

User generated content in general and textual reviews in particular constitute a vast source of information for the decision making of tourists and management and are therefore a key component for e-tourism. This paper provides a description of the topic model method with a particular application focus on the tourism domain. It therefore contribute...

In recent years mobile devices became the way of choice for consuming digital content. One of the areas in which this monumental shift has a great impact that creates a lot of opportunities and challenges is tourism. Proactive computing comes with the promise of providing a more engaging user experience. However, due to many practical challenges in...

User generated content in general and textual reviews in particular constitute a vast source of information for the decision making of tourists and management and are therefore a key component for e-tourism. This paper explores different application scenarios for the topic model method to process these textual reviews in order to provide accurate d...

Recommender systems provide valuable support for users who are searching for products in e-commerce environments. Research in the field long focused on rating-based algorithms supporting the recommendation of quality and taste products such as news, books, or movies. The recommendation of more complex products such as financial services or electron...