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 precise acquisition or estimation of the user’s preferences. While general recommender systems research often relies on the existence of explicit preference statements for personalization, such information is often very sparse or unavailable in real-world applications. Information that allows us to assess the relevance of certain items indirectly through a user’s actions and behavior (implicit feedback) is in contrast often available in abundance. In this chapter we categorize different types of implicit feedback and review their use in the context of recommender systems and Social Information Access applications. We then extend the categorization scheme to be suitable to recent application domains. Finally, we present state-of-the-art algorithmic approaches, discuss challenges when using implicit feedback signals in particular with respect to popularity biases, and discuss selected recent works from the literature.

Recommender systems are among the most visible applications of intelligent systems technology in practice and are used to help users find items of interest, for example on e-commerce sites, in a personalized way. While past research has focused mainly on accurately predicting the relevance of items that are unknown to the user, other quality criteria for recommendations have been investigated in recent years, including diversity, novelty, or serendipity. Considering these additional factors, however, often leads to the following two challenges. First, in many application domains, trade-offs like "diversity vs. accuracy" have to be balanced. Second, it is not always clear how much diversity or novelty is desirable in practice.In this work, we propose a novel parameterizable optimization scheme that re-ranks accuracy-optimized recommendation lists in order to cope with these challenges. Our method is both capable of considering multiple optimization goals at the same time and designed to consider individual user tendencies regarding the different quality factors, like diversity. In contrast to previous work, the method is not restricted to a specific underlying item ranking algorithm and its generic design allows the algorithm to be parameterized according to the requirements of the application domain. Experimental evaluations with different datasets show that balancing the quality factors with our method can be done with a marginal or no loss in ranking accuracy. Given that our method can be applied in various domains and within the narrow time constraints of online recommendation, our work opens new opportunities to design novel finer-grained personalization approaches in practical applications. (C) 2017 Elsevier Ltd. All rights reserved.

Research in the field of recommender systems is largely based on offline experimentation on historical datasets. Several recent works however suggest that models optimized for accuracy measures are not necessarily those that lead to the best user experience or perceived system utility. In this work we first determine the offline performance of different algorithms in the domain of video game recommendation and then investigate the perceived recommendation quality through a user study. The offline results show that learning-to-rank methods optimized for implicit feedback situations as expected perform best in terms of accuracy, where higher accuracy often comes with a stronger tendency of the algorithms to recommend mostly popular items. In the user study, however, methods that also consider the similarity between items in their algorithms perform at least equally well in terms of accuracy, which could not be expected from the offline experiment. Such content-enhanced methods were also slightly favored by users in terms of perceived transparency.

Many e-commerce sites present additional item recommendations to their visitors while they navigate the site, and ample evidence exists that such recommendations are valuable for both customers and providers. Academic research often focuses on the capability of recommender systems to help users discover items they presumably do not know yet and which match their long-term preference profiles. In reality, however, recommendations can be helpful for customers also for other reasons, for example, when they remind them of items they were recently interested in or when they point site visitors to items that are currently discounted. In this work, we first adopt a systematic statistical approach to analyze what makes recommendations effective in practice and then propose ways of operationalizing these insights into novel recommendation algorithms. Our data analysis is based on log data of a large e-commerce site. It shows that various factors should be considered in parallel when selecting items for recommendation, including their match with the customer's shopping interests in the previous sessions, the general popularity of the items in the last few days, as well as information about discounts. Based on these analyses we propose a novel algorithm that combines a neighborhood-based scheme with a deep neural network to predict the relevance of items for a given shopping session.

Die passende Musik fur einen gewunschten Anwendungszweck auszuwahlen, etwa fur eine Wiedergabeliste fur Hintergrundmusik oder als Untermalung in einem Werbespot, ist aufgrund von verschiedensten Anforderungen und der schieren Menge an verfugbaren Stucken ein aufwandiger Prozess. Es existieren zahlreiche Kriterien, beispielsweise Metadaten, aber auch die Beschaffenheit der Musik selbst, anhand derer ein Stuck charakterisiert werden kann. Mithilfe von Empfehlungssystemen – speziellen Algorithmen, die Elemente anhand festgelegter Kriterien auswahlen konnen – lasst sich dieser Prozess vereinfachen und teilweise automatisieren. Ihre Daten beziehen solche Systeme oft aus sogenannten Musikdatenbanken, die Informationen uber Musikstucke aggregieren und kategorisieren, und damit die Moglichkeit bieten, Titel nach verschiedenen Kriterien zu finden, dem Anwendungszweck gemas auszuwahlen und oft auch direkt zu erwerben oder abzuspielen. In diesem Kapitel wird das Problem der automatisierten Erstellung von Wiedergabelisten charakterisiert sowie algorithmische Ansatze im Uberblick vorgestellt. Anschliesend wird eine Ubersicht uber aktuelle Online-Musikdatenbanken gegeben.

Many e-commerce sites present additional item recommendations to their visitors while they navigate the site, and ample evidence exists that such recommendations are valuable for both customers and...

The automated generation of playlists given a user's last played tracks is a common feature of modern music platforms. Existing approaches to this "next-track music recommendation" problem often focus solely on the user's recent listening behavior or current situational context and do not consider long-term preferences. In this work, we explore the value of including different types of information that reflect long-term preferences into the playlist generation process. Although empirical evaluations show that the most recently played tracks should generally govern the next-track selection process, considering long-term preferences can help to improve the quality of the playlists in different dimensions.1

The paper was selected based on nominations from journal reviewers, editorial board members and guest editors, and a subsequent comparative review of the shortlisted papers by an award committee. The James Chen Annual Award for Best Journal Article has been donated by the Chen family in commemoration of James R. Chen, a creative researcher in the area of user modeling and information retrieval, and twice a UMUAI author. The award carries a cash price of US$ 1,000.

Most research in recommender systems is focused on the problem of identifying and ranking items that are relevant for the individual users but unknown to them. The potential value of such systems is to help users discover new items, e.g., in e-commerce settings. Many real-world systems however also utilize recommendation lists for a different goal, namely to remind users of items that they have viewed or consumed in the past. In this work, we aim to quantify the value of such reminders in recommendation lists ("recominders"), which has to our knowledge not been done in the past. We first report the results of a live experiment in which we applied a naive reminding strategy on an online platform and compare them with results obtained through different offline analyses. We then propose more elaborate reminding techniques, which aim to avoid reminders of too obvious or of already outdated items. Overall, our results show that although reminders do not lead to new item discoveries, they can be valuable both for users and service providers.

The automated generation of playlists given a user’s most recent listening history is a common feature of modern music streaming platforms. In the research literature, a number of algorithmic proposals for this “next-track recommendation” problem have been made in recent years. However, nearly all of them are based on the user’s most recent listening history, context, or location but do not consider the users’ long-term listening preferences or social network. In this work, we explore the value of long-term preferences for personalizing the playlist generation process and evaluate different strategies of applying multi-dimensional user-specific preference signals. The results of an empirical evaluation on five different datasets show that although the short-term listening history should generally govern the next-track selection process, long-term preferences can mea-surably help to increase the personalization quality.

Machine learning and data analytics tasks in practice require several consecutive processing steps. RapidMiner is a widely used software tool for the development and execution of such analytics workflows. Unlike many other algorithm toolkits, it comprises a visual editor that allows the user to design processes on a conceptual level. This conceptual and visual approach helps the user to abstract from the technical details during the development phase and to retain a focus on the core modeling task. The large set of preimplemented data analysis and machine learning operations available in the tool, as well as their logical dependencies, can, however, be overwhelming in particular for novice users. In this work, we present an add-on to the RapidMiner framework that supports the user during the modeling phase by recommending additional operations to insert into the currently developed machine learning workflow. First, we propose different recommendation techniques and evaluate them in an offline setting using a pool of several thousand existing workflows. Second, we present the results of a laboratory study, which show that our tool helps users to significantly increase the efficiency of the modeling process. Finally, we report on analyses using data that were collected during the real-world deployment of the plug-in component and compare the results of the live deployment of the tool with the results obtained through an offline analysis and a replay simulation.

Laboratory studies are a common way of comparing recommendation approaches with respect to dierent quality dimensions that might be relevant for real users. One typical experimental setup is to rst present the participants with recommendation lists that were created with dierent algorithms and then ask the participants to assess these recommendations individually or to compare two item lists. The cognitive eort required by the participants for the evaluation of item recommendations in such settings depends on whether or not they already know the (features of the) recommended items. Furthermore, lists containing popular and broadly known items are correspondingly easier to evaluate. In this paper we report the results of a user study in which participants recruited on a crowdsourcing platform assessed system-provided recommendations in a between-subjects experimental design. The results surprisingly showed that users found non-personalized recommendations of popular items the best match for their preferences. An analysis revealed a measurable correlation between item familiarity and user acceptance. Overall, the observations indicate that item familiarity can be a potential confounding factor in such studies and should be considered in experimental designs.

An essential characteristic in many e-commerce settings is that website visitors can have very specific short-term shopping goals when they browse the site. Relying solely on long-term user models that are pre-trained on historical data can therefore be insufficient for a suitable next-basket recommendation. Simple "real-time" recommendation approaches based, e.g., on unpersonalized co-occurrence patterns, on the other hand do not fully exploit the available information about the user's long-term preference profile. In this work, we aim to explore and quantify the effectiveness of using and combining long-term models and short-term adaptation strategies. We conducted an empirical evaluation based on a novel evaluation design and two real-world datasets. The results indicate that maintaining short-term content-based and recency-based profiles of the visitors can lead to significant accuracy increases. At the same time, the experiments show that the choice of the algorithm for learning the long-term preferences is particularly important at the beginning of new shopping sessions.

Abstract User studies play an important role in academic research in the field of recommender systems as they allow us to assess quality factors other than the predictive accuracy of the underlying algorithms. User satisfaction is one such factor that is often evaluated in laboratory settings and in many experimental designs one task of the participants is to assess the suitability of the system-generated recommendations. The effort required by the user to make such an assessment can, however, depend on the user’s familiarity with the presented items and directly impact on the reported user satisfaction. In this paper, we report the results of a preliminary recommender systems user study using Mechanical Turk, which indicates that item familiarity is strongly correlated with overall satisfaction.

Automated playlist generation is a special form of music recommendation and a common feature of digital music playing applications. A particular challenge of the task is that the recommended items should not only match the general listener's preference but should also be coherent with the most recently played tracks. In this work, we propose a novel algorithmic approach and optimization scheme to generate playlist continuations that address these requirements. In our approach, we first use collections of shared music playlists, music metadata, and user preferences to select suitable tracks with high accuracy. Next, we apply a generic re-ranking optimization scheme to generate playlist continuations that match the characteristics of the last played tracks. An empirical evaluation on three collections of shared playlists shows that the combination of different input signals helps to achieve high accuracy during track selection and that the re-ranking technique can both help to balance different quality optimization goals and to further increase accuracy.

RapidMiner is a software framework for the development and execution of data analysis workflows. Like many modern software development environments, the tool comprises a visual editor which allows the user to design processes on a conceptual level, thereby abstracts technical details, and thus helps the user focus on the core modeling task. The large set of pre-implemented data analysis operations available in the framework, as well as their logical dependencies, can, however, be overwhelming in particular for novice users. In this work we present an intelligent add-on to the RapidMiner framework that supports the user during the modeling phase by recommending additional operations to insert into the currently developed data analysis workflow. In the paper, we first propose different recommendation techniques and evaluate them in an offline setting using a pool of several thousand existing workflows. Second, we present the results of a laboratory study, which show that our tool helps users to significantly increase the efficiency of the modeling process.