Decision Support Systems (DSSs) are used in precision agriculture to provide feedback to a variety of stakeholders, including farmers, advisers, researchers and policymakers. However, increments in the amount of data might lead to data quality issues, and as these applications scale into big, real-time monitoring systems the problem gets even more challenging. Visualisation is a powerful technique used in these systems that provides an indispensable step in assisting end-users to understand and interpret the data. In this paper, we present a systematic review to synthesise literature related to the use of visualisation techniques in the domain of agriculture. The search identified 61 eligible articles, from which we established end-users, visualisation techniques and data collection methods across different application domains. We found visualisation techniques used in various areas of agriculture, including viticulture, dairy farming, wheat production and irrigation management. Our results show that the majority of DSSs utilise maps, together with satellite imagery, as the central visualisation. Also, we observed that there is an excellent opportunity for dashboards to enable end-users with better interaction support to understand the uncertainty of data. Based on this analysis, we provide design guidelines towards the implementation of more interactive and visual DSSs.

Background Health recommender systems (HRSs) offer the potential to motivate and engage users to change their behavior by sharing better choices and actionable knowledge based on observed user behavior. Objective We aim to review HRSs targeting nonmedical professionals (laypersons) to better understand the current state of the art and identify both the main trends and the gaps with respect to current implementations. Methods We conducted a systematic literature review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and synthesized the results. A total of 73 published studies that reported both an implementation and evaluation of an HRS targeted to laypersons were included and analyzed in this review. Results Recommended items were classified into four major categories: lifestyle, nutrition, general health care information, and specific health conditions. The majority of HRSs use hybrid recommendation algorithms. Evaluations of HRSs vary greatly; half of the studies only evaluated the algorithm with various metrics, whereas others performed full-scale randomized controlled trials or conducted in-the-wild studies to evaluate the impact of HRSs, thereby showing that the field is slowly maturing. On the basis of our review, we derived five reporting guidelines that can serve as a reference frame for future HRS studies. HRS studies should clarify who the target user is and to whom the recommendations apply, what is recommended and how the recommendations are presented to the user, where the data set can be found, what algorithms were used to calculate the recommendations, and what evaluation protocol was used. Conclusions There is significant opportunity for an HRS to inform and guide health actions. Through this review, we promote the discussion of ways to augment HRS research by recommending a reference frame with five design guidelines.

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Recommender systems have been increasingly used in online services that we consume daily, such as Facebook, Netflix, YouTube, and Spotify. However, these systems are often presented to users as a "black box", i.e. the rational for providing individual recommendations remains unexplained to users. In recent years, various attempts have been made to address this black box issue by providing textual explanations or interactive visualisations that enable users to explore the provenance of recommendations, and benefits in terms of precision and user satisfaction, among others, have been demonstrated. Previous research had also indicated that personal characteristics such as domain knowledge, trust propensity and persistence may also play an important role on such perceived benefits. Yet, to date, little is known about the effects of personal characteristics when explaining recommendations. To address this gap, we developed a music recommender system with explanations and conducted an online study using a within-subject design. We captured various personal characteristics of participants and administered both qualitative and quantitative evaluation methods. Results indicated that personal characteristics have some significant influence on the interaction and perception of recommender systems and that this influence changes by adding explanations. Especially people with a low need for cognition benefited from explained recommendations. For people with a high need for cognition, we observed that explanations could lower their confidence. Based on these results, we present some first design implications for explaining recommendations.

The majority of research into Collaborative Information Retrieval (CIR) has assumed a uniformity of information access and visibility between collaborators. However in a number of real world scenarios, information access is not uniform between all collaborators in a team e.g. security, health etc. This can be referred to as Multi-Level Collaborative Information Retrieval (MLCIR). To the best of our knowledge, there has not yet been any systematic investigation of the effect of MLCIR on search outcomes. To address this shortcoming, in this paper, we present the results of a simulated evaluation conducted over 4 different non-uniform information access scenarios and 3 different collaborative search strategies. Results indicate that there is some tolerance to removing access to the collection and that there may not always be a negative impact on performance. We also highlight how different access scenarios and search strategies impact on search outcomes.