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We studied an alternative choice-based interface for preference elicitation during the cold start phase and compared it directly with a standard rating-based interface. In this alternative interface users started from a diverse set covering all movies and iteratively narrowed down through a matrix factorization latent feature space to smaller sets of items based on their choices. The results show that compared to a rating-based interface, the choice-based interface requires less effort and results in more satisfying recommendations, showing that it might be a promising candidate for alleviating the cold start problem of new users.

South Tyrol (northern Italy) produces around one million metric tons of apple fruit annually and contributes to almost 10% of the European apple harvest. Despite the availability of advanced storage technologies, postharvest diseases of apple may lead to the deterioration of quality and losses of fruit not only during the storage process but also in the course of packing, shipment and shelf-life. In order to decide on a strategy for damage control or to implement plant protection programs, it is crucially important to reliably determine the nature of plant diseases. Apart from the observation of macroscopic symptoms and the application of laboratory-dependent microscopic, microbiological and molecular methods, a variety of novel approaches for the detection of plant diseases and pathogens is being developed. The present study focuses on the potential of information technology to bring the diagnosis process closer to practitioners. Particular attention is paid to the application of computer vision techniques and use of decision support systems. We provide a review of existing implementations of computer-based tools to diagnose plant diseases and present an outline of the “DSSApple” research project, which focuses on the development of a decision support system for the determination of postharvest diseases of apples.

This paper describes a decision support system to fulfill the diagnosis of post-harvest diseases in apple fruit. The diagnostic system builds on user feedback elicitation via multiple conversational rounds. The interaction with the user is conducted by selecting and displaying images depicting symptoms of diseased apples. The system is able to adapt the image selection mechanism by exploiting previous user feedback through a contextual multi-armed bandit approach. We performed a large scale user experiment, where different strategies for image selection have been compared in order to identify which reloading strategy makes users more effective in the diagnosis task. Concretely, we compared contextual multi-armed bandit methods with two baseline strategies and identified that the exploration-exploitation principle significantly paid off in comparison to a greedy and to a random stratified selection strategy. Although the application context is very domain specific, we believe that the general methodology of conversational item selection for eliciting user preferences applies to other scenarios in decision support and recommendation systems.

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 for conversational recommender systems and provides evidence for its usability based on a first small-scale comparative usability study. © 2018 Copyright held by the owner/author(s)

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 based algorithms. In this paper we propose a hybrid recommendation system combining user ratings and natural language text processing to solve the 0/1 recommendation problem. In particular, we describe a deep learning architecture combining two information sources, namely natural language text and user rating. Natural language text is used to learn a user-specific content-based classifier, while user ratings are used to develop user-adaptive collaborative filtering recommendations. We perform numerical experiments on MovieLens 1M and reach first preliminary, but promising results, showing the proposed architecture has the potential to combine content-based and collaborative filtering recommendation mechanisms using a deep learning supervisor.