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A Systematic Review of the Impact of Auxiliary Information on Recommender SystemsMatthew Ayemowa · 2024Recommender systems are essential tools that provide personalized user experiences across various domains such as e-commerce, entertainment, social media, education and content streaming. The integration of auxiliary information, including user demographics, item attributes, and contextual data has shown significant promise in enhancing the performance of recommender systems. This systematic review investigates the impact of incorporating auxiliary information into various types of recommender systems, examining recent advancements, methodologies, datasets, evaluation metrics, and to equally examine its significance on generative artificial intelligence. Similarly, five (5) reputable online databases were used to identify the relevant studies for answering our research questions. To obtain effective results of our findings, we focus more on the recent studies published between (2019 - June 2024) to ensure that of our findings up-to-date. After filtering the selected primary papers that solely focused on auxiliary information recommender systems a total of 37 papers were identified and analyzed. Our analysis shows the most utilized datasets, metrics, models, addressed issues and future works. Research limitations and future scope are also highlighted to assist researchers and practitioners for their future studies. INDEX TERMS Recommender systems, auxiliary information, data sparsity, cold start problem.

UX of AI & PersonalizationResearch Papers

Adaptive user interface for workflow-ERP systemMarcin Smereka, Grzegorz Kołaczek et al. · 2023In this paper, the problem of user interface recommendations for workflow management systems is investigated. The user interface is automatically adapted using a software tool based on content-based filtering. This tool collects information about the way processes are carried out in an organization, analyzes and processes the data, and recommends the next action to the user in order to increase efficiency, facilitate training, and improve decision-making. The proposed tool was verified in the real environment within three organizations. For each organization, after at least several weeks of learning, the tool was able to offer suggestions that were selected by real users. 2382 Marcin Smereka et al. / Procedia Computer Science 225 (2023) 2381–2391 The aim of the project carried out by Sente was to investigate the possibility of automatically analyzing user behavior in business software (especially ERP class) in order to automatically adapt the way processes are carried out to these behaviors using content-based filtering algorithms. A tool that would be able to collect information about the way processes are carried out in an organization, analyze and process the collected data and recommend the next action to the user could increase the efficiency of using the ERP system, and facilitate training and decision-making processes. 2. State of the art According to Kobsa [7], proper user model selection should be the starting point when designing the user inter- face. However, with the growing number of potential users, particularly in web-based systems, the differences among them are also increasing. Consequently, the user model is becoming more complex, resulting in a highly differentiated proposed user interface, such as in interaction styles. [14]. The user differences may reflex their demographical, psy- chological, sociological, and anthropological user characteristics, which have an influence on the users’ information needs, interaction habits, and potential limitations of the interaction systems usage. The consequence of these differ- ences is causing difficulties in modeling these users in the standard way [7], in a result more sophisticated solutions have been proposed, for example, one enhanced with the application of ontologies [12]. The ontologies may be used to enhance information systems design and development on at least several different levels, such as database integration, business logic, or Graphical User Interfaces (GUI) [8]. User interfaces have been also ado

UX of AI & PersonalizationResearch Papers

Moodplay: Interactive Music Recommendation Based on Artists' Mood SimilarityIvana Andjelkovic, Denis Parra et al. · 2018A large amount of research in recommender systems focuses on algorithmic accuracy and optimization of ranking metrics. However, recent work has unveiled the importance of other aspects of the recommendation process, including explanation, transparency, control and user experience in general. Building on these aspects, this paper introduces MoodPlay , an interactive music-artists recommender system which integrates content and mood-based filtering in a novel interface. We show how MoodPlay allows the user to explore a music collection by musical mood dimensions, building upon GEMS, a music-specific model of affect, rather than the traditional Circumplex model. We describe system architecture, algorithms, interface and interactions followed by use-case and offline evaluations of the system, providing evidence of the benefits of our model based on similarities between the typical moods found in an artist’s music, for contextual music recommendation. Finally, we present results of a user study (N = 279) in which four versions of the interface are evaluated with varying degrees of visualization and interaction. Results show that our proposed visualization of items and mood information improves user acceptance and understanding of both the underlying data and the recommendations. Furthermore, our analysis reveals the role of mood in music recommendation, considering both artists’ mood and users’ self-reported mood in the user study. Our results and discussion highlight the impact of visual and interactive features in music recommendation, as well as associated human-cognitive limitations. This research also aims to inform the design of future interactive recommendation systems.

UX of AI & PersonalizationResearch Papers