Design Of Recommendation System Research Articles

Investigating the working principle of the recommending system and designing a personal music recommending system

This paper, Investigating the Working Principle of the Recommending System and Designing a Personal Music Recommending System, delves into the intricacies of recommendation systems, with a specific focus on music. It begins by exploring the fundamental principles that underpin recommendation systems.The latter part of the paper presents the design of a novel personal music recommendation system. This system leverages the results of K-means clustering to provide faster personalized music recommendations. It considers various auditory factors such as valence (the musical positiveness conveyed by a track), duration (the length of the track), and energy (a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity). These factors are calculated by Spotify and made available in an open data set. This research contributes significantly to the field by providing a deeper understanding of recommendation systems and proposing an innovative solution for personalized music recommendations. The novelty of this system lies in its use of K-means clustering and the specific auditory factors it considers, which sets it apart from existing systems.

Design of music recommendation system based on EDA and K-means cluster analysis

This paper describes the construction and analysis of a music recommendation system, through visual and exploratory analysis of the open source dataset of music recommendation system in the UCL database, exploring the relationship between music popularity and music acoustic properties, using the K-means clustering algorithm for clustering analysis of genres and songs, and finally building a music recommendation system that can, based on the user input of music data, to match the ten music songs that best meet the listener's taste. The analyses in this paper show that there is a certain correlation between music popularity and the indicators of sound, danceability, energy, instrumentality, liveliness, loudness, language, rhythm, mood, duration and tone. The indicators of energy, liveliness, loudness and tempo are positively correlated with the popularity of music, while the indicators of mood and duration are negatively correlated with the popularity of music. In addition, this paper uses K-means clustering algorithm to cluster analysis of genres and songs, and shows the similarity and difference between different genres and songs through visualisation, which provides important data support for the construction of music recommendation system. Finally, this paper establishes a music recommendation system based on K-means clustering algorithm, which can match the ten music songs that best meet the listener's taste according to the music data input by the user. The system is intelligent and personalised, and can recommend music works that are more in line with the user's taste based on the user's preferences and historical listening records. The results of this paper provide useful reference and support for the algorithms and models of the music recommendation system, which can be further researched in depth in the future to improve its intelligence and personalisation level and provide users with better music services.

An Improved Fusion-Based Semantic Similarity Measure for Effective Collaborative Filtering Recommendations

Semantic-enhanced recommendation systems are promising approaches to overcome the sparsity and cold-start problems, which are hard to handle using the conventional collaborative filtering (CF) approaches. Further research is needed to effectively integrate ontologies into collaborative filtering recommender systems. This paper proposes an ontology-based semantic similarity measure to evaluate similarities between items and eventually generate accurate recommendations. The proposed semantic similarity measure termed fusion-based semantic similarity takes into account the semantics of ontological instances (i.e. items) inferred from a specific domain ontology, which is determined by analyzing the hierarchical relationships among the instances, as well as the features of the instances and their relationships to other instances. The new measure comprehensively captures the semantic knowledge associated with instances by exploiting all possible shared semantics between instances in a given domain ontology. Furthermore, this paper proposes a new semantic-enhanced hybrid recommendation approach as a result of combining the new semantic similarity measure with the standard item-based CF to enhance the quality of generated recommendations. In order to assess the effectiveness of our semantic-enhanced hybrid collaborative filtering method, a series of experiments were conducted to compare the performance of the proposed approach against well-established benchmark techniques. The reported experimental results consistently emphasize its superiority, demonstrating enhanced predictive abilities and a notable improvement in the quality of recommendations. More specifically, the proposed approach achieved notable 6% reduction in Mean Absolute Error (MAE) in certain cases, outperforming other benchmark techniques. Additionally, this study highlights the potential of using semantic-based similarity to enhance the performance of recommendation systems. Such enhancements address challenges within collaborative filtering, potentially leading to advancements in recommendation system design and optimization.

Open Science Recommendation Systems for Academic Libraries

An interdisciplinary academic team offers a comprehensive case study describing the development of a predictive model as the cornerstone for an open science recommendation system tailored to the Carnegie Mellon University community. This initiative will empower users in choosing open science services that align with their academic requirements, introduce academics to resources they find valuable, and bridge gaps within academic library service offerings.As an institution with a longstanding commitment to a science-informed approach and a focus on computer science, engineering, and artificial intelligence, Carnegie Mellon University has enthusiastically embraced open science practices. The Carnegie Mellon University’s Libraries has been instrumental in bringing these practices into our academic landscape.The authors strive to develop a predictive model which will evolve into a recommendation system. The pursuit of this endeavor has led the authors through several ethical considerations, such as data privacy, the involvement of student contributors, and the design of a persuasive recommendation system. We are committed to exploring ethical approaches for delivering user-centered recommendations and to preserving individual autonomy.The authors have actively engaged with diverse academic departments, students, and faculty, embarking on data exploration, and applying open science principles throughout the process. The resulting system will raise awareness of library services and deliver tailored recommendations for the adoption of proven research tools and practices.This case study serves as an exemplar of how universities can enact open science principles and develop systems that prioritize the user's interests, navigate institutional complexities to forge interdisciplinary collaboration, and muster resources to support innovative, multi-disciplinary efforts. 

A sustainable health and educational goal development (SHEGD) prediction using metaheuristic extreme learning algorithms

The United Nations established the 17 Sustainable Development Goals (SDGs) in 2015 to address issues like gender equality, clean water, health, education, and hunger by 2030. Of the 17 SDGs, health and education have an outsized impact on countries’ socioeconomic development, so providing insights into progress on these two goals is crucial. Machine learning can help solve many real-world problems, including working towards the SDGs. This paper proposes using a metaheuristic ensemble of Cat Swarm Optimization algorithms with Feed Forward Extreme Learning Machines, called Sustainable Health And Educational Goal Development (SHEGD) Prediction, to effectively contribute to countries’ economic growth by achieving health and education SDGs through machine learning. The model is assessed using UN SDG datasets and performance metrics like accuracy, precision, recall, specificity, and F1-score. Comparisons to other machine learning models demonstrate this model's superiority in designing a recommendation system for progressing towards the health and education SDGs. The proposed model outperforms the other approaches, proving its value for an SDG recommendation system design.

Evaluating Trust in Recommender Systems: A User Study on the Impacts of Explanations, Agency Attribution, and Product Types

Recommender systems have emerged as powerful tools for providing personalized recommendations, often employed by online platforms to suggest products or media content based on user behavior. The design of these systems significantly influences and shapes user experience, as they guide users through vast amounts of information and help them make decisions more efficiently. In this study, we conducted an online experiment (N = 268) to test how different framings of source (human-oriented vs. machine-oriented vs. proxy-oriented vs. none) in recommendation explanations induced users’ attribution of human vs. machine agency in the recommendation process, thus impacting trusting beliefs and trusting behavioral intentions. Results revealed that subtle wording variations could lead participants to orient to different recommendation sources and then attribute human or machine agencies differently, regardless of their understanding of the true technical mechanism. Participants who attributed greater human agency to the recommendations exhibited higher confidence in making choices based on the system’s suggestions and a greater willingness to disclose personal information for continued use, mediated by the competence and integrity dimensions of trust, respectively. By evaluating both wine and vacuum recommendations, we also explored the contextual differences between hedonic and utilitarian product types in the recommendation process. The findings of this study provide implications for the trust-building and ethical communication design of recommender systems.

Entity Footprinting: Modeling Contextual User States via Digital Activity Monitoring

Our digital life consists of activities that are organized around tasks and exhibit different user states in the digital contexts around these activities. Previous works have shown that digital activity monitoring can be used to predict entities that users will need to perform digital tasks. There have been methods developed to automatically detect the tasks of a user. However, these studies typically support only specific applications and tasks and relatively little research has been conducted on real-life digital activities. This paper introduces user state modeling and prediction with contextual information captured as entities, recorded from real-world digital user behavior, called entity footprinting ; a system that records users’ digital activities on their screens and proactively provides useful entities across application boundaries without requiring explicit query formulation. Our methodology is to detect contextual user states using latent representations of entities occurring in digital activities. Using topic models and recurrent neural networks, the model learns the latent representation of concurrent entities and their sequential relationships. We report a field study in which the digital activities of thirteen people were recorded continuously for 14 days. The model learned from this data is used to 1) predict contextual user states, and 2) predict relevant entities for the detected states. The results show improved user state detection accuracy and entity prediction performance compared to static, heuristic, and basic topic models. Our findings have implications for the design of proactive recommendation systems that can implicitly infer users’ contextual state by monitoring users’ digital activities and proactively recommending the right information at the right time.

Enhancing recommendation acceptance: Resolving the personalization–privacy paradox in recommender systems: A privacy calculus perspective

The concept of recommendation acceptance intention (RAI) refers to the willingness of an individual to adopt and use recommender systems. However, contemporary recommender systems, relying solely on accuracy as an evaluation metric have demonstrated subpar performance. The evolving landscape requires the integration of multiple objectives, including relevance, diversity, novelty, and coverage. This study investigates the influence of distinct product organization formats (similar vs. related products) on consumer reactions, aiming to reduce privacy concerns while achieving objectives such as relevance and variety. Grounded in the privacy calculus theory and personalization–privacy paradox framework, this study asserts that organization formats prompt consumers to evaluate a recommender system, considering both privacy risks and personalization benefits. Findings from three experiments and one qualitative study indicate that a related product organization format effectively decreases consumer perceptions of privacy violation while enhancing their perceptions of variety compared to a similar product organization format. This result contributes to RAI and shapes consumer preferences for recommender systems. Moreover, this research distinguishes the mechanisms underlying the influence of organization formats on RAI in the context of different product types, such as durable goods and consumables. This distinction enhances implications for designing recommender systems in e-commerce.

Understanding with a Practical Perspective the Corruption Mode of Goods/Services Procurement in Indonesian Public Organizations

This research aims to clarify the study of corruption in public procurement in public organizations in Indonesia, which when studied through the dimensions of anthropology and democracy is considered too broad in scope and cannot touch the main substance in the discussion of corruption that is specific to public organizations. Corruption in public procurement management in Indonesia is still seen as part of unethical behavior and from a rational perspective is also seen as deviant behavior. The factors for the formation of unethical behavior and categorized as deviant behavior are due to, among others, the structural pressure factors of individuals in public organizations and the intervention factors of parties ranging from vendors, state administrators, and state officials in providing "lure" in the form of gifts or rewards for state administrators and state officials to abuse authority. The results of the study successfully identified modes of corruption in the procurement of goods/services based on the stages and perpetrators of corruption. Corruption in particular in Indonesia is also caused by the weak supervision and management system for preventing corruption in Indonesia. The design of an appropriate and credible public procurement method is one of the solutions to reduce the risk of corruption that begins with the public procurement process. The e-Purchasing method with the e-Cataloque mechanism without negotiations with vendors as a concrete manifestation of the selection of goods/services procurement methods is considered to be very supportive of corruption prevention efforts in Indonesia, because it can directly limit corrupt behavior in public organizations. The novelty, in this study, is to provide recommendations for improvement in the form of a recommendation system design for government procurement of goods/services that is more directed at prioritizing the use of the e-Purchasing method with the e-Cataloque mechanism to reduce the risk of corruption in government spending.

Towards the design of user-centric strategy recommendation systems for collaborative Human–AI tasks

Artificial Intelligence is being employed by humans to collaboratively solve complicated tasks for search and rescue, manufacturing, etc. Efficient teamwork can be achieved by understanding user preferences and recommending different strategies for solving the particular task to humans. Prior work has focused on personalization of recommendation systems for relatively well-understood tasks in the context of e-commerce or social networks. In this paper, we seek to understand the important factors to consider while designing user-centric strategy recommendation systems for decision-making. We conducted a human-subjects experiment (n=60) for measuring the preferences of users with different personality types towards different strategy recommendation systems. We conducted our experiment across four types of strategy recommendation modalities that have been established in prior work: (1) Single strategy recommendation, (2) Multiple similar recommendations, (3) Multiple diverse recommendations, (4) All possible strategies recommendations. While these strategy recommendation schemes have been explored independently in prior work, our study is novel in that we employ all of them simultaneously and in the context of strategy recommendations, to provide us an in-depth overview of the perception of different strategy recommendation systems. We found that certain personality traits, such as conscientiousness, notably impact the preference towards a particular type of system (p< 0.01). Finally, we report an interesting relationship between usability, alignment, and perceived intelligence wherein greater perceived alignment of recommendations with one’s own preferences leads to higher perceived intelligence (p< 0.01) and higher usability (p< 0.01).

Towards a Greener and Fairer Transportation System: A Survey of Route Recommendation Techniques

In recent years, ride-hailing services have emerged as a popular means of transportation for the residents of urban areas. There is an inequality in the spatio-temporal distribution of demand and supply, which requires the proper recommendation of routes to drivers in order to guide them towards riders optimally. This paper provides a review of different route recommendation strategies that have been applied in ride-hailing platforms with the main focus on fairness, and environmental issues. It is important to consider the environmental aspects of route recommendation systems as the transportation sector is one of the major sources of air pollution and has reduced the life expectancy of people around the globe. Moreover, there is an unfair distribution of resources and opportunities among the drivers and riders of the platform which has affected their long-term sustainability in the market. In this paper, we highlight the critical challenges and opportunities inherent in the design of green and fair route recommendation systems and indicate some possible directions for future research.

Design and implementation of clothing fashion style recommendation system using deep learning

Design and implementation of clothing fashion style recommendation system using deep learning

RADio* – An Introduction to Measuring Normative Diversity in News Recommendations

In traditional recommender system literature, diversity is often seen as the opposite of similarity, and typically defined as the distance between identified topics, categories or word models. However, this is not expressive of the social science’s interpretation of diversity, which accounts for a news organization’s norms and values and which we here refer to as normative diversity. We introduce RADio, a versatile metrics framework to evaluate recommendations according to these normative goals. RADio introduces a rank-aware Jensen Shannon (JS) divergence. This combination accounts for (i) a user’s decreasing propensity to observe items further down a list and (ii) full distributional shifts as opposed to point estimates. We evaluate RADio’s ability to reflect five normative concepts in news recommendations on the Microsoft News Dataset and six (neural) recommendation algorithms, with the help of our metadata enrichment pipeline. We find that RADio provides insightful estimates that can potentially be used to inform news recommender system design.

How Do Product Recommendations Help Consumers Search? Evidence from a Field Experiment

Product recommendations can benefit consumers’ online product search via multiple underlying mechanisms, such as showing products that offer them high value, facilitating navigation on the website, or exposing more product information. However, it is unclear ex ante which is the primary underlying mechanism that drives the benefits of product recommendations to consumers. We conducted a randomized field experiment to estimate the benefits of an item-based collaborative filtering (CF) recommendation system to consumers. We collect unique data on the affinity scores computed by an item-based CF algorithm to develop measures of a product’s net value and horizontal (taste) fit for consumers. Our results indicate that product recommendations help consumers search for higher-value products that are lower priced, fit their tastes better, or both. Besides that, we find that the ability to find higher-value products (rather than easy navigation or exposure to more product information) is the primary driver for consumers’ higher purchase probabilities under recommendations. We further find a higher benefit of recommendations in product categories with higher price dispersion and heterogeneity in consumers’ tastes, providing additional evidence for the lower price and better horizontal fit mechanisms. Finally, we find that when made available, consumers substitute their usage of other search tools on the website with product recommendations. Our findings have important implications for online retailers, policymakers, regulators, and item-based CF recommendation system design. This paper was accepted by D. J. Wu, information systems. Funding: This work was supported by the Public Utility Research Center of the University of Florida, the Hi! PARIS Fellowship, the HEC Foundation, and the Leavey School of Business at Santa Clara University [Grant 102720]. Supplemental Material: Data and the online appendix are available at https://doi.org/10.1287/mnsc.2023.4951 .

When Variety Seeking Meets Unexpectedness: Incorporating Variety-Seeking Behaviors into Design of Unexpected Recommender Systems

In this paper, we study the consumers’ variety-seeking behavior in recommender system applications and propose a comprehensive framework to measure such behavior based on past consumption records. The effectiveness of the proposed framework is validated through user questionnaire studies conducted at Alibaba, where our constructed variety-seeking measures match well with consumers’ self-reported levels of their variety-seeking behaviors. We subsequently present a recommendation framework that combines the identified variety-seeking levels with unexpected recommender systems in the data mining literature to address consumers’ heterogenous desire for product variety, in which we provide more unexpected product recommendations to variety-seeking consumers and vice versa. Through off-line experiments on three different recommendation scenarios and a large-scale online controlled experiment at a major video-streaming platform, we demonstrate that those models following our recommendation framework significantly increase various business performance metrics and generate tangible economic impact for the company. Our findings lead to important managerial implications to better understand consumers’ variety-seeking behaviors and design recommender systems. As a result, the best performing model in our proposed frameworks is deployed by the company to serve all consumers on the video-streaming platform.

A Crowdsourcing Recommendation Model for Image Annotations in Cultural Heritage Platforms

Cultural heritage is one of many fields that has seen a significant digital transformation in the form of digitization and asset annotations for heritage preservation, inheritance, and dissemination. However, a lack of accurate and descriptive metadata in this field has an impact on the usability and discoverability of digital content, affecting cultural heritage platform visitors and resulting in an unsatisfactory user experience as well as limiting processing capabilities to add new functionalities. Over time, cultural heritage institutions were responsible for providing metadata for their collection items with the help of professionals, which is expensive and requires significant effort and time. In this sense, crowdsourcing can play a significant role in digital transformation or massive data processing, which can be useful for leveraging the crowd and enriching the metadata quality of digital cultural content. This paper focuses on a very important challenge faced by cultural heritage crowdsourcing platforms, which is how to attract users and make such activities enjoyable for them in order to achieve higher-quality annotations. One way to address this is to offer personalized interesting items based on each user preference, rather than making the user experience random and demanding. Thus, we present an image annotation recommendation system for users of cultural heritage platforms. The recommendation system design incorporates various technologies intending to help users in selecting the best matching images for annotations based on their interests and characteristics. Different classification methods were implemented to validate the accuracy of our work on Egyptian heritage.

Trust in Artificial Intelligent Agent while Completing a Procedural Construction Task

The use of AI-enabled recommender systems in construction activities has the potential to improve worker performance and reduce errors; however, the accuracy of such systems in providing effective suggestions is dependent on the quality of their training data. A within-subjects experimental study was conducted using a simulated recommender system for installation tasks to investigate the effect of system reliability and construction task complexity on worker trust, workload, and performance. Results indicate that overall trust in the AI agent was higher for the highly reliable condition but remained consistent across various levels of task complexity. The workload was found to be higher for low reliability and complex conditions, and the effect of reliability on performance was influenced by task complexity. These findings offer insights for designing recommender systems to support construction workers in completing procedural tasks.

Design and Implementation of a Learning Resource Recommendation System based on User Habits Based on GNN

This project aims to design and implement a learning resource recommendation system based on Graph Neural Networks (GNN). The system utilizes user learning habits as a foundation to provide personalized learning resource recommendations. By collecting and preprocessing user learning history data, and constructing a user-resource relationship graph, the GNN model is used to learn the representation vectors of users and resources. Combined with user habit features, appropriate recommendation algorithms are employed to recommend learning resources that align with their interests and habits.

Decisions with ChatGPT: Reexamining choice overload in ChatGPT recommendations

This research examines how individuals respond differently to recommendation options generated by ChatGPT, an AI-powered language model, in five studies. In contrast to previous research on choice overload, Studies 1 and 2 demonstrate that people tend to respond positively to a large number of recommendation options (60 options), revealing diverse consumer perceptions of AI-generated recommendations. Studies 3 and 4 further illustrate the moderating effect of recommendation agents and indicate that choice overload elicits distinct patterns of consumer reactions depending on whether the recommendations are from a human or AI agent. Lastly, Study 5 directly measures consumer preferences for recommendation agents, revealing a general preference for ChatGPT, particularly when a large number of options are available. These findings have significant implications for recommendation system design and user preferences regarding AI-powered recommendations.

Decision Support Strategies for Household Water Consumption Behaviors Based on Advanced Recommender Systems

Water is one of the most important factors that can influence human health. Therefore, constant monitoring of water consumption is essential to maintain a balance of water demand. A recommendation system represents a major challenge, but with huge potential for the water industry, providing consumers the most efficient ways to conserve water based on their data collected from smart water meters. This paper proposes a novel recommendation system design architecture that promotes water conservation behavior among residential consumers from urban areas. We analyzed 480,000 data samples from several households with different profiles to generate personalized recommendations for each household and encourage consumers to adopt measures to raise awareness and reduce water consumption. Moreover, data were collected from three different measurement points in the household (cold_sink, hot_sink, and toilet), with a sampling time of 60 s. The proposed recommendation system implements collaborative filtering combined with a set of rules to generate recommendations based on the consumption patterns of similar households. The results are promising, offering personalized feedback that could help change the consumption behavior of households if the recommendations made are followed.