Preferences Of Potential Users Research Articles

E-Catalog Promotion of Fishermen Business Group with User Centered Design Method

This research aims to design a promotional e-catalog for fishing business groups using the User Centered Design (UCD) method to improve the quality of their promotional services online. The partners in this research are the Fisheries Supervisory Community Group in Batu Basar Village, Nongsa District, Batam City, Riau Islands. So far, this group and fishermen have often faced obstacles in promoting their products effectively to potential consumers, especially in the rapidly developing digital era. The UCD method used in this research is a user-focused approach. The initial stage of research includes gathering information regarding the needs and preferences of potential users, including fishing business groups and potential consumers. This information was obtained through interviews, observations and literature studies. Based on the data collected, a promotional e-catalog was designed that took into account user needs and promotional objectives which included features such as complete product descriptions, product photos, price information, user testimonials and contact information. After the design is complete, the manufacturing stage is carried out and then continues to the testing stage involving potential users. This testing aims to maximize usability, efficiency and user satisfaction. The test results used the Nielsen model with five indicators with the results of Easy To Learn getting a score of 3.85, Efficiency To Use getting a score of 3.75, the Easy To Remember indicator getting a score of 3.85, and the Few Errors indicator getting a score of 4.05. Then the fun to use indicator has a value of 3.95.

Hyperbolic multichannel graph convolutional network for social recommendation

Social recommendations enhance the quality of recommendations by integrating social network information. Existing methods predominantly rely on pairwise relationships to uncover potential user preferences. However, they usually overlook the exploration of higher-order user relations. Moreover, because social relation graphs often exhibit scale-free graph structures, directly embedding them in Euclidean space will lead to significant distortion. To this end, we propose a novel graph neural network framework with hypergraph and hyperbolic embedding learning, namely HMGCN. Specifically, we first construct hypergraphs over user-item interactions and social networks, and then perform graph convolution on the hypergraphs. At the same time, a multi-channel setting is employed in the convolutional network, with each channel encoding its corresponding hypergraph to capture different high-order user relation patterns. In addition, we feed the item embeddings and the obtained high-order user embeddings into a hyperbolic graph convolutional network to extract user and item representations, enabling the model to better capture the hierarchical structure of their complex relationships. Experimental results on three public datasets, namely FilmTrust, LastFM, and Yelp, demonstrate that the model achieves more comprehensive user and item representations, more accurate fitting and processing of graph data, and effectively addresses the issues of insufficient user relationship extraction and data embedding distortion in social recommendation models.

ASKAT: Aspect Sentiment Knowledge Graph Attention Network for Recommendation

In modern online life, recommender systems can help us filter unimportant information. Researchers of recommendation algorithms usually utilize historical interaction data to mine potential user preferences. However, most existing methods use rating data to mine user interest preferences, ignoring rich textual information such as reviews. Although some researchers have attempted to combine ratings and reviews for recommendation, we believe the following shortcomings still exist. First, existing methods are overly dependent on the accuracy of external sentiment analysis tools. Second, existing methods do not fully utilize the features extracted from reviews. Further, existing methods focus only on the aspects that users like, while ignoring the aspects that users dislike, and they cannot completely model users’ true preferences. To address the above issues, in this paper, we propose a recommendation model based on the aspect of the sentiment knowledge graph attention network (ASKAT). We first use the improved aspect-based sentiment analysis algorithm to extract aspectual sentiment features from reviews. Then, to overcome the difficulty in underutilizing the information extracted from the comments, we build aspects of sentiment-enhanced collaborative knowledge mapping. After that, we propose a new graph attention network that uses sentiment-aware attention mechanisms to aggregate neighbour information. Finally, our experimental results on three datasets, Movie, Amazon book, and Yelp, show that our model consistently outperforms the baseline model in two recommendation scenarios, click-through-rate prediction and Top-k recommendation. Compared with other models, the method shows significant improvement in both recommendation accuracy and personalised recommendation effectiveness.

Ride experience in automated minibuses: measuring users’ transport mode preferences before and after a test ride

In the present study, we explored the influence of ride experience in automated minibuses (AmBs) on transport mode choice that includes the automated shuttles as well as conventional transport options (car, bus and bicycle) on the first-/ last-mile stage of rail trips. We used the case study of the connection between Brandevoort train station and the newly developing working and living area in Helmond (the Netherlands) where an AmB was tested in the February-March period of 2021. We conducted a two-wave stated preference experiment wherein data was gathered both before and after the participants had a test ride in the AmB. The results of the joint hybrid mixed logit model indicate a clear preference towards flexible-service AmBs, particularly in relation to travel time and costs. While preferences for less favoured regular-service AmBs experienced a noteworthy shift in travel time and costs, waiting and walking time parameters influenced by participants' ride experience in this pilot and by prior ride experience from other pilots. This reinforces the idea that the ride experience in AmBs even in a short pilot trial like the one conducted in Helmond has a significant impact on preferences for AmBs in comparison with car, bus and bicycle alternatives. Hence, panel studies can provide a more comprehensive understanding of how attitudes and preferences of potential users evolve over time.

Inter- and Intra-Domain Potential User Preferences for Cross-Domain Recommendation

Inter- and Intra-Domain Potential User Preferences for Cross-Domain Recommendation

Data-driven implicit design preference prediction model for product concept evaluation via BP neural network and EEG

Customer preference-involved product concept evaluation is a vital stage for developing new products. One of the key factors in this stage is to mine the potential relationships between customers’ needs and technical requirements. In most new product development (NPD), design characteristics (DCs) are employed as performance indicators to describe the technical requirements of a scheme. Previous studies have focused on evaluating the degree to which a scheme meets the performance value of DCs, disregarding the relationship between DCs and potential user preferences. This oversight leads to an inability to accurately predict customer satisfaction with the candidate schemes based on their preferences. Previous research, which was aimed at ensuring the preference value of schemes by utilizing mapping data between design preference (DP) and DCs, overlooked users' psychological states and thus failed to accurately capture their cognitive preferences for the schemes. To address these issues, an electroencephalogram (EEG) data-driven, implicit DP prediction approach is proposed. The method characterizes the implicit cognitive relationship between DP and DCs by mining EEG data and utilizes a back propagation (BP) neural network to predict DP. First, a user's DP word pairs were constructed based on the approach of statistical analysis, and the best-worst method (BWM) was introduced to obtain the importance of DCs. Second, an experiment was designed based on the extracted preference word pairs to collect EEG data that reflect a user's DC preference, record the user's stimulation state toward the DCs, and label their DP based on the segmentation results of preference evaluation. Third, a BP neural network was employed to construct a preference prediction model for DCs, and the reliability of the proposed preference prediction model was validated by using the forklift design example. The prediction results further exhibited the potential association between user preferences and DCs that can be acquired by EEG data mining. The results also provide support for designers to promptly acquire design concepts that satisfy user requirements (URs), thereby enhancing the efficiency and quality of product development in a highly competitive market.

Variation in Preferences for Long-Acting Injectable Pre-Exposure Prophylaxis Among US Men Who Have Sex with Men: A Latent Class Analysis.

Cabotegravir long-acting injectable HIV pre-exposure prophylaxis (LA PrEP) is efficacious, with a good safety profile, and was approved by the US Food and Drug Administration in December 2021. Understanding variations in potential user preferences for LA PrEP may inform implementation and subsequently improve uptake and community-level effectiveness. HIV-negative, sexually active men who have sex with men (MSM) aged ≥15 years were recruited online for the 2019 American Men's Internet Survey, before LA PrEP approval. Respondents completed a discrete-choice experiment (DCE) with hypothetical LA PrEP attributes (out-of-pocket cost, perceived side effects, injection frequency, perceived stigma, service location). Latent class analysis segmented respondents into groups based on their preferences for the attributes presented, and relative importance of preference weights and willingness-to-pay were calculated. While the majority had never used daily oral PrEP, 73% of the 2489 respondents were very or somewhat likely to use LA PrEP. Three latent classes were identified from 2241 respondents in the DCE. The "side effects-averse" class was the largest group (64% of respondents) and placed 61% relative importance on side effects. The "ambivalent" class (20% of respondents) placed higher importance on stigma (17% of relative importance) than other classes. The "cost-conscious" class (16% of respondents) placed higher relative importance (62%) on cost compared with other attributes and classes. Perceived side effects were an important hypothetical barrier for LA PrEP uptake among a large proportion of potential MSM users. Minimizing out-of-pocket costs is likely to increase uptake and may be important to equitable access. Tailored communication strategies are recommended for the different groups of potential LA PrEP users.

A conditional random field recommendation method based on tripartite graph

Recommender System (RS) has generated widespread attention with the aim of expanding different items. Among graph-based recommendation methods, the tripartite graph can better manage data sparsity and cold start, while improving the metrics of various recommendations such as recall, precision, and diversity. Existing tripartite graph-based methods encounter numerous challenges, including mitigating data sparsity, improving diversity, and capturing potential user preferences via social relations. To address these challenges, a Conditional Random Field based on Tripartite Graph (CRF-TG) is proposed. The tripartite graph consists of the user, item, and trust level. The method can mine potentially similar users, create probabilistic models based on TG, and uncover potential user preferences. Moreover, to mine the users with similar preferences outside the social relationship, the random walk method is used to test CRF-TG. Experiments are designed to verify the validity of CRF-TG. Compared to the others considered methods, CRF-TG gives a 15% increase on average in performance indicators such as diversity, recall, and F1.

Heterogeneity in the Preferences of Potential Users of Automated Transit Network (ATN)

Many cities in Iran, including the metropolis of Shiraz, are increasingly car-oriented, resulting in traffic congestion and related issues. Considering the current conditions of Iran, an automated transit network (ATN) can be one of the available solutions to this problem. ATN is an advanced type of public transit consisting of automated vehicles moving passengers on a network of dedicated guideways. As a combination of public, personal, and private transport, ATNs may decrease the use of cars and address related problems. In order to design effective policies aimed at achieving the benefits of ATN, it is necessary to have a better understanding of how people accept an ATN system, especially car users. This research aims at advancing future research on the effects of ATNs on travel behavior through identifying the characteristics of users who are likely to accept ATN services, by examining the heterogeneity in the preferences of these people. To achieve this goal, a stated choice survey was conducted and analyzed using multinomial logit (MNL) and mixed logit (ML) models. The results showed that the parameters of trip purpose, owning a hybrid car, and the level of education affect the preferences toward the ATN system. Additionally, from the comparison of the results of the MNL and ML models, it was found that despite the greater ability of the ML model in estimating possible heterogeneities, likely the MNL model can also help to record some heterogeneities more realistically. In the end, the methodological limitations of the study were also acknowledged. Despite the potential hypothesis bias and the status quo bias, the results captured the directionality and relative importance of the attributes of interest.

Contrastive Learning Based Graph Convolution Network for Social Recommendation

Exploiting social networks is expected to enhance the performance of recommender systems when interaction information is sparse. Existing social recommendation models focus on modeling multi-graph structures and then aggregating the information from these multiple graphs to learn potential user preferences. However, these methods often employ complex models and redundant parameters to get a slight performance improvement. Contrastive learning has been widely researched as an effective paradigm in the area of recommendation. Most existing contrastive learning-based models usually focus on constructing multi-graph structures to perform graph augmentation for contrastive learning. However, the effect of graph augmentation on contrastive learning is inconclusive. In view of these challenges, in this work, we propose a contrastive learning based graph convolution network for social recommendation (CLSR), which integrates information from both the social graph and the interaction graph. First, we propose a fusion-simplified method to combine the social graph and the interaction graph. Technically, on the basis of exploring users’ interests by interaction graph, we further exploit social connections to alleviate data sparsity. By combining the user embeddings learned through two graphs in a certain proportion, we can obtain user representation at a finer granularity. Meanwhile, we introduce a contrastive learning framework for multi-graph network modeling, where we explore the feasibility of constructing positive and negative samples of contrastive learning by conducting data augmentation on embedding representations. Extensive experiments verify the superiority of CLSR’s contrastive learning framework and fusion-simplified method of integrating social relations.

Factors Affecting Disabled Consumer Preferences for an Electric Vehicle for Rural Mobility: An Italian Experimental Study

Although it is often assumed that physically disabled individuals have different behaviors towards environment, there has been very little empirical evidence supporting this statement. For this reason, this study aimed to explain the different behaviors and preferences of potential users by analyzing their attitudes towards an electric vehicle specifically designed for rural mobility. In other words, this study analyzed both the willingness of disabled people to drive an electric vehicle and their willingness to pay (WTP) for such a vehicle. A total of 209 data were collected from a sample of physically disabled consumers in Italy using a structured questionnaire. Therefore, a stated preferences Italian dataset was used, and an exploratory factor analysis, a probit model, and an ordered logit regression model were applied. The results suggested that to encourage the use of electric cars for rural mobility, it should be necessary to manage environmental concerns, vehicle-related attributes, attitudes towards eco-friendly cars, people’s emotional experience, and their purchase intention. In addition, to improve customer intention, development plans and service features must be synergistically organized to mutually support each other.

Social Recommendation Algorithm Based on Self-Supervised Hypergraph Attention

Social network information has been widely applied to traditional recommendations that have received significant attention in recent years. Most existing social recommendation models tend to use pairwise relationships to explore potential user preferences, but overlook the complexity of real-life interactions between users and the fact that user relationships may be higher order. These approaches also ignore the dynamic nature of friend influence, which leads the models to treat different friend influences equally in different ways. To address this, we propose a social recommendation algorithm that incorporates graph embedding and higher-order mutual information maximization based on the consideration of social consistency. Specifically, we use the graph attention model to build higher-order information among users for deeper mining of their behavioral patterns on the one hand; while on the other hand, it models user embedding based on the principle of social consistency to finally achieve finer-grained inference of user interests. In addition, to alleviate the problem of losing its own hierarchical information after fusing different levels of hypergraphs, we use self-supervised learning to construct auxiliary branches that fully enhance the rich information in the hypergraph. Experimental results conducted on two publicly available datasets show that the proposed model outperforms state-of-the-art methods.

Explainable Recommendation Based on Weighted Knowledge Graphs and Graph Convolutional Networks

Knowledge Graphs (KGs) have been shown to have great potential to provide rich and highly defined structured data about Recommender Systems (RSs) items. This paper introduces Explain- KGCN, an Explainable RS based on KGs and Graph Convolutional Networks (GCNs). The system emphasises the importance of semantic information characterisation and high-order connectivity of message passing to explore potential user preferences. Thus, based on a relation-specific neighbourhood aggregation function, it aims to generate for each given item a set of relation-specific embeddings that depend on each semantic relation in the KG. Specifically, the relation-specific aggregator discriminates neighbours based on their relationship with the target node, allowing the system to model the semantics of various relationships explicitly. Experiments conducted on two real-world datasets for the top-K recommendation task demonstrate the state-of-the-art performance of the system proposed. Besides improving predictive performance in terms of precision and recall, Explain-KGCN fully exploits wealthy structured information provided by KGs to offer recommendation explanation.

A Multi-Objective Clustering for Better Data Management in Connected Environment

Over the past decade, the rapid increase in connected devices has enabled the emergence of new digital ecosystems to provide new opportunities for monitoring and managing systems to optimize overall performance. With these connected environments, data collection and management become increasingly challenging. A significant number of works in the literature have addressed data collection and management based on different contexts (e.g., mobile ad hoc, Peer-2-Peer, and IoT networks). Today, a wired network uses all of these protocols simultaneously, thus highlighting the need to build a standard data collection and management framework that considers all potential user preferences. For this purpose, multi-objective clustering has been utilized as a promising solution to ensure the stability of connected devices during the collection and management of data. In this paper, we introduce a new multi-objective clustering (MOC) technique based on various criteria for cluster construction and head selection in connected environments. More precisely, the proposed solution is based hypergraphs to represent the connected environment and clusters according to similarities between heterogeneous devices. Then, a cross-sectional hypergraph algorithm is applied to select the cluster heads. Experiments conducted show that our solution outperforms the pioneering literature methods in terms of performance and effectiveness.

Exploring Users’ Preferences for Automated Minibuses and Their Service Type: A Stated Choice Experiment in the Netherlands

In this paper, we study the deployment potential of automated minibuses (AmBs) on the first-mile part of public transport (PT) trips or short (sub)urban commutes by comparing “regular” (fixed route and fixed schedule) and “flexible” (door-to-door and on-demand) service types. For reaching that goal, we run a stated choice experiment in the Netherlands. The participants have assessed the referred two AmB alternatives compared to their current travel mode (car, PT, or active modes (AM) – bicycle and walking) used as the main mode for unimodal travellers or as access travel mode to transit lines for multimodal travellers. The results of a joint mixed logit model estimation based on data obtained from Dutch travellers show that there are similarities and differences in the preferences for the AmBs service type within and between the segments of travellers (car, PT, and AM) and that these are mostly in instrumental variables (cost and time) and attitudes. Current PT users prefer the flexible service to regular service based on their perception of in-vehicle travel time and waiting time, while current users of car and AM do not show a difference in preference between the two services concerning these variables. Moreover, their perception of in-vehicle travel time and waiting time is not significantly different from PT users’ perception of those variables in the regular service. This may mean that for non-PT users (car and AM), AmB’s flexibility of door-to-door transport is not seen as offering a significant advantage over what they think about public transport. When looking at the preferences of potential users explained by underlying psychological factors, we conclude that a positive attitude towards riding in AmBs is a significant factor in all three segments of travellers. Trust, usefulness, and enjoyment in using AmBs are important prerequisites for car and PT users to choose either service type. The experience with technology positively influences the preferences of current PT users for both AmB services.

TAFM: A Recommendation Algorithm Based on Text-Attention Factorization Mechanism.

The click-through rate (CTR) prediction task is used to estimate the probabilities of users clicking on recommended items, which are extremely important in recommender systems. Recently, the deep factorization machine (DeepFM) algorithm was proposed. The DeepFM algorithm incorporates a factorization machine (FM) to learn not only low-order features but also the interactions of higher-order features. However, DeepFM lacks user diversity representations and does not consider the text. In view of this, we propose a text-attention FM (TAFM) based on the DeepFM algorithm. First, the attention mechanism in the TAFM algorithm is used to address the diverse representations of users and goods and to mine the features that are most interesting to users. Second, the TAFM model can fully learn text features through its text component, text attention component, and N-gram text feature extraction component, which can fully explore potential user preferences and the diversity among user interests. In addition, the convolutional autoencoder in the TAFM can learn some higher-level features, and the higher-order feature mining process is more comprehensive. On the public dataset, the better performing models in the existing models are deep cross network (DCN), DeepFM, and product-based neural network (PNN), respectively, and the AUC score metrics of these models hover between 0.698 and 0.699. The AUC score of our design model is 0.730, which is at least 3% higher than that of the existing models. The accuracy metric of our model is at least 0.1 percentage points higher than that of existing models.

Planning parental ehealth education about healthy-weight lifestyle in pediatric primary care patients 6-12 years

Background: Childhood obesity is a pandemic with immediate and future health risks that demand holistic, personalized, and innovative treatments. Healthcare providers struggle to provide such treatment within the constraints of medical visits. A theoretical and evidence-based eHealth intervention could engage parents and caregivers to improve health behaviors and increase reach to low-income vulnerable families, a high-risk chort often neglected in clinical research. This study aims to identify needs and preferences of potential users of an eHealth intervention.
 Methods: Focus groups and interviews of ethnically diverse, low-income parents and semi-structured interviews of primary healthcare staff obtained perspectives on the content and structure of a eHealth intervention intended to improve lifestyle habits to reduce childhood obesity. Research team members analyzed the data to identify specific needs and communication strategies for the target population.
 
 Results: Parents endorsed learning more about healthy eating, physical activity, and the effects of sugar-sweetened beverages on their child’s weight. Parents reported a preference for bright colors, short texts, videos, summary quizzes, and additional resources to explore on their own and with their child. Healthcare staff reported time limitations during clinical visits and an interest in looping family progress into their EHR. Using an eHealth intervention, parents expect an improved relationship and increased trust with their child’s pediatrician.
 
 Conclusion: Parents and staff saw value in this tool and provided useful recommendations for its design. This eHealth intervention could create a health alliance between the healthcare provider and family, provide personalized content, and be scaled to address health disparities broadly.

Perceptions of Older People with Cognitive Impairment, Caregivers, and Professionals about ehcoBUTLER (Tablet Health Care Platform): A Qualitative Focus Group Study.

EhcoBUTLER is a tablet platform mainly aimed at the elderly with mild cognitive impairment (MCI) to promote their well-being and health. The main objective of this study was to explore the perceptions and feedback level of the ehcoBUTLER potential users and stakeholders to improve its development. Through this exploration, the secondary objective was to contribute to the development of software/apps that promote their integral health. Focus groups were conducted (13 elderly with MCI, 13 with dementia, 12 caregivers, 11 professionals). The content and feedback level were analyzed. Participants liked the appearance of ehcoBUTLER, would like to use it, and were mainly interested in the emotional, healthy lifestyle, cognitive, and ergonomic tools. It is necessary to have prior training, more intuitive/customizable apps, low-price/free, offline/USB content, and add other activities/features. EhcoBUTLER is well-oriented to meet the needs and preferences of potential users. However, improvements in its usability, accessibility, and sustainability are needed. The participants’ perspectives provided a comprehensive view to improve ehcoBUTLER, so that in the future, it can benefit the elderly to be active agents in their health; support caregivers in their role and to have a respite; and professionals to have a multi-intervention platform. The present findings can contribute to the development of tablet software/apps that promote the integral well-being of this population.

Implants for HIV prevention in young women: Provider perceptions and lessons learned from contraceptive implant provision.

Preventing new HIV infections, especially amongst young women, is key to ending the HIV epidemic especially in sub-Saharan Africa. Potent antiretroviral (ARV) drugs used as pre-exposure prophylaxis (PrEP) are currently being formulated as long-acting implantable devices, or nanosuspension injectables that release drug at a sustained rate providing protection from acquiring HIV. PrEP as implants (PrEP Implants) offers an innovative and novel approach, expanding the HIV prevention toolbox. Feedback from providers and future users in the early clinical product development stages may identify modifiable characteristics which can improve acceptability and uptake of new technologies. Healthcare workers (HCWs) perspectives and lessons learned during the rollout of contraceptive implants will allow us to understand what factors may impact the roll-out of PrEP implants. We conducted eighteen interviews with HCWs (9 Nurses and 9 Community Healthcare Workers) in rural KwaZulu-Natal, South Africa. HCWs listed the long-acting nature of the contraceptive implant as a key benefit, helping to overcome healthcare system barriers like heavy workloads and understaffing. However, challenges like side effects, migration of the implant, stakeholder buy-in and inconsistent training on insertion and removal hampered the roll-out of the contraceptive implant. For PrEP implants, HCWs preferred long-acting products that were palpable and biodegradable. Our findings highlighted that the characteristics of PrEP implants that are perceived to be beneficial by HCWs may not align with that of potential users, potentially impacting the acceptability and uptake of PrEP implants. Further our data highlight the need for sustained and multi-pronged approaches to training HCWs and introducing new health technologies into communities. Finding a balance between the needs of HCWs that accommodate their heavy workloads, limited resources at points of delivery of care and the needs and preferences of potential users need to be carefully considered in the development of PrEP implants.

IMPROVEMENT OF SVD ALGORITHM TO INCREASE THE EFFICIENCY OF RECOMMENDATION SYSTEMS

Many existing websites use recommendation systems for their users. They generate various offers for them, for example, similar products or recommend the people registered on this site with similar interests. Such referral mechanisms process vast amounts of information to identify potential user preferences. Recommendation systems are programs that try to determine what users want to find, what might interest them, and recommend it to them. These mechanisms have improved the interaction between the user and the site. Instead of static information, they provide dynamic information that changes: recommendations are generated separately for each user, based on his previous activity on this web resource. Information from other visitors may also be taken into account. The methods of collecting information provided by the Internet have greatly simplified the use of human thought through collaborative filtering. But, on the other hand, the large amount of information complicates the implementation of this possibility. For example, the behavior of some people is quite clearly amenable to modeling, while others behave completely unpredictably. And it is the latter that affect the shift of the results of the recommendation system and reduce its effectiveness. An analysis of Internet resources has shown that most of the recommendation systems do not provide recommendations to users, and the part that does, for example, offers products to the user, selects recommendations manually. Therefore, the task of developing methods for automated generation of recommendations for a limited set of input data is quite relevant. The problems of data sparseness, new user problem, scalability of the widely used SVD algorithm for the development of such recommendation systems are proposed to be eliminated by improving this algorithm by the method of the nearest k-neighbors. This method will allow you to easily segment and cluster system data, which will save system resources.