Preference Matching Research Articles

Supplemental Material for A Worldwide Test of the Predictive Validity of Ideal Partner Preference Matching

Supplemental Material for A Worldwide Test of the Predictive Validity of Ideal Partner Preference Matching

A tensor recommendation method based on HMM network and meta-path

The main approach to capturing user latent preferences in recommendation systems (RS) is through high-order tensor decomposition and the deep-walk method. Several key issues, if solved, could improve the performance of RS. These include enforcing the interpretation of RS in the context of sparse data completion, cold start, and interpretability, mining user latent preferences with a tensor constructed from a user-item rating matrix (RM) and a preference match mechanism based on K-nearest neighbor (KNN) similar users. In this paper, a method that integrates a hidden Markov model, meta-path, and third-order tensor (HMM-MP-TOT) is proposed. An HMM, based on the user-item RM and latent preferences from KNN users is constructed. Subsequently, the Viterbi and deep-walk methods are used to obtain a series of user-item two-dimensional MPs. Then, truncated − singular value decomposition (t-SVD) is applied to a user-item-KNN third-order tensor to obtain a better recommendation result. On average, HMM-MP-TOT obtains 94.7% precision, 80.2% recall, and 96.4% diversity.

Collaborative trajectory representation for enhanced next POI recommendation

Point-of-Interest (POI) recommendation stands as the cornerstone within a variety of location-based applications and services that intend to anticipate upcoming movements that users may be interested in. The current state-of-the-art methods have effectively explored spatio-temporal contextual features and users’ long-term and short-term preference patterns. Nevertheless, most existing work lacks the ability to effectively capture group movement patterns from trajectory collaboration. Additionally, they pay close attention to the accuracy of personalized recommendations, neglecting recommendation diversity, which refers to offering broader location options that extend beyond a user’s typical preferences, thereby avoiding excessive homogeneity or repetition. To address these gaps, this study proposes a Collaborative Trajectory Representation model (CTRNext), which enhances the diversity of recommendations while maintaining the precision of personalized preferences. To be specific, we first design two trajectory embedding layers to extract joint semantic interactions and the explicit spatiotemporal context-aware representation. Then, a trajectory semantic similarity calculation module that captures collaborative signals from potentially similar-minded users and eliminates barriers caused by trajectory length is proposed. Next, the implicit correlation and further updated representation between different check-in records are achieved through a multi-head self-attention aggregation module. Finally, we put forward a dual-driven user preference matching module to generate the preference-based next POI recommendation while enhancing diversity. Our approach demonstrates its remarkable recommendation accuracy through extensive experimentation on four real-world datasets, surpassing the performance of state-of-the-art methodologies.

Gift giving in the age of AI: The role of social closeness in using AI gift recommendation tools

AbstractFirms increasingly employ artificial intelligence (AI) gift recommendation tools to assist consumers with their gift choices. Yet, a notable gap exists in understanding consumers’ responses to AI recommendations in a gift giving context. Through five studies, we found that social closeness between the giver and recipient significantly affects the use of AI gift recommendation tools, driven by two underlying mechanisms: expected relational signaling and preference matching. In addition to establishing these effects, this research identifies relevant boundary conditions. Our findings reveal that self‐oriented perfectionism increases preference matching for gifts to distant friends, while revealing the giver's identity boosts AI tool use for close friends by reducing relational signaling expectations. Additionally, AI tools capable of turn‐taking enhance preference matching and AI tool use, especially for gifts to close friends. Our findings advance the understanding of how and why social closeness influences givers’ utilization of AI gift recommendation tools and offer valuable insights for practitioners on designing these tools more effectively, considering the nuances of social relationships.

The value of store brand customization: consider demand learning and preference matching

The value of store brand customization: consider demand learning and preference matching

Work Preferences, Control, and Independence among Midlife and Older Adults in Aotearoa/New Zealand.

In 2023, current life expectancy for adults aged 65 and over in Aotearoa/New Zealand ranged from 84.6 to 89.2 years. Mandatory retirement has been abolished, but pension age eligibility remains at age 65. However, some older adults prefer to continue working, often experiencing a mismatch between current and preferred work statuses. The current study used data drawn from the 2020 wave of the New Zealand Health, Work and Retirement study (HWR; N = 3,916; Mage = 66.84) to examine associations between work preferences and perceptions of control and independence. We found that those not working but who preferred to work reported the lowest levels of perceived control and independence. Conversely, work preference match and higher levels of perceived control and independence were positively related to life satisfaction. The current study adds to our understanding of the centrality of work status, perceived control, and perceived independence among midlife and older adults, particularly in Aotearoa/New Zealand.

Who are the best voluntary disseminators on social networking sites? An exploration based on panel data from twitter

PurposeUsers' voluntary forwarding behavior opens a new avenue for companies to promote their brands and products on social networking sites (SNS). However, research on voluntary information disseminators is limited. This paper aims to bring an in-depth understanding of voluntary disseminators by answering the following questions: (1) What is the underlying mechanism by which some users are more enthusiastic to voluntarily forward content of interest? (2) How to identify them? We propose a theoretical model based on the Elaboration-Likelihood Model (ELM) and examine three types of factors that moderate the effect of preference matching on individual forwarding behavior, including personal characteristics, tweet characteristics and sender–receiver relationships.Design/methodology/approachVia Twitter API, we randomly crawled 1967 Twitter users' data to validate the conceptual framework. Each user’s original tweets and retweeted tweets, profile data such as the number of followers and followees and verification status were obtained. The final corpus contains 163,554 data points composed of 1,634 valid twitterers' retweeting behavior. Tweets produced by these core users' followees were also crawled. These data points constitute an unbalanced panel data and we employ different models — fixed-effects, random-effects and pooled logit models — to test the moderation effects. The robustness test shows consistency among these different models.FindingsPreference matching significantly affects users' forwarding behavior, implying that SNS users are more likely to share contents that align with their preferences. In addition, we find that popular users with lots of followers, heavy SNS users who author tweets or forward other-sourced tweets more frequently and users who tend to produce longer original contents are more enthusiastic to disseminate contents of interest. Furthermore, interaction strength has a positive moderating effect on the relationship between preference matching and individuals' forwarding decisions, suggesting that users are more likely to disseminate content of interest when it comes from strong ties. However, the moderating effect of perceived affinity is significantly negative, indicating that an online community of individuals with many common friends is not an ideal place to engage individuals in sharing information.Originality/valueThis work brings about a deep understanding of users' voluntary forwarding behavior of content of interest. To the best of our knowledge, the current study is the first to examine (1) the underlying mechanism by which some users are more likely to voluntarily forward content of interest; and (2) how to identify these potential voluntary disseminators. By extending the ELM, we examine the moderating effect of tweet characteristics, sender–receiver relationships as well as personal characteristics. Our research findings provide practical guidelines for enterprises and government institutions to choose voluntary endorsers when trying to engage individuals in information dissemination on SNS.

Research on the application of bilateral collaborative algorithm in hotel tourism consumption preference recommendation

SummaryThe continuous expansion of the domestic tourism market has driven the development of the hotel related industry. It is a key focus of the hotel industry to provide accurate supporting services to tourists based on their preferences. However, the current recommendation model mainly considers tourist factors and lacks consideration for hotel factors. In order to address the concerns of recommendation services, a bilateral collaborative recommendation model is proposed, which first analyzes tourist feature data through comprehensive similarity calculation. Firstly, the comprehensive similarity calculation is used to analyze tourist feature data, and the K‐means Clustering Algorithm (K‐means) is introduced to improve the clustering effect between data. And to better distinguish tourist preferences, the Gale Shapley (G‐S) bilateral matching model is introduced to achieve preference matching of consumer features. In the Mean Absolute Error (MAE) analysis of the model, the User‐based Collaborative Filtering (UBCF) model performs the worst. When the k value is 90, the minimum MAE value is 0.7896. The Item‐based Collaborative Filtering (IBCF) model is 0.7776, with the best performance being the improved G‐S model of 0.7325. In the performance testing of three models' preference recommendations, the improved G‐S model achieves an accuracy of over 90% in all three types of feature data recommendations, with the lowest accuracy of 62.5% for UBCF and 71.3% for IBCF. It can be seen that the proposed improved G‐S model can accurately identify tourist and hotel preference data and accurately recommend corresponding projects. The research content provides important technical references for the digital transformation and development of the tourism industry.

Exploiting dynamic social feedback for session-based recommendation

Since people with close relationships are easily influenced by each other, social friends usually have more preferences of higher similarities than others. For this reason, social recommendation methods are proposed to adopt social links to improve the degree of preference matching between users and recommended items. Although current social recommendation methods have captured the general preference similarities among social friends, it is still difficult to model the evolution of dynamic social influence among friends, especially in session-based scenarios. In reality, when users’ dynamic preferences are changing, social feedback from their friends is also changing over time. So that the dynamic social feedback is an important social influence, which has not been considered in current studies. To this end, we propose a social feedback-enhanced session-based recommendation (SFRec) method, which not only utilizes the similarity of general preferences among friends but also captures the friends’ influence which reflects people’s dynamic preferences. Specifically, we first coordinate similarity relations via information propagation on social graph, item transition graph and user–item interaction graph. To capture social feedback based on users’ dynamic preferences, we then construct a social feedback generation module that consists of preference extraction, feedback generation and feedback aggregation. Finally, we construct a preference fusion module to obtain the final preference representation and make personalized recommendation. We conduct comprehensive experiments on three datasets. Results demonstrate that SFRec surpasses the state-of-the-art models on recommendation performance.

Vertical nonlinear vibration analysis of cold rolling mill considering structural gap and dynamic rolling force

To reveal the impact of vertical nonlinear vibration on the stabilization of the precision cold rolling system, the vertical nonlinear vibration model with consideration of structural gap and dynamic rolling force has been established. Utilizing the Magnus series method with high accuracy and long-term numerical stabilization, we calculate the vibration response of the cold rolling system and conduct numerical simulations to determine the bifurcation characteristics, maximum Lyapunov exponent, and two-parameter dynamical behavior. The results reveal the parameter regions of bifurcation and chaos that cause the vibration instability of the cold rolling system. It has also been found that the system has the nonlinear phenomenon of transition from period-3 motion to chaotic motion. More importantly, two-parameter matching regions for the cold rolling system performing periodic and chaotic motions are determined. Two-parameter matching solutions for improving and deteriorating the stabilization of the cold rolling system are clarified, and the preferred matching regions for the parameters coupling with the structural gap and dynamic rolling force are revealed. The results reveal the impact of system parameters upon the vertical nonlinear vibration of the cold rolling mill, which can serve as an academic reference for reducing the vibration and enhancing the stabilization of the cold rolling system.

Opinion Dynamics with Preference Matching: How the Desire to Meet Facilitates Opinion Exchange

The paper reexamines an agent-based model of opinion formation under bounded confidence with heterogeneous agents. The paper is novel in that it extends the standard model of opinion dynamics with the assumption that interacting agents share the desire to exchange opinion. In particular, the interaction between agents in the paper is modeled via a dynamic preferential-matching process wherein agents reveal their preferences to meet according to three features: coherence, opinion difference, and agents’ positive sentiments towards others. Only preferred matches meet and exchange opinion. Through an extensive series of simulation treatments, it follows that the presence of sentiments, on one hand, hardens the matching process between agents, which leads to less communication. But, on the other hand, it increases the diversity in preferred matches between agents and thereby leads to a better-integrated social network structure, which reflects in a reduction of the opinion variance between agents. Moreover, at combinations of (a) high tolerance, (b) low sensitivity of agents to opinion volatility, and (c) low levels of confidence, agents are occasionally drawn away from the consensus, forming small groups that hold extreme opinions.

1171-P: The Challenges of Implementing a Clinic-Based Peer Support Program for Young People with T1D—The DiaBuddies Study

Aim: Many adolescents and young adults (AYAs) with T1D express a desire to meet others with T1D. We designed a pilot peer support program (DiaBuddies) and assessed feasibility of the match process and program implementation. Methods: Eligible participants (13-25 years old) were recruited and enrolled virtually. After informed consent, participants completed questions about preferred match type (peer vs mentor vs mentee), personal interests, and reasons for entry. Study staff “matched” AYAs 1:1 based on shared interests and mutual preferences on match type. Matches were introduced by email and encouraged to interact using any modality (e.g., text, video, in-person) and frequency they preferred. Study staff did not monitor interactions. Results: Eighteen AYAs (94% female, 89% non-Hispanic white, age 18.9±6.0, T1D duration 8.5±3.3 years) were enrolled from April-December 2022. Median time between first contact and consent was 14 days (range 4-63). The majority (83%) preferred a same-age peer match. Reasons for participation included: not knowing many people with T1D, wanting to discuss T1D with people who shared their experiences, and feeling eager to give advice and support. Six pairs (n=12) have been matched; median time between consent and match was 40 days (range 25-133). Unmatched participants (n=6) have been waiting a median of 131 days (range 21-196) for a suitable match. During the study, 2 participants expressed disappointment that their match was not responsive to messages, and 5 participants expressed excitement about knowing someone new with T1D. Conclusion: The DiaBuddies pilot study presented challenges with virtual recruitment and matching AYAs on shared interests. Open-ended questions limited opportunity to match objectively, affecting participant wait time and possibly decreasing enthusiasm. Future programs should incorporate lessons learned by modifying the match process to better facilitate AYAs’ desired type and frequency of peer support. Disclosure P.Commissariat: None. H.L.Owens: None. L.K.Volkening: None. L.M.Laffel: Advisory Panel; Medtronic, Lilly Diabetes, Novo Nordisk, Vertex Pharmaceuticals Incorporated, Roche Diagnostics, Provention Bio, Inc., Consultant; Dexcom, Inc., Janssen Pharmaceuticals, Inc., Medscape. Funding JDRF; National Institutes of Health (P30DK036836)

Influence of multi-aspect job preference matching on job tenure for people with mental disorders in supported employment programs in Japan.

This study examined the association between job tenure and job preference matching for five job preference domains for people with mental disorders enrolled in Individual Placement and Support (IPS) programs in Japan. The domains include occupation type, monthly income, weekly work hours, commute time, and illness disclosure. We conducted secondary analysis of participants who obtained employment in a longitudinal study during the 24-month follow-up period at 16 agencies routinely providing IPS programs. We included 112 participants who expressed job preferences and were employed at least once. A total of 130 employment cases were analyzed. Matches in the five domains were determined using participants' job preferences and employment information. The Match Level (0-5) indicates the number of domains that match the participant's job preferences. Job tenure (weeks worked) was compared between the matched and unmatched groups in each domain and between each match levels using linear regression mixed-effects models. A match for a given domain did not show a significant relationship with job tenure, whereas Match Levels 3 (B = 29.6, 95% CI [10.8, 48.4], p = .003) and 4 (B = 37.0, 95% CI [17.1, 56.9], p < .001) had a significantly longer tenure than those with Match Level 1. A higher match level may be related to a longer job tenure. The results suggest that employment specialists should prioritize clients' preferences in job searches. Further replication studies in other settings and countries should be conducted to verify the findings in this study. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Early vs Single Match in the Transition to Residency: Analysis Using NRMP Data From 2014 to 2021.

An Early Result Acceptance Program (ERAP) has been proposed for obstetrics and gynecology (OB/GYN) to address challenges in the transition to residency. However, there are no available data-driven analyses on the effects of ERAP on the residency transition. We used National Resident Matching Program (NRMP) data to simulate the outcomes of ERAP and compare those to what occurred in the Match historically. We simulated ERAP outcomes in OB/GYN, using the de-identified applicant and program rank order lists from 2014 to 2021, and compared them to the actual NRMP Match outcomes. We report outcomes and sensitivity analyses and consider likely behavioral adaptations. Fourteen percent of applicants receive a less preferred match under ERAP, while only 8% of applicants receive a more preferred match. Less preferred matches disproportionately affect DOs and international medical graduates (IMGs) compared to US MD seniors. Forty-one percent of programs fill with more preferred sets of applicants, while 24% fill with less preferred sets of applicants. Twelve percent of applicants and 52% of programs are in mutually dissatisfied applicant-program pairs (a pair in which both prefer each other to the match each received). Seventy percent of applicants who receive less preferred matches are part of a mutually dissatisfied pair. In 75% of programs with more preferred outcomes, at least one assigned applicant is part of a mutually dissatisfied pair. In this simulation, ERAP fills most OB/GYN positions, but many applicants and programs receive less preferred matches, and disparities increase for DOs and IMGs. ERAP creates mutually dissatisfied applicant-program pairs and problems for mixed-specialty couples, which provides incentives for gamesmanship.

Preference-Matched Multitask Assignment for Group Socialization under Mobile Crowdsensing.

Mobile crowdsensing (MCS) has been an emerging sensing paradigm in recent years, which uses a sensing platform for real-time processing to support various services for the Internet of Things (IoT) and promote the development of IoT. As an important component of MCS, how to design task assignment algorithms to cope with the coexistence of multiple concurrent heterogeneous tasks in group-oriented social relationships while satisfying the impact of users' preferences on heterogeneous multitask assignment and solving the preference matching problem under heterogeneous tasks, is one of the most pressing issues. In this paper, a new algorithm, group-oriented adjustable bidding task assignment (GO-ABTA), is considered to solve the group-oriented bilateral preference-matching problem. First, the initial leaders and their collaborative groups in the social network are selected by group-oriented collaboration, and then the preference assignment of task requesters and users is modeled as a stable preference-matching problem. Then, a tunable bidding task assignment process is completed based on preference matching under budget constraints. Finally, the individual reasonableness, stability, and convergence of the proposed algorithm are demonstrated. The effectiveness of the proposed algorithm and its superiority to other algorithms are verified by simulation results.

Uberisation of Private Homes? Workplace Location and the Distribution of Workplace Costs Between Employers and Employees – the German Case

Digitalisation and the COVID-19 pandemic have increased the use of telework. Employees often use private resources for working from home (WFH). However, we know little about how employers and employees divide telework-related workplace costs, whether employees are burdened with workplace costs in a trend of 'Uberisation of private homes', or whether there are legal or economic reasons to have employees bear the workplace cost. We first analyse Germany's legal framework. Second, we estimate the compositions and levels of workplace costs in different scenarios. We then analyse the allocation of workplace costs from the perspectives of the de facto legal situation, risk allocation and preference matching. We show that, for the most common telework types, the assumption of costs is insufficiently regulated. In practice, there is often only an implicit understanding that employees bear the workplace costs – which runs partly against the legal framework. Cost assumption by employees is efficient only in special conditions – if the employee is free to decide on workplace location and thus whether telework is voluntary. We advocate an obligation to conclude an agreement on whether the employer or the employee decides on the workplace location, whether the employer will reimburse (parts of) the workplace costs, and, if so, which parts of the costs will be borne. Such an agreement would make workplace cost negotiation mandatory, reduce uncertainty, and help improve preference matching, thereby increasing the efficiency of spatial workplace organisation.

Food and Beer Matching to Promote Destinations: A Central and Eastern European Perspective

The purpose of the study is to determine and evaluate Central and Eastern European (CEE) consumers’ food and beer matching preferences with a view to encouraging tourism stakeholders to promote local destinations. A literature review determined the characteristics of beer that interacted with different types of food and revealed issues for consideration when matching beer with food. A series of tasting sessions was conducted with 214 hospitality and tourism, food and beverage educators and industrialists from six CEE countries, comprising roughly equal numbers of males and females aged from 20 to 64. Seven foods (oily, acidic, salty, mild and spicy high protein, high fat, sweet) were offered alongside seven styles of beer. The tasters’ preferred matches were recorded. The most popular match (67.3%) was dark chocolate with Belgian fruit beer followed by sausages with both Bohemian pilsner and Hefeweizen beer. Every beer was matched by some tasters with every food; the least popular match (7.5%) was spicy meatballs with Belgian fruit beer. Further research into food and beer matching is recommended to validate the findings and to benefit publicans and restaurateurs. As it focuses on food and beer matching, as opposed to pairing, is located in CEE and suggests ways in which microbreweries in CEE can promote health and sustainable tourism, the research is original. Recommendations are made for stakeholders including the managers of F&B outlets and destination managers, who are advised to operationalise sense of place by developing a unique, dedicated toolkit to inform destination branding, and for further research.

Techno-invasion and job satisfaction in China: The roles of boundary preference for segmentation and marital status

BACKGROUND: While the intensive work-related use of information and communication technologies after working hours have led to increased techno-invasion, much less is known regarding whether and for whom techno-invasion influences job satisfaction. OBJECTIVE: Drawing on the conservation of resources theory and person-environment fit theory, this study examined the relationship between techno-invasion and decreased job satisfaction. Specific attention was paid to the moderating effect of boundary preference for segmentation and its joint influence with marital status on this relationship. METHODS: Questionnaire data were collected by an online survey of a nationwide and diverse sample of 472 employees from China. Data were analyzed using descriptive statistics, confirmatory factor analysis and hierarchical regression analysis. RESULTS: We found that techno-invasion negatively correlated with job satisfaction, which was strengthened by boundary preference for segmentation. Furthermore, the results of a three-way interaction effect suggested that the moderating role of boundary preference for segmentation on the relationship between techno-invasion and job satisfaction is stronger for unmarried employees than it is for married ones. CONCLUSIONS: The findings expand the understanding of the mechanisms underlying the relationship between techno-invasion and employee satisfaction in the Chinese context. Moreover, the findings have important implications for employers and employees that the interaction and matching of work environment, family environment, and individual preferences need to be considered to reduce the negative impact of techno-invasion on job satisfaction.

Food security and the cultural heritage missing link

Though enormous strides have been achieved in recent decades towards reducing food insecurity in the Global South, continued efforts are imperative in light of rapidly expanding populations and threats posed by climate change. A relatively unexplored area in this arena is the nexus between cultural heritage and food security. Cultural heritage embodies indigenous culture, values, and traditions inherited from previous generations. We focus on rice and identify five pathways through which cultural heritage affects food security. Although policy makers face the complex task of balancing trade-offs between preserving cultural heritage and productivity, they can harness cultural heritage to enhance food security by supporting (i) preservation of genetic resources, (ii) valorization, (iii) traditional food processing, (iv) preference matching, and (v) agritourism.

A Novel Drone-Station Matching model in Smart Cities based on Strict Preferences

There has been a considerable increase in the use of drones, or unmanned aerial vehicles (UAVs), in recent times, for a wide variety of purposes such as security, surveillance, delivery, search and rescue operations, penetration of inaccessible or unsafe areas, etc. The increasing number of drones working in an area poses a challenge to finding a suitable charging or resting station for each drone after completing its task or when it goes low on its charge. The classical methodology followed by drones is to return to their pre-assigned charging station every time it requires a station. This approach is found to be inefficient as it leads to an unnecessary waste of time as well as power, which could be easily saved if the drone is allotted a nearby charging station that is free. Therefore, we propose a drone-allocation model based on a preference matching algorithm where the drones will be allotted the nearest available station to land if the station is free. The problem is modeled as three entities: Drones, system controllers and charging stations. The matching algorithm was then used to design a Drone-Station Matching model. The simulation results of our proposed model showed that there would be considerably less power consumption and more time saving over the conventional system. This would save its travel time and power and ensure more efficient use of the drone.