https://arxiv.org/api/uTiXy8Oe4auptcnswMTLX1keZhY 2026-09-25T16:59:52Z 12 70 0 http://arxiv.org/abs/2608.19545v1 2026-08-20T01:25:03Z

Autonomous conversational agents and generative-AI features are being added to online dating platforms faster than public evidence about user attitudes can accumulate, and the scarcest evidence concerns the receiving side: how people react when the profiles, messages, or conversation partners they encounter are machine-generated. We release two anonymized survey datasets collected from active users of Fledge.Love, a dating platform serving an international user base. The first (N = 2,617; Russian and English forms) measures receptivity to autonomous conversational agents with a seven-item battery that separates the principal role (deploying one's own agent) from the counterpart role (encountering someone else's), plus six ordinal covariates and two auxiliary items. The second (N = 2,894) measures interest in three passive generative-AI features. The release includes model-derived scores for 2,499 complete cases, a bilingual codebook, a documented anonymization pipeline with a k-anonymity audit, executable analysis notebooks, and canonical outputs, supporting reuse in human-AI communication, recommender-systems, and cross-cultural technology-acceptance research.

2026-08-20T01:25:03Z Daria Leshchikova Valentina V. Kuskova Dmitry Zaytsev Valerii Klimov http://arxiv.org/abs/2608.18058v1 2026-08-18T17:51:16Z

Autonomous LLM agents that converse on a user's behalf are an emerging design pattern in matching platforms, yet their viability depends on a condition rarely examined: users must accept not only delegating conversation to an agent, but also receiving agent-mediated communication from others. We study this condition using two large-scale surveys of active users of a major dating platform (N=2,894 on generative profile features; N=2,617 on autonomous conversational agents, fielded in two languages). We develop a latent-variable measurement model of agent receptivity based on graded response models with latent regression, and show via model comparison that willingness to send and willingness to receive agent communication are distinct constructs: highly correlated (rho=0.92) but separable (Delta BIC=52), with partial measurement invariance across languages. The model quantifies a systematic delegation asymmetry: deploying one's own agent requires far lower receptivity (threshold -0.38) than engaging a counterpart's agent (+0.32; full engagement +1.39), and mean deployment propensity exceeds engagement propensity roughly threefold. Under a random-pairing counterfactual derived from stated receptivity, only 4-13% of directed dyads combine agent deployment with receiver engagement, with a pronounced gender-directional imbalance. Design counterfactuals quantify the levers: a reciprocity requirement cuts interaction volume by half or more by excluding nearly two-thirds of would-be deployment, while routing agent contacts on receive receptivity triples per-contact engagement, a lift that survives out-of-sample validation with the target item held out (AUC 0.88, 3.1x quartile lift under respondent-level cross-validation). We discuss implications for agentic recommender design, including disclosure, opt-in mechanics, and receptivity-aware matchmaking.

2026-08-18T17:51:16Z Daria Leshchikova Valentina V. Kuskova Dmitry Zaytsev Valerii Klimov http://arxiv.org/abs/2608.01731v1 2026-08-03T05:54:55Z

Matching platforms such as job posting services and online dating platforms have become widely used over the past decade. For a matching platform to be successful, it is crucial to design appropriate reciprocal recommendation systems (RRSs) that consider the preferences of users on both sides (job candidates and employers) and prevent opportunities from being concentrated too heavily on a few popular users. However, prioritizing concentration mitigation too much can lead to recommending undesirable results to some individual users, resulting in their dissatisfaction. In this paper, we formulate the concept of ``optimality of direct effects'' of the recommendation list for an individual user, given the recommendations to other users. Furthermore, we propose a novel method, MODE, that computes mutually optimal recommendations in direct effects. Experiments with synthetic and real-world data demonstrate that MODE surpasses other existing methods in terms of mutual optimality of direct effects, exhibits faster processing speeds, and enables a higher expected number of matches.

2026-08-03T05:54:55Z Accepted at RecSys2026 Yoji Tomita http://arxiv.org/abs/2606.31802v1 2026-06-30T15:20:03Z

Many bipartite social networks exhibit pronounced asymmetries in selectivity and matching opportunities: members of one side can afford to be highly selective, while members of the opposite side are forced to accept less desirable matches. While it is natural to try to explain this asymmetry in terms of the intrinsic characteristics of the two sides or other exogenous factors, here we show that such asymmetries can also emerge endogenously through a feedback process generated by the matching process itself: as one side becomes more selective, the other side is pushed to be less selective due to reduced matching opportunities, and vice versa. We develop a model in which individuals repeatedly form one-to-one matches across two groups and adapt their selectivity to achieve a target matching rate. Using both analytic and numerical methods, we show that when encounters are sufficiently frequent, the unique equilibrium is for one group to be highly selective and the other non-selective. This qualitative outcome holds even for heterogeneous groups with overlapping, almost indistinguishable distributions of target matching rates. The model makes several testable predictions, and it provides a mechanism for behavioral differentiation in repeated matching environments, with applications ranging from online dating to hiring and housing markets.

2026-06-30T15:20:03Z 26 pages, 7 figures Alexandros Gelastopoulos http://arxiv.org/abs/2606.23241v1 2026-06-22T12:25:12Z

The growth of mobile dating platforms has coincided with a rise in romance scams, in which offenders construct convincing personas to defraud users. While research on romance scams is expanding, victims lived experiences of recognizing and responding to deception in mobile-mediated interactions remain insufficiently understood. To address this gap, we conducted indepth interviews with 24 victims of online dating romance scams in Iran, where legal, social, and cultural constraints limit formal support. Our analysis identifies suspicion cues and the investigative strategies victims use to verify identities across platforms. We show that victims are not passive recipients of deception but engage in active, iterative detection practices under significant emotional, social, and relational pressure. Based on these findings, we contribute empirically grounded insights into deception cues and user driven detection work, and we discuss implications for the design of mobile technologies that better support users in identifying, resisting, and recovering from romance scams. Content Warning, This paper discusses sexual violence

2026-06-22T12:25:12Z Proc. ACM Hum.-Comput. Interact. 10, 5, Article MHCI8950, August 2026 Sima Amirkhani Jana Krüger Dave Randall Gunnar Stevens Douglas Zytko 10.1145/38217 http://arxiv.org/abs/2605.22587v1 2026-05-21T14:59:14Z

In its early days, online dating was heralded as a great equalizer, removing biases built into the structures of heterosexuality courtship. However, as repeatedly observed, that prophecy was never fulfilled, and some biases have even become exacerbated. In this paper, we identify a general endogenous mechanism that drives the widening of the gender gap in first-contact rates of heterosexual dating. This mechanism relies on assumptions about the participants' expectations of new contacts and their time constraints. We formulate this symmetry-breaking mechanism as a system of differential equations and analyze its fixed points and their stability.

2026-05-21T14:59:14Z Huaiyu Tan Chenyu Li Taha Yasseri Lei Shi Petter Holme http://arxiv.org/abs/2603.26727v1 2026-03-20T03:31:07Z

Online dating has become the dominant way romantic relationships begin, yet current platforms strip the nonverbal cues: gaze, facial expression, body posture, response timing, that humans rely on to signal comfort, disinterest, and consent, creating a communication gap with disproportionate safety consequences for women. We argue that this gap represents both a technical opportunity and a moral responsibility for the computer vision community, which has developed the affective tools, facial action unit detection, gaze estimation, engagement modeling, and multimodal affect recognition, needed to begin addressing it, yet has largely ignored the dating domain as a research context. We propose a fairness-first research agenda organized around four capability areas: real-time discomfort detection, engagement asymmetry modeling between partners, consent-aware interaction design, and longitudinal interaction summarization, each grounded in established CV methodology and motivated by the social psychology of romantic communication. We argue that responsible pursuit of this agenda requires purpose-built datasets collected under dyadic consent protocols, fairness evaluation disaggregated across race, gender identity, neurotype, and cultural background, and architectural commitments to on-device processing that prevent affective data from becoming platform surveillance infrastructure. This vision paper calls on the WICV community, whose members are uniquely positioned to understand both the technical opportunity and the human stakes, to establish online dating safety as a first-class research domain before commercial deployment outpaces ethical deliberation.

2026-03-20T03:31:07Z CVPR Humans of Generative AI (HuG) Workshop 2026 Ratna Kandala Niva Manchanda Akshata Kishore Moharir http://arxiv.org/abs/2602.15752v3 2026-05-19T05:54:23Z

On two-sided matching platforms such as online dating and recruiting, recommendation algorithms often aim to maximize the total number of matches. However, this objective creates an imbalance, where some users receive far too many matches while many others receive very few and eventually abandon the platform. Retaining users is crucial for many platforms, such as those that depend heavily on subscriptions. Some may use fairness objectives to solve the problem of match maximization. However, fairness in itself is not the ultimate objective for many platforms, as users do not suddenly reward the platform simply because exposure is equalized. In practice, where user retention is often the ultimate goal, casually relying on fairness will leave the optimization of retention up to luck. In this work, instead of maximizing matches or axiomatically defining fairness, we formally define the new problem setting of maximizing user retention in two-sided matching platforms. To this end, we introduce a dynamic learning-to-rank (LTR) algorithm called Matching for Retention (MRet). Unlike conventional algorithms for two-sided matching, our approach models user retention by learning personalized retention curves from each user's profile and interaction history. Based on these curves, MRet dynamically adapts recommendations by jointly considering the retention gains of both the user receiving recommendations and those who are being recommended, so that limited matching opportunities can be allocated where they most improve overall retention. Naturally but importantly, empirical evaluations on synthetic and real-world datasets from a major online dating platform show that MRet achieves higher user retention, since conventional methods optimize matches or fairness rather than retention.

2026-02-17T17:30:53Z Published as a conference paper at ICLR 2026 Ren Kishimoto Rikiya Takehi Koichi Tanaka Masahiro Nomura Riku Togashi Yoji Tomita Yuta Saito http://arxiv.org/abs/2601.16321v1 2026-01-22T21:08:17Z

Minors are at risk of myriad harms online, yet online dating romance scams are seldom considered one of them. While research of romance scams in Western countries finds victims to predominantly be middle-age, it is unknown if minors in geographic regions with cultural norms around teenage marriage are uniquely susceptible to online dating romance scams. We present an interview study with 16 victims of online dating romance scams in Iran who were minors when scammed. Findings show that, with westernized dating apps banned in Iran, scammers find teenage victims through messaging platforms tethered to local neighborhoods, offering relief for parental pressures around finding a marital partner and academic performance. Using threats, lies, and exploitation of emotional attachment lacking from their families, scammers pressured minors into financial and sexual favors. The study demonstrates how local cultural context should be foregrounded in future research on, and solutions for, technology-mediated harm against minors. Content Warning: This paper discusses sexual abuse.

2026-01-22T21:08:17Z 22 pages, 3 tables, CHI 2026 conference Sima Amirkhani Mahla Fatemeh Alizadeh Dave Randall Gunnar Stevens Douglas Zytko http://arxiv.org/abs/2601.13609v1 2026-01-20T05:18:25Z

Matching platforms, such as online dating services and job recommendations, have become increasingly prevalent. For the success of these platforms, it is crucial to design reciprocal recommender systems (RRSs) that not only increase the total number of matches but also avoid creating unfairness among users. In this paper, we investigate the fairness of RRSs on matching platforms. From the perspective of fair division, we define the users' opportunities to be recommended and establish the fairness concept of envy-freeness in the allocation of these opportunities. We first introduce the Social Welfare (SW) method, which approximately maximizes the number of matches, and show that it leads to significant unfairness in recommendation opportunities, illustrating the trade-off between fairness and match rates. To address this challenge, we propose the Nash Social Welfare (NSW) method, which alternately optimizes two NSW functions and achieves nearly envy-free recommendations. We further generalize the SW and NSW method to the $α$-SW method, which balances the trade-off between fairness and high match rates. Additionally, we develop a computationally efficient approximation algorithm for the SW/NSW/$α$-SW methods based on the Sinkhorn algorithm. Through extensive experiments on both synthetic datasets and two real-world datasets, we demonstrate the practical effectiveness of our approach.

2026-01-20T05:18:25Z arXiv admin note: text overlap with arXiv:2409.00720 ACM Transactions on Recommender Systems, 2026, forthcoming Yoji Tomita Tomohiko Yokoyama 10.1145/3793538 http://arxiv.org/abs/2507.01063v2 2026-01-28T16:02:39Z

Online dating platforms have fundamentally transformed the formation of romantic relationships, with millions of users worldwide relying on algorithmic matching systems to find compatible partners. However, current recommendation systems in dating applications suffer from significant algorithmic deficiencies, including but not limited to popularity bias, filter bubble effects, and inadequate reciprocity modeling that limit effectiveness and introduce harmful biases. This research integrates foundational work with recent empirical findings to deliver a detailed analysis of dating app recommendation systems, highlighting key issues and suggesting research-backed solutions. Through analysis of reciprocal recommendation frameworks, fairness evaluation metrics, and industry implementations, we demonstrate that current systems achieve modest performance with collaborative filtering reaching 25.1\% while reciprocal methods achieve 28.7\%. Our proposed mathematical framework addresses these limitations through enhanced similarity measures, multi-objective optimization, and fairness-aware algorithms that maintain competitive accuracy while improving demographic representation to reduce algorithmic bias.

2025-06-30T10:36:57Z This paper is withdrawn due to factual/methodological/other critical errors. It primarily synthesizes literature without original empirical validation/novel contributions (e.g., similarity measures from Xia et al.[2]; 28.7% claims from benchmarks). Core "proposed framework" lacks independent experiments, invalidating analysis. Do not cite prior versions Madhav Kotecha http://arxiv.org/abs/2502.11430v1 2025-02-17T04:39:47Z

Online dating is frequently used by individuals looking for potential relationships and intimate connections. Central to dating apps is the creation and refinement of a dating profile, which represents the way individuals desire to present themselves to potential mates, while hiding information they do not care to share. To investigate the way frequent users of dating apps construct their online profiles and perceive the effectiveness of strategies taken in making profiles, we conducted semi-structured interviews with 20 experienced users who are Chinese college-educated young adults and uncovered the processes and rationales by which they make profiles for online dating, particularly in selecting images for inclusion. We found that participants used idealized photos that exaggerated their positive personality traits, sometimes traits that they do not possess but perceive others to desire, and sometimes even traits they wish they had possessed. Users also strategically used photos that show personality and habits without showing themselves, and often hid certain identifying information to reduce privacy risks. This analysis signals potential factors that are key in building online dating profiles, providing design implications for systems that limit the use of inaccurate information while still promoting self-expression in relationship platforms.

2025-02-17T04:39:47Z 30 pages, 6 figures, to be published in CSCW 2025 Fan Zhang Yun Chen Xiaoke Zeng Tianqi Wang Long Ling RAY LC