Journal articles: 'Sequential behaviors'

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Kim, Byung-Jik. "Unstable Jobs Cannot Cultivate Good Organizational Citizens: The Sequential Mediating Role of Organizational Trust and Identification." International Journal of Environmental Research and Public Health 16, no. 7 (March 27, 2019): 1102. http://dx.doi.org/10.3390/ijerph16071102.

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Although existing works have investigated the influence of employee’s job insecurity on his or her perceptions or attitudes, those studies relatively have paid less attention to the influence of it on employee’s behaviors, as well as to its intermediating mechanisms of the relationship between job insecurity and the behaviors. Considering that employee’s behaviors substantially influence various organizational outcomes, I believe that studies which examine the impact of job insecurity on the behaviors as well as its underlying processes are required. Grounded on the context–attitude–behavior framework, I delved into the intermediating mechanism between job insecurity and organizational citizenship behavior with a sequential mediation model. In specific, I hypothesized that employee’s organizational trust and organizational identification would sequentially mediate the job insecurity–organizational citizenship behavior (OCB) link. Utilizing 3-wave time-lagged data from 303 employees in South Korea, I found that organizational trust and organizational identification function as sequential mediators in the link. The finding suggests that organizational trust and organizational identification are underlying processes to elaborately explain the job insecurity–OCB link.

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Jeon, Daejong, Jiye Choi, Ah Reum Yang, Jung-Seok Yoo, Sangwoo Kim, Sang Kun Lee, and Kon Chu. "Chronic social stress during early development elicits unique behavioral changes in adulthood." encephalitis 2, no. 2 (April 10, 2022): 45–53. http://dx.doi.org/10.47936/encephalitis.2021.00178.

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PurposeChronic social stress is known to induce inflammation in the brain, and early-life stress affects the brain and social behavior in adulthood. To study the relationship between social stress in childhood development and social behavior in adulthood, we subjected mice to a sequential early-life social stresses and characterized their adult behavioral phenotypes.MethodsC57BL/6 mice were sequentially subjected to maternal separation (MS), social defeat (SD), and social isolation (SI) in that order. The body weights of the MS/SD/SI mice were measured. Behavioral tasks related to anxiety, depression, locomotion, learning/memory, and repetitive/compulsive-like behavior were conducted. Social behaviors suggesting sociability, social interaction, aggression, and social fear were investigated. ResultsMS/SD/SI mice weighed less at 7 and 8 weeks of age. These mice displayed normal behaviors in anxiety-, depression-, and learning/memory-related tasks, but they exhibited increased locomotor activity and a low level of repetitive/compulsive-like behavior. Notably, they exhibited increased social interaction, impaired empathy-related fear, reduced predator fear, and increased defensive aggressiveness.ConclusionSocial stress during childhood development resulted in behavioral alterations, and MS/SD/SI mice generated by mimicking child abuse or maltreatment showed unique abnormalities in social behaviors. MS/SD/SI mice might be useful not only to study the relationship between social stress and brain inflammation but also psychosocial behaviors observed in individuals with brain disorders, such as psychopaths.

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Branco, Tatiane, Daniella Jorge de Moura, Irenilza de Alencar Nääs, Nilsa Duarte da Silva Lima, Daniela Regina Klein, and Stanley Robson de Medeiros Oliveira. "The Sequential Behavior Pattern Analysis of Broiler Chickens Exposed to Heat Stress." AgriEngineering 3, no. 3 (June 25, 2021): 447–57. http://dx.doi.org/10.3390/agriengineering3030030.

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Broiler productivity is dependent on a range of variables; among them, the rearing environment is a significant factor for proper well-being and productivity. Behavior indicates the bird’s initial response to an adverse environment and is capable of providing an indicator of well-being in real-time. The present study aims to identify and characterize the sequential pattern of broilers’ behavior when exposed to thermoneutral conditions (TNZ) and thermal stress (HS) by constant heat. The research was carried out in a climatic chamber with 18 broilers under thermoneutral conditions and heat stress for three consecutive days (at three different ages). The behavior database was first analyzed using one-way ANOVA, Tukey test by age, and Boxplot graphs, and then the sequence of the behaviors was evaluated using the generalized sequential pattern (GSP) algorithm. We were able to predict behavioral patterns at the different temperatures assessed from the behavioral sequences. Birds in HS were prostrate, identified by the shorter behavioral sequence, such as the {Lying down, Eating} pattern, unlike TNZ ({Lying down, Walking, Drinking, Walking, Lying down}), which indicates a tendency to increase behaviors (feeding and locomotor activities) that guarantee the better welfare of the birds. The sequence of behaviors ‘Lying down’ followed by ‘Lying laterally’ occurred only in HS, which represents a stressful thermal environment for the bird. Using the pattern mining sequences approach, we were able to identify temporal relationships between thermal stress and broiler behavior, confirming the need for further studies on the use of temporal behavior sequences in environmental controllers.

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Kumar, R., V. Garg, and S. I. Marcus. "On supervisory control of sequential behaviors." IEEE Transactions on Automatic Control 37, no. 12 (1992): 1978–85. http://dx.doi.org/10.1109/9.182487.

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Chien, Yu-Hung, Chun-Kai Yao, and Yu-Han Chao. "Effects of Multidisciplinary Participatory Design Method on Students’ Engineering Design Process." Eng 1, no. 2 (October 10, 2020): 112–21. http://dx.doi.org/10.3390/eng1020007.

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This study took the ergonomics design course as an example to propose a design teaching model of multidisciplinary participatory design (MPD), and investigated the effects of this teaching model on the engineering design behavior of college students. We used lag behavior sequential analysis to compare the design behaviors of three student groups: a participatory design (PD) experimental group, an MPD experimental group, and a control group. The results of the study show that (1) students in the PD experimental group had 13 significant sequential engineering design behaviors, students in the MPD experimental group had 10, and students in the control group had only seven. The engineering design behaviors of the experimental groups were more diversified than those of the control group. (2) The three groups of students had a small number of significant design behavior transfers in the engineering design process, indicating that the students’ sequential design behaviors between two different design activities were insufficient. We concluded by detailing the pros and cons of using the MPD teaching model based on the results of this study, and hopefully by providing a reference for teaching engineering design.

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Wang, Bing-Yun, Yi-Chun Yen, and Yu Chin Cheng. "Specifying Internet of Things Behaviors in Behavior-Driven Development: Concurrency Enhancement and Tool Support." Applied Sciences 13, no. 2 (January 5, 2023): 787. http://dx.doi.org/10.3390/app13020787.

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The Internet of Things (IoT) systems are inherently distributed with many concurrent behaviors. In order to apply behavior-driven development (BDD), a proven agile practice of software development that brings many benefits, we must ensure that the specification of sequential and concurrent behaviors is supported at the specification level and that tool support is in place to execute the specification. This study proposes a minimal semantic enhancement to the Gherkin language, the most popular specification language in BDD, to distinguish sequential and concurrent behaviors. At the same time, a tool called concurrentSpec is developed to support the correct execution of specifications written in the enhanced Gherkin language. With two IoT examples involving both sequential and concurrent behaviors, it is shown that the enhanced Gherkin with concurrentSpec can correctly specify and execute the specifications, while the original Gherkin with existing tools is unable to do so. Hence, the contribution of this study is to eliminate a technical impediment for the IoT development community to adopt BDD and receive its benefits.

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Chen, Zhengxing, Magy Seif El Nasr, Alessandro Canossa, Jeremy Badler, Stefanie Tignor, and Randy Colvin. "Modeling Individual Differences through Frequent Pattern Mining on Role-Playing Game Actions." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 11, no. 5 (June 24, 2021): 2–7. http://dx.doi.org/10.1609/aiide.v11i5.12847.

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There has been much work on player modeling using game behavioral data collected. Many of the previous research projects that targeted this goal used aggregate game statistics as features to develop behavior models using both statistical and machine learning techniques. While existing methods have already led to interesting findings, we suspect that aggregated features discard valuable information such as temporal or sequential patterns, which may be important in deciphering information about decisionmaking, problem solving, or individual differences. Such sequential information is critical to analyze player behaviors especially in role-playing games (RPG) where players can face ample choices, experience different contexts, behave freely with individual propensities but possibly end up with similar aggregated statistics (e.g., levels, time spent). In this paper we intend to develop and apply a modeling technique that takes into consideration sequential patters to decipher individual differences in playing a Role Playing Game (RPG) game. Using an RPG with multiple affordances, we designed an experiment collecting granular in-game behaviors of 64 players. Using closed sequential pattern mining and logistic regression, we developed a model that uses gameplay action sequences to predict the real world characteristics, including gender, game play expertise and five personality traits (as defined by psychology). The results show that game expertise is a dominant factor that impacts in-game behaviors. The contribution of this paper is the algorithms we developed combined with a validation procedure to determine the reliability and validity of the results and the results themselves.

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Slate, John R., and Richard A. Saudargas. "Classroom Behaviors of LD, Seriously Emotionally Disturbed, and Average Children: A Sequential Analysis." Learning Disability Quarterly 10, no. 2 (May 1987): 125–34. http://dx.doi.org/10.2307/1510219.

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The regular classroom behaviors of LD, seriously emotionally disturbed, and average children were observed via a system that permitted sequential analysis of the data. A minimum of 80 minutes of classroom observational data was collected on each of 52 children. A lag sequential analysis was subsequently performed to determine regularities and differences in the sequential ordering of behaviors across time. The results revealed that the teachers behaved differentially toward the handicapped but not the average children. When the handicapped children were engaged in schoolwork, the teachers tended not to interact with them. However, when the handicapped students were not on task, the teachers tended to interact with them. These findings were not evident in the summary level analysis of the data. Lag sequential analysis appears to enhance our understanding of the organization of behavior within the classroom setting.

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Burridge, R. R., A. A. Rizzi, and D. E. Koditschek. "Sequential Composition of Dynamically Dexterous Robot Behaviors." International Journal of Robotics Research 18, no. 6 (June 1999): 534–55. http://dx.doi.org/10.1177/02783649922066385.

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Ruh, Nicolas, Richard P. Cooper, and Denis Mareschal. "Action selection in complex routinized sequential behaviors." Journal of Experimental Psychology: Human Perception and Performance 36, no. 4 (2010): 955–75. http://dx.doi.org/10.1037/a0017608.

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Alibasa, Muhammad J., Rafael A. Calvo, and Kalina Yacef. "Sequential Pattern Mining Suggests Wellbeing Supportive Behaviors." IEEE Access 7 (2019): 130133–43. http://dx.doi.org/10.1109/access.2019.2939960.

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Fukuda, T., Y. Nakauchi, K. Noguchi, and T. Matsubara. "Recognition and Support of Sequential Human Behaviors." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2004 (2004): 184. http://dx.doi.org/10.1299/jsmermd.2004.184_1.

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Lijowska, Anna S., Nevada W. Reed, Barbara A. Mertins Chiodini, and Bradley T. Thach. "Sequential arousal and airway-defensive behavior of infants in asphyxial sleep environments." Journal of Applied Physiology 83, no. 1 (July 1, 1997): 219–28. http://dx.doi.org/10.1152/jappl.1997.83.1.219.

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Lijowska, Anna S., Nevada W. Reed, Barbara A. Mertins Chiodini, and Bradley T. Thach. Sequential arousal and airway-defensive behavior of infants in asphyxial sleep environments. J. Appl. Physiol. 83(1): 219–228, 1997.—Infants are prone to accidental asphyxiation. Therefore, we studied airway-defensive behaviors and their relationship to spontaneous arousal behavior in 41 healthy sleeping infants (2–26 wk old), using two protocols: 1) infant was rebreathing expired air, face covered by bedding material; and 2) infant was exposed to hypercarbia, face uncovered. Multiple measurements of respiratory and motor activities were recorded (video, polygraph). The infants’ response to increasing hypercarbia consisted of four highly stereotyped behaviors: sighs (augmented breaths), startles, thrashing limb movements, and full arousal (eyes open, cry). These behaviors occurred abruptly in self-limited clusters of activity and always in the same sequence: first a sigh coupled with a startle, then thrashing, then full arousal. Incomplete sequences (initial behaviors only) occurred far more frequently than the complete sequence and were variably effective in removing the bedding covering the airway. In both protocols, as inspired CO2increased, incomplete arousal sequences recurred periodically and with increasing frequency and complexity until the infant either succeeded in clearing his/her airway or was completely aroused. Spontaneous arousal sequences, identical to those occurring during hypercarbia, occurred periodically during sleep. This observation suggests that the infant’s airway-defensive responses to hypercarbia consist of an increase in the frequency and complexity of an endogenously regulated, periodically occurring sequence of arousal behaviors.

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Chorney, Jill MacLaren, Edwin T. Tan, and Zeev N. Kain. "Adult–Child Interactions in the Postanesthesia Care Unit." Anesthesiology 118, no. 4 (April 1, 2013): 834–41. http://dx.doi.org/10.1097/aln.0b013e31827e501b.

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Abstract Background: Many children experience significant distress before and after surgery. Previous studies indicate that healthcare providers’ and parents’ behaviors may influence children’s outcomes. This study examines the influence of adults’ behaviors on children’s distress and coping in the postanesthesia care unit. Methods: Children aged 2–10 yr were videotaped during their postanesthesia care unit stay (n = 146). Adult and child behaviors were coded from video, including the onset, duration, and order of behaviors. Correlations were used to examine relations between behaviors, and time-window sequential statistical analyses were used to examine whether adult behaviors cued or followed children’s distress and coping. Results: Sequential analysis demonstrated that children were significantly less likely to become distressed after an adult used empathy, distraction, or coping/assurance talk than they were at any other time. Conversely, if a child was already distressed, children were significantly more likely to remain distressed if an adult used reassurance or empathy than they were at any other time. Children were more likely to display coping behavior (e.g., distraction, nonprocedural talk) after an adult used this behavior. Conclusions: Adults can influence children’s distress and coping in the postanesthesia care unit. Empathy, distraction, and assurance talk may be helpful in keeping a child from becoming distressed, and nonprocedural talk and distraction may cue children to cope. Reassurance should be avoided when a child is already distressed.

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Hashemiparast, Mina, Ali Montazeri, Saharnaz Nedjat, Reza Negarandeh, Roya Sadeghi, Masoumeh Hosseini, and Gholamreza Garmaroudi. "Pedestrian Road-Crossing Behaviours: A Protocol for an Explanatory Mixed Methods Study." Global Journal of Health Science 8, no. 5 (August 23, 2015): 27. http://dx.doi.org/10.5539/gjhs.v8n5p27.

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BACKGROUND: Pedestrian crossing is an important traffic safety concern. The aim of this paper is to report the protocol for a sequential explanatory mixed methods study that set out to determine the pedestrians’ traffic behaviors, the associated factors and exploring the perception of young people about the traffic risky behaviors in crossing the road. The ultimate purpose of the study is to design a preventive and cultural based strategy to promote young people’s health. METHODS: This is a sequential explanatory mixed methods design. The study has two sequential phases. During the first phase, a population-based cross-sectional survey of a sample of young people will be conducted using the proportional random multistage cluster sampling method, in Tehran, Iran. Data will be collected by a questionnaire including items on socio-demographic information, items on measuring social conformity tendency, and questions on subjective norms, attitudes, and perceived behavioral control based on the Theory of Planned behavior. In the second phase, a qualitative study will be conducted. A purposeful sampling strategy will be used and participants who can help to explain the quantitative findings will be selected. Data collection in qualitative phase will be predominately by individual in-depth interviews. A qualitative content analysis approach will be undertaken to develop a detailed understanding of the traffic risky behaviors among young pedestrians. CONCLUSION: The findings of this explanatory mixed methods study will provide information on traffic risky behaviors in young pedestrians. The findings will be implemented to design a cultural based strategy and intervention programs.

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Dai, Xu Dong, Zheng Shan Zhang, Xiang Hui Meng, Zhi Nan Zhang, and You Bai Xie. "Research on Multi-Disciplinary Behavior Coupling Model in IC Engine." Applied Mechanics and Materials 44-47 (December 2010): 2075–79. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.2075.

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The paper analyzes the coupling of multi-disciplinary behaviors, including system dynamical behavior, combustion behavior and tribological behavior, of the cylinder liner-piston-rod-crank system in IC engine. Based on the state equation method, multi-disciplinary behavior coupling model of the Cylinder-Piston-Rod-Crank system is constructed and a multi-disciplinary behavior coupling analysis method in IC engine is presented on the basis of the state equation. With the coupling analysis model, the coupling effect of multi-disciplinary behaviors can be considered in the two sequential state calculations. By means of state calculation in time domain, the coupling effect of multi-disciplinary behaviors on life performance of IC engine can be predicted.

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Huang, Xiaowen, Jitao Sang, Jian Yu, and Changsheng Xu. "Learning to Learn a Cold-start Sequential Recommender." ACM Transactions on Information Systems 40, no. 2 (April 30, 2022): 1–25. http://dx.doi.org/10.1145/3466753.

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The cold-start recommendation is an urgent problem in contemporary online applications. It aims to provide users whose behaviors are literally sparse with as accurate recommendations as possible. Many data-driven algorithms, such as the widely used matrix factorization, underperform because of data sparseness. This work adopts the idea of meta-learning to solve the user’s cold-start recommendation problem. We propose a meta-learning-based cold-start sequential recommendation framework called metaCSR, including three main components: Diffusion Representer for learning better user/item embedding through information diffusion on the interaction graph; Sequential Recommender for capturing temporal dependencies of behavior sequences; and Meta Learner for extracting and propagating transferable knowledge of prior users and learning a good initialization for new users. metaCSR holds the ability to learn the common patterns from regular users’ behaviors and optimize the initialization so that the model can quickly adapt to new users after one or a few gradient updates to achieve optimal performance. The extensive quantitative experiments on three widely used datasets show the remarkable performance of metaCSR in dealing with the user cold-start problem. Meanwhile, a series of qualitative analysis demonstrates that the proposed metaCSR has good generalization.

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Vu, Huy Quan, Gang Li, Rob Law, and Yanchun Zhang. "Travel Diaries Analysis by Sequential Rule Mining." Journal of Travel Research 57, no. 3 (February 1, 2017): 399–413. http://dx.doi.org/10.1177/0047287517692446.

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Because of the inefficiency in analyzing the comprehensive travel data, tourism managers are facing the challenge of gaining insights into travelers’ behavior and preferences. In most cases, existing techniques are incapable of capturing the sequential patterns hidden in travel data. To address these issues, this article proposes to analyze the travelers’ behavior through geotagged photos and sequential rule mining. Travel diaries, constructed from the photo sequences, can capture comprehensive travel information, and then sequential patterns can be discovered to infer the potential destinations. The effectiveness of the proposed framework is demonstrated in a case study of Australian outbound tourism, using a data set of more than 890,000 photos from 3,623 travelers. The introduced framework has the potential to benefit tourism researchers and practitioners from capturing and understanding the behaviors and preferences of travelers. The findings can support destination-marketing organizations (DMOs) in promoting appropriate destinations to prospective travelers.

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Petty, Jane, Debbie Allen, and Chris Oliver. "Relationship Among Challenging, Repetitive, and Communicative Behaviors in Children With Severe Intellectual Disabilities." American Journal on Intellectual and Developmental Disabilities 114, no. 5 (September 1, 2009): 356–68. http://dx.doi.org/10.1352/1944-7558-114.5.356.

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Abstract We used experimental and descriptive functional analyses and lag sequential analyses to examine the functional and temporal relationship among the self-injurious (SIB), potentially injurious, repetitive, challenging, and pragmatic communicative behaviors of 6 children with intellectual disabilities. Functional analyses revealed social function for SIB, potentially injurious, and repetitive behaviors across 5, 4, and 5 participants, respectively. Sixteen functionally equivalent response classes were identified across participants using both experimental and naturalistic observation data. Repetitive, potentially injurious, and SIB behaviors were significantly temporally associated, and pragmatic communicative behaviors were strongly temporally associated with challenging behaviors. The importance of the temporal and functional relationship between imperative communicative acts and challenging behavior is discussed.

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Liu, Wen, Yelena Perkhounkova, Maria Hein, and Roger Bakeman. "TEMPORAL RELATIONSHIPS OF PERSON- AND TASK-CENTERED DEMENTIA CARE AND MEALTIME BEHAVIORS: SEQUENTIAL ANALYSIS." Innovation in Aging 6, Supplement_1 (November 1, 2022): 134. http://dx.doi.org/10.1093/geroni/igac059.533.

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Abstract Person-centered mealtime care is highly recommended in dementia care. While current research examined associative relationships between person- and task-centered care and resident mealtime behaviors, few studies evaluated their temporal associations. Videotaped mealtime observations (N=160) involving 36 staff and 27 residents (53 staff-resident dyads) in 9 nursing homes were coded. Staff person-centered and task-centered approaches were conceptualized as antecedents of resident positive behaviors, functional impairments, and resistive behaviors using 5-, 10-, and 15-second time windows. Immediately after staff person-centered approaches, resident positive and resistive behaviors were more likely (p range=<.001–.29) and functional impairments less likely (p range=<.001–.62) with diminished effects in time. Immediately after staff task-centered approaches, resident positive behaviors were less likely (p range=<.001–.09). Person-centered mealtime care should be individualized, context-based, and resident-oriented. Resident resistiveness to care may be behavioral responses to person-centered care indicating mismatch to individual preferences and needs, warranting adequate awareness and appropriate assessment.

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Peng, Tai-Quan, and Jonathan J. H. Zhu. "Mobile Phone Use as Sequential Processes: From Discrete Behaviors to Sessions of Behaviors and Trajectories of Sessions." Journal of Computer-Mediated Communication 25, no. 2 (March 2020): 129–46. http://dx.doi.org/10.1093/jcmc/zmz029.

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Abstract Mobile phone use is an unfolding process by nature. In this study, it is explicated as two sequential processes: mobile sessions composed of an uninterrupted set of behaviors and mobile trajectories composed of mobile sessions and mobile-off time. A data set of a five-month behavioral logfile of mobile application use by approximately 2,500 users in Hong Kong is used. Mobile sessions are constructed and mined to uncover sequential characteristics and patterns in mobile phone use. Mobile trajectories are analyzed to examine intraindividual change and interindividual differences on mobile re-engagement as indicators of behavioral dynamics in mobile phone use. The study provides empirical support for and expands the boundaries of existing theories about combinatorial use of information and communication technologies (ICTs). Finally, the understanding on mobile temporality is enhanced, that is, mobile temporality is homogeneous across social sectors. Furthermore, mobile phones redefine, rather than blur, the boundary between private and public time.

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Mukhopadhyay, N., and M. E. Ekwo. "A Note on Minimum Risk Point Estimation of the Shape Parameter of a Pareto Distribution." Calcutta Statistical Association Bulletin 36, no. 1-2 (March 1987): 69–78. http://dx.doi.org/10.1177/0008068319870108.

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The minimum risk point estimation problem is considered for the shape parameter of a Pareto distribution where the Joss function is taken as squared error plus the linear cost of sampling. A suitable purely sequential procedure is proposed for this problem and the asrmptotic behavior of the “regret” function proposed by Robbins (1959) and many other characteristics are examined. An extensive numerical study is presented in order to look into moderate sample behaviors of the proposed sequential estimation procedure. The procedure is found to be very satisfactory.

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Cao, Longbing, and Chengzhang Zhu. "Personalized next-best action recommendation with multi-party interaction learning for automated decision-making." PLOS ONE 17, no. 1 (January 27, 2022): e0263010. http://dx.doi.org/10.1371/journal.pone.0263010.

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Automated next-best action recommendation for each customer in a sequential, dynamic and interactive context has been widely needed in natural, social and business decision-making. Personalized next-best action recommendation must involve past, current and future customer demographics and circumstances (states) and behaviors, long-range sequential interactions between customers and decision-makers, multi-sequence interactions between states, behaviors and actions, and their reactions to their counterpart’s actions. No existing modeling theories and tools, including Markovian decision processes, user and behavior modeling, deep sequential modeling, and personalized sequential recommendation, can quantify such complex decision-making on a personal level. We take a data-driven approach to learn the next-best actions for personalized decision-making by a reinforced coupled recurrent neural network (CRN). CRN represents multiple coupled dynamic sequences of a customer’s historical and current states, responses to decision-makers’ actions, decision rewards to actions, and learns long-term multi-sequence interactions between parties (customer and decision-maker). Next-best actions are then recommended on each customer at a time point to change their state for an optimal decision-making objective. Our study demonstrates the potential of personalized deep learning of multi-sequence interactions and automated dynamic intervention for personalized decision-making in complex systems.

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Rai, Alka, and Benjamin Nandy. "Employer brand to leverage employees’ intention to stay through sequential mediation model: evidence from Indian power sector." International Journal of Energy Sector Management 15, no. 3 (March 1, 2021): 551–65. http://dx.doi.org/10.1108/ijesm-10-2019-0024.

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Purpose This study aims to explain the linkage between employer branding and employee retention; a sequential mediation is hypothesized, where it is proposed that the relationship between employer branding and employee retention is sequentially mediated by person–organization fit (P-O fit) and organizational identification. Design/methodology/approach The sample belongs to 224 executive-level employees of the Indian power sector organization. The sequential mediation model is tested by using SPSS macro command of Preacher and Hayes. Findings The findings established that the relationship between employer brand and employees’ intention to stay is sequentially mediated by P-O fit and organizational identification. Practical implications The findings emphasize the role of employer brand on constructs such as P-O fit, organizational identification and intention to stay. In addition, the established mechanism emphasizes the role of P-O fit to realize the benefits such as organizational identification and employee retention. Originality/value Internal branding efforts may have a major impact on workforce attitude and behavior including engagement, job performance and retention. “Yet studies of the positive impact of employer brand on employee attitudes and behaviors, or of the factors that shape employer brand, are rare” (Charbonnier-Voirin et al., 2017, p. 2). Along the line of such gap, this study has taken up to test the unexplored sequential mediation mechanism between employer brand and employees’ intention to stay through P-O fit and organizational identification.

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Wang, Haiyan, Kaiming Yao, Jian Luo, and Yi Lin. "An Implicit Preference-Aware Sequential Recommendation Method Based on Knowledge Graph." Wireless Communications and Mobile Computing 2021 (August 14, 2021): 1–12. http://dx.doi.org/10.1155/2021/5206228.

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Sequential recommendation system has received widespread attention due to its good performance in solving data overload. However, most of the sequential recommendation methods assume that user’s preferences only depend on specific items in the current sequence and do not consider user’s implicit interests. In addition, most of the previous works mainly focus on exploiting relationships between items in the sequence and seldom consider quantifying the degree of preferences for items implied by user’s different behaviors. In order to address these above two problems, we propose an implicit preference-aware sequential recommendation method based on knowledge graph (IPAKG). Firstly, this method introduces knowledge graph to exploit user’s implicit preference representations. Secondly, we integrate recurrent neural network and attention mechanism to capture user’s evolving interests and relationships between different items in the sequence. Thirdly, we introduce the concept of behavior intensity and design a behavior activation unit to exploit the degree of preferences for items implied by a user’s different behaviors. Through the activation unit, the user’s preferences on different items are further quantified. Finally, we conduct experiments on an Amazon electronics dataset and Tmall dataset to evaluate the performance of our method. Experimental results demonstrate that our proposed method has better performance than those baseline methods.

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Ruan, Kangrui, and Xuan Di. "Learning Human Driving Behaviors with Sequential Causal Imitation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 2022): 4583–92. http://dx.doi.org/10.1609/aaai.v36i4.20382.

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Learning human driving behaviors is an efficient approach for self-driving vehicles. Traditional Imitation Learning (IL) methods assume that the expert demonstrations follow Markov Decision Processes (MDPs). However, in reality, this assumption does not always hold true. Spurious correlation may exist through the paths of historical variables because of the existence of unobserved confounders. Accounting for the latent causal relationships from unobserved variables to outcomes, this paper proposes Sequential Causal Imitation Learning (SeqCIL) for imitating driver behaviors. We develop a sequential causal template that generalizes the default MDP settings to one with Unobserved Confounders (MDPUC-HD). Then we develop a sufficient graphical criterion to determine when ignoring causality leads to poor performances in MDPUC-HD. Through the framework of Adversarial Imitation Learning, we develop a procedure to imitate the expert policy by blocking π-backdoor paths at each time step. Our methods are evaluated on a synthetic dataset and a real-world highway driving dataset, both demonstrating that the proposed procedure significantly outperforms non-causal imitation learning methods.

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SUN, Guang-Fu, Le WU, Qi LIU, Chen ZHU, and En-Hong CHEN. "Recommendations Based on Collaborative Filtering by Exploiting Sequential Behaviors." Journal of Software 24, no. 11 (January 3, 2014): 2721–33. http://dx.doi.org/10.3724/sp.j.1001.2013.04478.

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Wang, Xinhua, Xuemeng Yu, Lei Guo, Fangai Liu, and Liancheng Xu. "Student Performance Prediction with Short-Term Sequential Campus Behaviors." Information 11, no. 4 (April 9, 2020): 201. http://dx.doi.org/10.3390/info11040201.

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As students’ behaviors are important factors that can reflect their learning styles and living habits on campus, extracting useful features of them plays a helpful role in understanding the students’ learning process, which is an important step towards personalized education. Recently, the task of predicting students’ performance from their campus behaviors has aroused the researchers’ attention. However, existing studies mainly focus on extracting statistical features manually from the pre-stored data, resulting in hysteresis in predicting students’ achievement and finding out their problems. Furthermore, due to the limited representation capability of these manually extracted features, they can only understand the students’ behaviors shallowly. To make the prediction process timely and automatically, we treat the performance prediction task as a short-term sequence prediction problem, and propose a two-stage classification framework, i.e., Sequence-based Performance Classifier (SPC), which consists of a sequence encoder and a classic data mining classifier. More specifically, to deeply discover the sequential features from students’ campus behaviors, we first introduce an attention-based Hybrid Recurrent Neural Network (HRNN) to encode their recent behaviors by giving a higher weight to the ones that are related to the students’ last action. Then, to conduct student performance prediction, we further involve these learned features to the classic Support Vector Machine (SVM) algorithm and finally achieve our SPC model. We conduct extensive experiments in the real-world student card dataset. The experimental results demonstrate the superiority of our proposed method in terms of Accuracy and Recall.

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Romano, Joan M., Judith A. Turner, Larry S. Friedman, Richard A. Bulcroft, Mark P. Jensen, Hyman Hops, and Steven F. Wright. "Sequential analysis of chronic pain behaviors and spouse responses." Journal of Consulting and Clinical Psychology 60, no. 5 (1992): 777–82. http://dx.doi.org/10.1037/0022-006x.60.5.777.

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Zhang, Zhijun, and Hong Liu. "Social recommendation model combining trust propagation and sequential behaviors." Applied Intelligence 43, no. 3 (May 19, 2015): 695–706. http://dx.doi.org/10.1007/s10489-015-0681-y.

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Kim, Youlim, Hyeonkyeong Lee, Mikyung Lee, Hyeyeon Lee, Sookyung Kim, and Kennedy Diema Konlan. "The Sequential Mediating Effects of Dietary Behavior and Perceived Stress on the Relationship between Subjective Socioeconomic Status and Multicultural Adolescent Health." International Journal of Environmental Research and Public Health 18, no. 7 (March 31, 2021): 3604. http://dx.doi.org/10.3390/ijerph18073604.

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Studies have examined the impact of social determinants of health on the health behaviors and health statuses of ethnic minority adolescents. This study examines the subjective health of this population by examining the direct effects of multicultural adolescents’ subjective socioeconomic status (SES) and the sequential mediating effects of their dietary behaviors and perceived stress. We utilized secondary data of 500 middle school students from multicultural families who participated in the 15th Korean Youth Health Behavior Survey, 2019. Information about SES, perceived stress, subjective health status, and dietary behavior (measured by the breakfast intake frequency during the prior week) were utilized. For the relationship between the SES and the subjective health status, we confirmed the sequential mediating effects of breakfast frequency and perceived stress using SPSS 25.0 and PROCESS macro with bootstrapping. The results showed that SES had a direct effect on subjective health status and indirectly influenced subjective health status through the sequential mediating effect of breakfast frequency and perceived stress. However, SES had no direct effects on perceived stress. These findings emphasize that broadening the community-health lens to consider the upstream factor of SES when preparing health promotion interventions is essential to achieving health equity for vulnerable populations.

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Jayasudha, J., and A. Christina Esther. "Mining Sequential Pattern of Data in Textual Document Using Data Mining Classification Technique." Asian Journal of Computer Science and Technology 8, S1 (February 5, 2019): 41–45. http://dx.doi.org/10.51983/ajcst-2019.8.s1.1961.

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Text document were transmitted over the internet for the text communication. So they were occurred many problems like repeated text occurred because of same data were provided in the internet. To characterize and extracting that is a most critical task for the researchers. Many researchers were characterized and applied in many fields like real-life scenarios, such as real-time monitoring on abnormal user behaviors, etc. In this case to detect and characterize the personalized behavior of the user were provide some drawbacks. To solve this problem, this paper analyzing the sequential data and characterize the user behavior with the help of the data mining sequential pattern matching algorithm.

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Wang, Shuli, Xuewen Li, Xiaomeng Kou, Jin Zhang, Shaojie Zheng, Jinlong Wang, and Jibing Gong. "Sequential Recommendation through Graph Neural Networks and Transformer Encoder with Degree Encoding." Algorithms 14, no. 9 (August 31, 2021): 263. http://dx.doi.org/10.3390/a14090263.

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Predicting users’ next behavior through learning users’ preferences according to the users’ historical behaviors is known as sequential recommendation. In this task, learning sequence representation by modeling the pairwise relationship between items in the sequence to capture their long-range dependencies is crucial. In this paper, we propose a novel deep neural network named graph convolutional network transformer recommender (GCNTRec). GCNTRec is capable of learning effective item representation in a user’s historical behaviors sequence, which involves extracting the correlation between the target node and multi-layer neighbor nodes on the graphs constructed under the heterogeneous information networks in an end-to-end fashion through a graph convolutional network (GCN) with degree encoding, while the capturing long-range dependencies of items in a sequence through the transformer encoder model. Using this multi-dimensional vector representation, items related to a user historical behavior sequence can be easily predicted. We empirically evaluated GCNTRec on multiple public datasets. The experimental results show that our approach can effectively predict subsequent relevant items and outperforms previous techniques.

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Zhang, Juyan, Dharma Adhikari, Shahira Fahmy, and Seok Kang. "Exploring the impacts of national image, service quality, and perceived value on international tourist behaviors: A Nepali case." Journal of Vacation Marketing 26, no. 4 (July 20, 2020): 473–88. http://dx.doi.org/10.1177/1356766720942559.

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The dominant paradigm on tourist behaviors depicts a sequential relationship among image, quality, satisfaction, and post-purchase behavior while the alternative view argues that consumer behaviors are better understood through perceived value. Using Nepal as a case, we tested a synthetic model of tourist behaviors and applied the Fombrun-RI Country Reputation Index (CRI) that was developed in the field of nation branding to measure destination image, an elusive concept in tourism literature since long. Structural Equation Modeling (SEM) shows that the data largely supported the model, and national image has impacts on tourist behaviors. However, service quality is not a significant predictor of tourist behaviors. This corroborates the argument that perceived value is a better way to analyze tourist behaviors. The research has practical implications for nation branding programs in the less-developed nations.

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Zhang, Qi, Satinder Singh, and Edmund Durfee. "Minimizing Maximum Regret in Commitment Constrained Sequential Decision Making." Proceedings of the International Conference on Automated Planning and Scheduling 27 (June 5, 2017): 348–56. http://dx.doi.org/10.1609/icaps.v27i1.13836.

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In cooperative multiagent planning, it can often be beneficial for an agent to make commitments about aspects of its behavior to others, allowing them in turn to plan their own behaviors without taking the agent's detailed behavior into account. Extending previous work in the Bayesian setting, we consider instead a worst-case setting in which the agent has a set of possible environments (MDPs) it could be in, and develop a commitment semantics that allows for probabilistic guarantees on the agent's behavior in any of the environments it could end up facing. Crucially, an agent receives observations (of reward and state transitions) that allow it to potentially eliminate possible environments and thus obtain higher utility by adapting its policy to the history of observations. We develop algorithms and provide theory and some preliminary empirical results showing that they ensure an agent meets its commitments with history-dependent policies while minimizing maximum regret over the possible environments.

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Zhang, Hanshu, Frederic Moisan, and Cleotilde Gonzalez. "Rock-Paper-Scissors Play: Beyond the Win-Stay/Lose-Change Strategy." Games 12, no. 3 (June 22, 2021): 52. http://dx.doi.org/10.3390/g12030052.

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This research studied the strategies that players use in sequential adversarial games. We took the Rock-Paper-Scissors (RPS) game as an example and ran players in two experiments. The first experiment involved two humans, who played the RPS together for 100 times. Importantly, our payoff design in the RPS allowed us to differentiate between participants who used a random strategy from those who used a Nash strategy. We found that participants did not play in agreement with the Nash strategy, but rather, their behavior was closer to random. Moreover, the analyses of the participants’ sequential actions indicated heterogeneous cycle-based behaviors: some participants’ actions were independent of their past outcomes, some followed a well-known win-stay/lose-change strategy, and others exhibited the win-change/lose-stay behavior. To understand the sequential patterns of outcome-dependent actions, we designed probabilistic computer algorithms involving specific change actions (i.e., to downgrade or upgrade according to the immediate past outcome): the Win-Downgrade/Lose-Stay (WDLS) or Win-Stay/Lose-Upgrade (WSLU) strategies. Experiment 2 used these strategies against a human player. Our findings show that participants followed a win-stay strategy against the WDLS algorithm and a lose-change strategy against the WSLU algorithm, while they had difficulty in using an upgrade/downgrade direction, suggesting humans’ limited ability to detect and counter the actions of the algorithm. Taken together, our two experiments showed a large diversity of sequential strategies, where the win-stay/lose-change strategy did not describe the majority of human players’ dynamic behaviors in this adversarial situation.

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TSAI, CHIEH-YUAN, CHIH-CHUNG LO, and CHAO-WEN LIN. "A TIME-INTERVAL SEQUENTIAL PATTERN CHANGE DETECTION METHOD." International Journal of Information Technology & Decision Making 10, no. 01 (January 2011): 83–108. http://dx.doi.org/10.1142/s0219622011004233.

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Several studies have focused on mining changes in different time-period databases. Analyzing these change behaviors provides useful information for managers to develop better marketing strategies and decision making. Although some researchers have developed efficient methods for association rule change detection, no attempt has been made to analyze time-interval sequential pattern changes in databases collected over time. Therefore, this research proposes a time-interval sequential pattern change detection framework to derive the change trends in customer behaviors in two periods. First, two time-interval sequential pattern sets are generated from two time-period databases respectively using the proposed DTI-Apriori algorithm. Different from previous mining methods that require users to manually define a set of time-interval ranges in advance, the DTI-Apriori algorithm automatically arranges the time-interval range and then generates time-interval sequential patterns. The degree of change for each pair of time-interval sequential patterns from different time periods is evaluated next. Based on the degree of change, a time-interval sequential pattern is clarified as one of the following three change types: an emerging time-interval sequential pattern, an unexpected time-interval sequential pattern, or an added/perished time-interval sequential pattern. Significant change patterns are returned to users for further analysis if the degree of change is large enough.

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Chen, Jiayi, Wen Wu, Liye Shi, Yu Ji, Wenxin Hu, Xi Chen, Wei Zheng, and Liang He. "DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation." Applied Sciences 12, no. 22 (November 19, 2022): 11765. http://dx.doi.org/10.3390/app122211765.

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Sequential recommendations have made great strides in accurately predicting the future behavior of users. However, seeking accuracy alone may bring side effects such as unfair and overspecialized recommendation results. In this work, we focus on the calibrated recommendations for sequential recommendation, which is connected to both fairness and diversity. On the one hand, it aims to provide fairer recommendations whose preference distributions are consistent with users’ historical behaviors. On the other hand, it can improve the diversity of recommendations to a certain degree. But existing methods for calibration have mainly relied on the post-processing on the candidate lists, which require more computation time in generating recommendations. In addition, they fail to establish the relationship between accuracy and calibration, leading to the limitation of accuracy. To handle these problems, we propose an end-to-end framework to provide both accurate and calibrated recommendations for sequential recommendation. We design an objective function to calibrate the interests between recommendation lists and historical behaviors. We also provide distribution modification approaches to improve the diversity and mitigate the effect of imbalanced interests. In addition, we design a decoupled-aggregated model to improve the recommendation. The framework assigns two objectives to two individual sequence encoders, and aggregates the outputs by extracting useful information. Experiments on benchmark datasets validate the effectiveness of our proposed model.

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Park, Sungjin, and Keuntae Cho. "Agility and Innovativeness: The Serial Mediating Role of Helping Behavior and Knowledge Sharing and Moderating Role of Customer Orientation." Behavioral Sciences 12, no. 8 (August 8, 2022): 274. http://dx.doi.org/10.3390/bs12080274.

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This study aims to understand the mechanism whereby the Agile approach works by analyzing the effect of agility on innovativeness, the sequential mediating effect of helping behavior and knowledge sharing, and the moderating effect of customer orientation. Data for 323 Information and Communication Technology (ICT) companies and 964 non-ICT companies were collected and analyzed through online surveys. Bootstrapping analysis using Model No. 83 of the PROCESS macro confirmed that agility increases team members’ helping behaviors and strengthens knowledge sharing, which in turn has a positive effect on innovativeness. More specifically, helping behavior and knowledge sharing sequentially mediate the relationship between agility and innovativeness. In addition, the study verified that customer orientation moderates the effect of agility on helping behavior. This study is meaningful in showing that it is important to create a culture that pursues “customer value” while promoting mutually helping behavior and sharing knowledge when introducing Agile methodology.

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Di, Weiqiang, Zhihao Wu, and Youfang Lin. "Attenuated and normalized item-item product network for sequential recommendation." PeerJ Computer Science 8 (January 21, 2022): e867. http://dx.doi.org/10.7717/peerj-cs.867.

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Sequential recommendation has become a research trending that exploits user’s recent behaviors for recommendation. The user-item interactions contain a sequential dependency that we need to capture to better recommend. Item-item Product (IIP), which models item co-occurrence, has shown good potential by characterizing the pairwise item relationships. Generally, recent behaviors have a greater impact on the current than long-term historical behaviors. And the decaying rate of influence around infrequent behaviors is fast. However, IIP ignores such a phenomenon when considering item-item relevance and leads to suboptimal performance. In this paper, we propose an attenuated IIP mechanism which is position-aware and decays the influence of historical items at an exponential rate. Besides, In order to make up for scenarios where the influence is not in a monotonous decline trend, we add another normalized IIP mechanism to complement the attenuated IIP mechanism. It also strengthen the model’s ability in discriminating favorite items under the sparse data condition by enlarging the gap of matching degree between items. Experiments conducted on five real-world datasets demonstrate that our proposed model achieves better performance than a set of state-of-the-art sequential recommendation models.

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Li, Zhiyong, Rui Cui, Li Li, Yingli Hu, and Ruwan Ranasinghe. "Inertia Stages and Tourists' Behavior." International Journal of Tourism and Hospitality Management in the Digital Age 2, no. 1 (January 2018): 1–17. http://dx.doi.org/10.4018/ijthmda.2018010101.

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This article integrates the concept of inertia into a tourism context to understand how repeat visitors act and make revisit decisions. Moderating effect of zone of tolerance (ZOT), switching barriers (SWI) and external opportunity (OPP) on the sequential development of inertial behavior was analyzed through 518 tourists. Findings confirm that cognitive inertia (COGI) and affective inertia (AFFI) are significantly and positively related to conative inertia (CONI), while conative inertia also has a direct effect on determining action inertia (ACTI). The tests of moderating effects of zone of tolerance, switching barriers and external opportunity on the sequential development of inertial behavior show these variables have a significant moderating effect on the sequential development of inertia, except for the relation between switching barriers and affective inertia acting together, and conative inertia. These findings provide valuable insights that enable one to understand tourists' revisit behaviors. Theoretical and empirical implications are discussed, for the purpose of advancing tourism marketing discourse.

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Huang, Ching-Hsu, and Hsiao-Yi Tseng. "An Exploratory Study of Consumer Food Waste Attitudes, Social Norms, Behavioral Intentions, and Restaurant Plate Waste Behaviors in Taiwan." Sustainability 12, no. 22 (November 23, 2020): 9784. http://dx.doi.org/10.3390/su12229784.

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The rising trend of people dining out has made food waste in restaurants become a significant issue because of sustainability. Consumers’ attitudes toward food waste in restaurants are still undergoing scrutiny. The main purpose of this study was to test the relationships among consumers’ attitudes, social norms, behavioral intentions, and plate waste behaviors in restaurants in Taiwan. This paper contributes to the understanding of consumers’ food waste behaviors by examining a hypothesized research model. Based on a questionnaire with 464 restaurant customers, the hypothesized research model was examined by using structural equation modeling. Sequential mediation for examining the relationships of variables was conducted, and it was found that there was a significant serial mediation effect on the path of consumers’ attitudes, behavioral intentions, food waste behavior, and consumer plate waste. The results of this study can contribute to better engaging consumers in mitigating food waste in restaurants. Implications and suggestions for further research and recommendations for restaurant managers are provided based on sustainable management.

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Gebremeskel, Gebeyehu Belay, Chai Yi, Chengliang Wang, and Zhongshi He. "Critical analysis of smart environment sensor data behavior pattern based on sequential data mining techniques." Industrial Management & Data Systems 115, no. 6 (July 13, 2015): 1151–78. http://dx.doi.org/10.1108/imds-12-2014-0386.

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Purpose – Behavioral pattern mining for intelligent system such as SmEs sensor data are vitally important in many applications and performance optimizations. Sensor pattern mining (SPM) is also dynamic and a hot research issue to pervasive and ubiquitous of smart technologies toward improving human life. However, in large-scale sensor data, exploring and mining pattern, which leads to detect the abnormal behavior is challenging. The paper aims to discuss these issues. Design/methodology/approach – Sensor data are complex and multivariate, for example, which data captured by the sensors, how it is precise, what properties are recorded or measured, are important research issues. Therefore, the method, the authors proposed Sequential Data Mining (SDM) approach to explore pattern behaviors toward detecting abnormal patterns for smart space fault diagnosis and performance optimization in the intelligent world. Sensor data types, modeling, descriptions and SPM techniques are discussed in depth using real sensor data sets. Findings – The outcome of the paper is measured as introducing a novel idea how SDM technique’s scale-up to sensor data pattern mining. In the paper, the approach and technicality of the sensor data pattern analyzed, and finally the pattern behaviors detected or segmented as normal and abnormal patterns. Originality/value – The paper is focussed on sensor data behavioral patterns for fault diagnosis and performance optimizations. It is other ways of knowledge extraction from the anomaly of sensor data (observation records), which is pertinent to adopt in many intelligent systems applications, including safety and security, efficiency, and other advantages as the consideration of the real-world problems.

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TORIGOE, TAKASHI. "A Sequential Analysis of Early Development of Behaviors in Chicks." Annual of Animal Psychology 35, no. 1 (1985): 23–30. http://dx.doi.org/10.2502/janip1944.35.23.

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Thomas, Hoben, and Michael P. Dahlin. "Inferring children's categorizations from sequential touching behaviors: An analytical model." Psychological Review 107, no. 1 (2000): 182–94. http://dx.doi.org/10.1037/0033-295x.107.1.182.

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Walker, Edward D., and William E. Archer. "Sequential organization of grooming behaviors of the mosquito,Aedes triseriatus." Journal of Insect Behavior 1, no. 1 (January 1988): 97–109. http://dx.doi.org/10.1007/bf01052506.

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Bodle, J. H., L.-Y. Zhou, C. Shore, and W. E. Dixon. "The emergence of sequential play behaviors in a dyadic context." Infant Behavior and Development 19 (April 1996): 233. http://dx.doi.org/10.1016/s0163-6383(96)90287-8.

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Steele, Catriona M., and Pascal H. H. M. Van Lieshout. "Influence of Bolus Consistency on Lingual Behaviors in Sequential Swallowing." Dysphagia 19, no. 3 (August 2004): 192–206. http://dx.doi.org/10.1007/s00455-004-0006-5.

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Woods, Diana, and Kathleen Buckwalter. "Taking Another Look: Thoughts on Behavioral Symptoms in Dementia and Their Measurement." Healthcare 6, no. 4 (October 22, 2018): 126. http://dx.doi.org/10.3390/healthcare6040126.

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This article proposes taking another look at behavioral symptoms of dementia (BSDs) both from a theoretical perspective that informs research and practice and from a measurement perspective. We discuss why this rethinking of behaviors impacts current models of care and our ability to better detect outcomes from interventions. We propose that BSDs be viewed from a pattern perspective and provide some suggestions for how to identify and measure these patterns that can influence the timing and type of intervention. Evidence suggests that BSDs are complex, sequential, patterned clusters of behavior recurring repeatedly in the same individual and escalate significantly without timely intervention. However, BSDs are frequently viewed as separate behaviors rather than patterns or clusters of behaviors, a view that affects current research questions as well as the choice, timing, and outcomes of interventions. These symptoms cause immense distress to persons with the disease and their caregivers, trigger hospitalizations and nursing home placement, and are associated with increased care costs. Despite their universality and that symptoms manifest across disease etiologies and stages, behaviors tend to be underrecognized, undertreated, and overmanaged by pharmacological treatments that may pose more harm than benefit.

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Tan, Qiaoyu, Jianwei Zhang, Ninghao Liu, Xiao Huang, Hongxia Yang, Jingren Zhou, and Xia Hu. "Dynamic Memory based Attention Network for Sequential Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (May 18, 2021): 4384–92. http://dx.doi.org/10.1609/aaai.v35i5.16564.

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Sequential recommendation has become increasingly essential in various online services. It aims to model the dynamic preferences of users from their historical interactions and predict their next items. The accumulated user behavior records on real systems could be very long. This rich data brings opportunities to track actual interests of users. Prior efforts mainly focus on making recommendations based on relatively recent behaviors. However, the overall sequential data may not be effectively utilized, as early interactions might affect users' current choices. Also, it has become intolerable to scan the entire behavior sequence when performing inference for each user, since real-world system requires short response time. To bridge the gap, we propose a novel long sequential recommendation model, called Dynamic Memory-based Attention Network (DMAN). It segments the overall long behavior sequence into a series of sub-sequences, then trains the model and maintains a set of memory blocks to preserve long-term interests of users. To improve memory fidelity, DMAN dynamically abstracts each user's long-term interest into its own memory blocks by minimizing an auxiliary reconstruction loss. Based on the dynamic memory, the user's short-term and long-term interests can be explicitly extracted and combined for efficient joint recommendation. Empirical results over four benchmark datasets demonstrate the superiority of our model in capturing long-term dependency over various state-of-the-art sequential models.

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Journal articles on the topic 'Sequential behaviors'

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