Journal articles on the topic 'Users’ preferences'
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Luo, Mingshi, Xiaoli Zhang, Jiao Li, Peipei Duan, and Shengnan Lu. "User Dynamic Preference Construction Method Based on Behavior Sequence." Scientific Programming 2022 (July 22, 2022): 1–15. http://dx.doi.org/10.1155/2022/6101045.
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People’s needs are constantly changing, and the performance of traditional recommendation algorithms is no longer enough to meet the demand. Considering that users’ preferences change with time, the users’ behavior sequence hides the evolution and change law of users’ preferences, so mining the dependence of the users’ behavior sequence is extremely important to predict users’ dynamic preferences. From the perspective of constructing users’ dynamic preferences, this paper proposes a users’ dynamic preference model based on users’ behavior sequences. Firstly, the user’s interest model is divided into short-term and long-term interest models. The short-term interest reflects the user’s current preference, and the long-term interest refers to the user’s interest from all his historical behaviors, representing the user’s consistent and stable preference. Users’ dynamic preference is obtained by integrating short-term interest and long-term interest, which solves the problem that the user’s preference cannot reflect the change in the user’s interest in real-time. We use the public Amazon review dataset to test the model we propose in the paper. Our model achieves the best performance, with a maximum performance improvement of 15.21% compared with the basic model (BPR, NCF) and 2.04% compared with the sequence model (GRU4REC, Caser, etc.), which proves that the user’s dynamic preference model can effectively predict the user’s dynamic preference. Users’ dynamic preferences are helpful in predicting users’ real-time preferences, especially in the field of recommendation.
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Shi, Yancui, Jianhua Cao, Congcong Xiong, and Xiankun Zhang. "A Prediction Method of Mobile User Preference Based on the Influence between Users." International Journal of Digital Multimedia Broadcasting 2018 (July 19, 2018): 1–12. http://dx.doi.org/10.1155/2018/8081409.
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User preference will be impacted by other users. To accurately predict mobile user preference, the influence between users is introduced into the prediction model of user preference. First, the mobile social network is constructed according to the interaction behavior of the mobile user, and the influence of the user is calculated according to the topology of the constructed mobile social network and mobile user behavior. Second, the influence between users is calculated according to the user’s influence, the interaction behavior between users, and the similarity of user preferences. When calculating the influence based on the interaction behavior, the context information is considered; the context information and the order of user preferences are considered when calculating the influence based on the similarity of user preferences. The improved collaborative filtering method is then employed to predict mobile user preferences based on the obtained influence between users. Finally, the experiment is executed on the real data set and the integrated data set, and the results show that the proposed method can obtain more accurate mobile user preferences than those of existing methods.
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Park, Han-Saem, Moon-Hee Park, and Sung-Bae Cho. "Mobile Information Recommendation Using Multi-Criteria Decision Making with Bayesian Network." International Journal of Information Technology & Decision Making 14, no. 02 (March 2015): 317–38. http://dx.doi.org/10.1142/s0219622015500017.
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The advancement of network technology and the popularization of the Internet lead to increased interest in information recommendation. This paper proposes a group recommendation system that takes the preferences of group users in mobile environment and applies the system to recommendation of restaurants. The proposed system recommends the restaurants by considering various preferences of multiple users. To cope with the uncertainty in mobile environment, we exploit Bayesian network, which provides reliable performance and models individual user's preference. Also, Analytical Hierarchy Process of multi-criteria decision-making method is used to estimate the group users' preference from individual users' preferences. Experiments in 10 different situations provide a comparison of the proposed method with random recommendation, simple rule-based recommendation and neural network recommendation, and confirm that the proposed method is useful with the subjective test.
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Nadi, S., and A. H. Houshyaripour. "A NEW MODEL FOR FUZZY PERSONALIZED ROUTE PLANNING USING FUZZY LINGUISTIC PREFERENCE RELATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 27, 2017): 417–21. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-417-2017.
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This paper proposes a new model for personalized route planning under uncertain condition. Personalized routing, involves different sources of uncertainty. These uncertainties can be raised from user’s ambiguity about their preferences, imprecise criteria values and modelling process. The proposed model uses Fuzzy Linguistic Preference Relation Analytical Hierarchical Process (FLPRAHP) to analyse user’s preferences under uncertainty. Routing is a multi-criteria task especially in transportation networks, where the users wish to optimize their routes based on different criteria. However, due to the lake of knowledge about the preferences of different users and uncertainties available in the criteria values, we propose a new personalized fuzzy routing method based on the fuzzy ranking using center of gravity. The model employed FLPRAHP method to aggregate uncertain criteria values regarding uncertain user’s preferences while improve consistency with least possible comparisons. An illustrative example presents the effectiveness and capability of the proposed model to calculate best personalize route under fuzziness and uncertainty.
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Zha, Yongfu, Yongjian Zhang, Zhixin Liu, and Yumin Dong. "Self-Attention Based Time-Rating-Aware Context Recommender System." Computational Intelligence and Neuroscience 2022 (September 17, 2022): 1–10. http://dx.doi.org/10.1155/2022/9288902.
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The sequential recommendation can predict the user’s next behavior according to the user’s historical interaction sequence. To better capture users’ preferences, some sequential recommendation models propose time-aware attention networks to capture users’ long-term and short-term intentions. However, although these models have achieved good results, they ignore the influence of users on the rating information of items. We believe that in the sequential recommendation, the user’s displayed feedback (rating) on an item reflects the user’s preference for the item, which directly affects the user’s choice of the next item to a certain extent. In different periods of sequential recommendation, the user’s rating of the item reflects the change in the user’s preference. In this paper, we separately model the time interval of items in the user’s interaction sequence and the ratings of the items in the interaction sequence to obtain temporal context and rating context, respectively. Finally, we exploit the self-attention mechanism to capture the impact of temporal context and rating context on users’ preferences to predict items that users would click next. Experiments on three public benchmark datasets show that our proposed model (SATRAC) outperforms several state-of-the-art methods. The Hit@10 value of the SATRAC model on the three datasets (Movies-1M, Amazon-Movies, Amazon-CDs) increased by 0.73%, 2.73%, and 1.36%, and the NDCG@10 value increased by 5.90%, 3.47%, and 4.59%, respectively.
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Chen, Pengzhan, Jihua Wu, and Ning Li. "A Personalized Navigation Route Recommendation Strategy Based on Differential Perceptron Tracking User’s Driving Preference." Computational Intelligence and Neuroscience 2023 (January 4, 2023): 1–14. http://dx.doi.org/10.1155/2023/8978398.
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With the increasing frequency of autonomous driving, more and more attention is paid to personalized path planning. However, the path selection preferences of users will change with internal or external factors. Therefore, this paper proposes a personalized path recommendation strategy that can track and study user’s path preference. First, we collect the data of the system, establish the relationship with the user preference factor, and get the user’s initial preference weight vector by dichotomizing the K-means algorithm. The system then determines whether user preferences change based on a set threshold, and when the user’s preference changes, the current preference weight vector can be obtained by redefining the preference factor or calling difference perception. Finally, the road network is quantized separately according to the user preference weight vector, and the optimal path is obtained by using Tabu search algorithm. The simulation results of two scenarios show that the proposed strategy can meet the requirements of autopilot even when user preferences change.
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Žnidaršič, Martin, Aljaž Osojnik, Peter Rupnik, and Bernard Ženko. "Improving Effectiveness of a Coaching System through Preference Learning." Technologies 10, no. 1 (January 31, 2022): 24. http://dx.doi.org/10.3390/technologies10010024.
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The paper describes an approach for indirect data-based assessment and use of user preferences in an unobtrusive sensor-based coaching system with the aim of improving coaching effectiveness. The preference assessments are used to adapt the reasoning components of the coaching system in a way to better align with the preferences of its users. User preferences are learned based on data that describe user feedback as reported for different coaching messages that were received by the users. The preferences are not learned directly, but are assessed through a proxy—classifications or probabilities of positive feedback as assigned by a predictive machine learned model of user feedback. The motivation and aim of such an indirect approach is to allow for preference estimation without burdening the users with interactive preference elicitation processes. A brief description of the coaching setting is provided in the paper, before the approach for preference assessment is described and illustrated on a real-world example obtained during the testing of the coaching system with elderly users.
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Thijssen, Kirsten, Marion Vlemminx, Michelle Westerhuis, Jeanne Dieleman, M. Beatrijs Van der Hout-Van der Jagt, and S. Guid Oei. "Uterine Monitoring Techniques from Patients' and Users' Perspectives." American Journal of Perinatology Reports 08, no. 03 (July 2018): e184-e191. http://dx.doi.org/10.1055/s-0038-1669409.
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Objective To evaluate preferences from patients and users on 3 uterine monitoring techniques, during labor. Study Design Women in term labor were simultaneously monitored with the intrauterine pressure catheter, the external tocodynamometer, and the electrohysterograph. Postpartum, these women filled out a questionnaire evaluating their preferences and important aspects. Nurses completed a questionnaire evaluating users' preferences. Results Of all 52 participating women, 80.8% preferred the electrohysterograph, 17.3% the intrauterine pressure catheter and 1.9% the external tocodynamometer. For these women, the electrohysterograph scored best regarding application and presence during labor (p < 0.001). Most important aspects were “least likely to harm” and “least discomfort”. Of 57 nurses, 40.4% preferred the electrohysterograph, 35.1% the external tocodynamometer, and 24.6% had no preference, or replied that their preference is subject to situation and patient. Conclusion Patients prefer the electrohysterograph over the external tocodynamometer and the intrauterine pressure catheter, while healthcare providers report ambiguous results.
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Bok, Kyoungsoo, Jinwoo Song, Jongtae Lim, and Jaesoo Yoo. "Personalized Search Using User Preferences on Social Media." Electronics 11, no. 19 (September 24, 2022): 3049. http://dx.doi.org/10.3390/electronics11193049.
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In contrast to traditional web search, personalized search provides search results that take into account the user’s preferences. However, the existing personalized search methods have limitations in providing appropriate search results for the individual’s preferences, because they do not consider the user’s recent preferences or the preferences of other users. In this paper, we propose a new search method considering the user’s recent preferences and similar users’ preferences on social media analysis. Since the user expresses personal opinions on social media, it is possible to grasp the user preferences when analyzing the records of social media activities. The proposed method collects user social activity records and determines keywords of interest using TF-IDF. Since user preferences change continuously over time, we assign time weights to keywords of interest, giving many high values to state-of-the-art user preferences. We identify users with similar preferences to extend the search results to be provided to users because considering only user preferences in personalized searches can provide narrow search results. The proposed method provides personalized search results considering social characteristics by applying a ranking algorithm that considers similar user preferences as well as user preferences. It is shown through various performance evaluations that the proposed personalized search method outperforms the existing methods.
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Wu, Li, and Ma. "A Comparative Study of Spatial and Temporal Preferences for Waterfronts in Wuhan based on Gender Differences in Check-In Behavior." ISPRS International Journal of Geo-Information 8, no. 9 (September 14, 2019): 413. http://dx.doi.org/10.3390/ijgi8090413.
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The geographical location and check-in frequency of social platform users indicate their personal preferences and intentions for space. On the basis of social media data and gender differences, this study analyzes Weibo users’ preferences and the reasons behind these preferences for the waterfronts of the 21 major lakes within Wuhan’s Third Ring Road, in accordance with users’ check-in behaviors. According to the distribution characteristics of the waterfronts’ points of interest, this study explores the preferences of male and female users for waterfronts and reveals, through the check-in behaviors of Weibo users, the gender differences in the preference and willingness of these users to choose urban waterfronts. Results show that men and women check in significantly more frequently on weekends than on weekdays. Women are more likely than men to check in at waterfronts. Significant differences in time and space exist between male and female users’ preferences for different lakes.
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Stroud, Laura, Erika Werner, Kristen Matteson, Michael Carey, Gideon St Helen, Thomas Eissenberg, and Lori A. J. Scott-Sheldon. "Waterpipe (hookah) tobacco use in pregnancy: use, preferences and perceptions of flavours." Tobacco Control 29, Suppl 2 (July 18, 2019): s62—s71. http://dx.doi.org/10.1136/tobaccocontrol-2019-054984.
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ObjectiveWaterpipe tobacco (WPT; hookah) use is common in pregnant and reproductive-age women. Sweet flavours contribute to the appeal of WPT and are a potential regulatory target. This study investigated use, preferences and perceptions of WPT flavours in pregnant WPT users, and the impact of flavour preferences on preconception/prenatal WPT use and exposure biomarkers.Methods58 pregnant WPT users (mean age=27 years) completed a detailed interview regarding their WPT flavours use, preferences and perceptions. Biomarkers of nicotine and carcinogen exposure (eg, cotinine, benzene, butadiene) were also collected.Results55% of participants were dual/poly WPT users (ie, reported use of one or more other tobacco products in addition to WPT). Pregnant WPT users reported nearly exclusive use of flavoured WPT, with greater use of menthol/mint (68%) followed by fruit flavours (48%) (p<0.001), and greater preferences for fruit followed by menthol/mint flavours (ps<0.05). Harm perceptions did not differ among flavours. Compared with dual/poly WPT users, WPT-only users reported more total WPT use events, greater use of and preference for menthol/mint flavoured WPT (ps<0.001), and decreased exposure biomarkers (ps≤0.040). Preference for menthol/mint and fruit flavours predicted more flavoured WPT use events during preconception and pregnancy; preference for menthol/mint predicted detectable cotinine and benzene levels but not butadiene.ConclusionsThis is the first study of WPT flavour use, preferences and perceptions in pregnant women. Use of and preference for menthol/mint and fruit WPT flavours in this vulnerable population could be considered in regulating WPT flavours to protect the health of women and children.
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Wu, Ping, Tao Yu, J. B. Du, G. Q. Qu, and Feng Xiong. "Research on Modeling User’s Preference in the Steel E-Trading Platform." Applied Mechanics and Materials 743 (March 2015): 687–91. http://dx.doi.org/10.4028/www.scientific.net/amm.743.687.
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In order to meet the increasing personalized needs of users in the steel trading platform, the intelligent recommendation system has been introduced into the platform. And the users’ interests and preferences-based modeling is the key and foundation of recommendation system, and changes with the change of time. So, in this paper, the user preferences are divided into long-term and short-term firstly, then the users’ basic information vectors and cluster method are used to model users’ long-term interests and preferences, while mining and analyzing users’ operating records in the platform to model users’ the short-term. Finally, the whole interest and preference’s model of user will be built by integrating the two models.
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Tian, Zhiqiang, Yezheng Liu, Jianshan Sun, Yuanchun Jiang, and Mingyue Zhu. "Exploiting Group Information for Personalized Recommendation with Graph Neural Networks." ACM Transactions on Information Systems 40, no. 2 (April 30, 2022): 1–23. http://dx.doi.org/10.1145/3464764.
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Personalized recommendation has become more and more important for users to quickly find relevant items. The key issue of the recommender system is how to model user preferences. Previous work mostly employed user historical data to learn users’ preferences, but faced with the data sparsity problem. The prevalence of online social networks promotes increasing online discussion groups, and users in the same group often have similar interests and preferences. Therefore, it is necessary to integrate group information for personalized recommendation. The existing work on group-information-enhanced recommender systems mainly relies on the item information related to the group, which is not expressive enough to capture the complicated preference dependency relationships between group users and the target user. In this article, we solve the problem with the graph neural networks. Specifically, the relationship between users and items, the item preferences of groups, and the groups that users participate in are constructed as bipartite graphs, respectively, and the user preferences for items are learned end to end through the graph neural network. The experimental results on the Last.fm and Douban Movie datasets show that considering group preferences can improve the recommendation performance and demonstrate the superiority on sparse users compared
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Braziunas, Darius, and Craig Boutilier. "Elicitation of Factored Utilities." AI Magazine 29, no. 4 (December 28, 2008): 79. http://dx.doi.org/10.1609/aimag.v29i4.2203.
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The effective tailoring of decisions to the needs and desires of specific users requires automated mechanisms for preference assessment. We provide a brief overview of recent direct preference elicitation methods: these methods ask users to answer (ideally, a small number of) queries regarding their preferences and use this information to recommend a feasible decision that would be (approximately) optimal given those preferences. We argue for the importance of assessing numerical utilities rather than qualitative preferences, and survey several utility elicitation techniques from artificial intelligence, operations research, and conjoint analysis.
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Kang, Seongju, and Kwangsue Chung. "Preference-Tree-Based Real-Time Recommendation System." Entropy 24, no. 4 (April 2, 2022): 503. http://dx.doi.org/10.3390/e24040503.
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In the current era of online information overload, recommendation systems are very useful for helping users locate content that may be of interest to them. A personalized recommendation system presents content based on information such as a user’s browsing history and the videos watched. However, information filtering-based recommendation systems are vulnerable to data sparsity and cold-start problems. Additionally, existing recommendation systems suffer from the large overhead incurred in learning regression models used for preference prediction or in selecting groups of similar users. In this study, we propose a preference-tree-based real-time recommendation system that uses various tree models to predict user preferences with a fast runtime. The proposed system predicts preferences based on two balance constants and one similarity threshold to recommend content with a high accuracy while balancing generalized and personalized preferences. The results of comparative experiments and ablation studies confirm that the proposed system can accurately recommend content to users. Specifically, we confirmed that the accuracy and novelty of the recommended content were, respectively, improved by 12.1% and 27.2% compared to existing systems. Furthermore, we verified that the proposed system satisfies real-time requirements and mitigates both cold-start and overfitting problems.
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Zhang, Bingjie, Junchao Yu, Zhe Kang, Tianyu Wei, Xiaoyu Liu, and Suhua Wang. "An adaptive preference retention collaborative filtering algorithm based on graph convolutional method." Electronic Research Archive 31, no. 2 (2022): 793–811. http://dx.doi.org/10.3934/era.2023040.
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<abstract> <p>Collaborative filtering is one of the most widely used methods in recommender systems. In recent years, Graph Neural Networks (GNN) were naturally applied to collaborative filtering methods to model users' preference representation. However, empirical research has ignored the effects of different items on user representation, which prevented them from capturing fine-grained users' preferences. Besides, due to the problem of data sparsity in collaborative filtering, most GNN-based models conduct a large number of graph convolution operations in the user-item graph, resulting in an over-smoothing effect. To tackle these problems, Adaptive Preference Retention Graph Convolutional Collaborative Filtering Method (APR-GCCF) was proposed to distinguish the difference among the items and capture the fine-grained users' preferences. Specifically, the graph convolutional method was applied to model the high-order relationship on the user-item graph and an adaptive preference retention mechanism was used to capture the difference between items adaptively. To obtain a unified users' preferences representation and alleviate the over-smoothing effect, we employed a residual preference prediction mechanism to concatenate the representation of users' preferences generated by each layer of the graph neural network. Extensive experiments were conducted based on three real datasets and the experimental results demonstrate the effectiveness of the model.</p> </abstract>
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Zhou, Qingqing, and Chengzhi Zhang. "Detecting Users' Dietary Preferences and Their Evolutions via Chinese Social Media." Journal of Database Management 29, no. 3 (July 2018): 89–110. http://dx.doi.org/10.4018/jdm.2018070105.
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Dietary preferences are linked to human life and region culture. With the rapid development of the Internet, people are becoming frequently interested in sharing their opinions about dietary in social media. This article aims to mine social media users' dietary preferences and their evolutions with user generated content. The authors use microblogs from weibo.com to detect dietary preferences and their evolutions of social media users in China via sentiment analysis. First, the authors compare four aspect extraction methods to obtain dietary aspects. Second, sentiment polarities of aspects and dishes are identified. Finally, dietary preference evolutions are analyzed. Empirical analysis shows that social media users in weibo are not satisfied with status quo of a Chinese diet. Meanwhile, gender and region have significant effects on dietary preferences, and users' dietary preferences change over time. In addition, experimental results show that contextual information is useful in extracting dietary aspects.
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Wang, Hui. "The Influence of Brand Visual Communication on Consumer Psychology Based on Deep Learning." Mathematical Problems in Engineering 2022 (September 29, 2022): 1–8. http://dx.doi.org/10.1155/2022/9599943.
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In order to solve the problem that there are few methods of users’ consumption psychology, the author proposes a research on the influence of brand visual communication on consumer psychology based on deep learning. First, establish the mapping relationship between experience level-product features-aspect words and then use aspect word extraction technology, mining users’ attention to different experience levels from user comments and dividing users into three types: instinctive preference, behavioral preference, and reflective preference; finally, the deep learning-based aspect sentiment analysis technology is used to calculate the user’s preference for the product and further analyze the characteristics of different types of users. Experimental results show that based on the application analysis of more than 900,000 JD.com mobile phone review data, three types of consumer preference user groups were obtained, of which instinctive preference users accounted for 41.6%; it is higher than behavioral preference users (33.01%) and reflection preference users (25.39%), and the consumption characteristics of the three types of users are analyzed from the aspects of mobile phone brand and price. It is proved that the author’s user portrait method can better express the consumption preferences of different types of users.
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Alshehri, Aziz, and Fayez Alotaibi. "Profiling Mobile Users Privacy Preferences." International Journal for Digital Society 10, no. 1 (March 30, 2019): 1436–41. http://dx.doi.org/10.20533/ijds.2040.2570.2019.0178.
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Li, Nan, William Cushing, Subbarao Kambhampati, and Sungwook Yoon. "Learning User Plan Preferences Obfuscated by Feasibility Constraints." Proceedings of the International Conference on Automated Planning and Scheduling 19 (October 16, 2009): 370–73. http://dx.doi.org/10.1609/icaps.v19i1.13393.
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It has long been recognized that users can have complex preferences on plans. Non-intrusive learning of such preferences by observing the plans executed by the user is an attractive idea. Unfortunately, the executed plans are often not a true representation of user preferences, as they result from the interaction between user preferences and feasibility constraints. In the travel planning scenario, a user whose true preference is to travel by a plane may well be frequently observed traveling by car because of feasibility constraints (perhaps the user is a poor graduate student). In this work, we describe a novel method for learning true user preferences obfuscated by such feasibility constraints. Our base learner induces probabilistic hierarchical task networks (pHTNs) from sets of training plans. Our approach is to rescale the input so that it represents the user's preference distribution on plans rather than the observed distribution on plans.
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Olteanu, Alexandra-Mihaela, Mathias Humbert, Kévin Huguenin, and Jean-Pierre Hubaux. "The (Co-)Location Sharing Game." Proceedings on Privacy Enhancing Technologies 2019, no. 2 (April 1, 2019): 5–25. http://dx.doi.org/10.2478/popets-2019-0017.
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Abstract Most popular location-based social networks, such as Facebook and Foursquare, let their (mobile) users post location and co-location (involving other users) information. Such posts bring social benefits to the users who post them but also to their friends who view them. Yet, they also represent a severe threat to the users’ privacy, as co-location information introduces interdependences between users. We propose the first game-theoretic framework for analyzing the strategic behaviors, in terms of information sharing, of users of OSNs. To design parametric utility functions that are representative of the users’ actual preferences, we also conduct a survey of 250 Facebook users and use conjoint analysis to quantify the users’ benefits o f sharing vs. viewing (co)-location information and their preference for privacy vs. benefits. Our survey findings expose the fact that, among the users, there is a large variation, in terms of these preferences. We extensively evaluate our framework through data-driven numerical simulations. We study how users’ individual preferences influence each other’s decisions, we identify several factors that significantly affect these decisions (among which, the mobility data of the users), and we determine situations where dangerous patterns can emerge (e.g., a vicious circle of sharing, or an incentive to over-share) – even when the users share similar preferences.
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Viappiani, P., B. Faltings, and P. Pu. "Preference-based Search using Example-Critiquing with Suggestions." Journal of Artificial Intelligence Research 27 (December 15, 2006): 465–503. http://dx.doi.org/10.1613/jair.2075.
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We consider interactive tools that help users search for their most preferred item in a large collection of options. In particular, we examine example-critiquing, a technique for enabling users to incrementally construct preference models by critiquing example options that are presented to them. We present novel techniques for improving the example-critiquing technology by adding suggestions to its displayed options. Such suggestions are calculated based on an analysis of users' current preference model and their potential hidden preferences. We evaluate the performance of our model-based suggestion techniques with both synthetic and real users. Results show that such suggestions are highly attractive to users and can stimulate them to express more preferences to improve the chance of identifying their most preferred item by up to 78%.
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MATSUO, TOKURO, and TAKAYUKI FUJIMOTO. "A NEW LECTURE ALLOCATION SUPPORT SYSTEM BASED ON USERS' MULTIPLE PREFERENCES IN CAMPUS INFORMATION SYSTEMS." International Journal of Computational Intelligence and Applications 06, no. 02 (June 2006): 245–56. http://dx.doi.org/10.1142/s1469026806001964.
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This paper proposes an effective lecture allocation method based on users' profiles and utilities in elective subjects. In many universities and colleges, elective subject systems are employed as a curriculum in which students make their own learning experiences. Students select multiple subjects based on their interests and preferences. Generally, each university determines members of elective subjects based on simple rules, such as the order of grade, random selection, and first arrival. Current determination systems have strong limitations in terms of reflecting users' multiple preferences. We propose a new determination algorithm and a user support system based on users' profiles and multi-attribute preferences. When users' preferences are not reflected in algorithm-based determination, a coordination agent decides lecture allocation cooperatively with each user's agent. Main advantage of our system is mainly that lectures are allocated effectively that reflected users' multiple preferences.
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Shi, Xiao Wei, Lin Ping Huang, Wei Jian Mi, Dao Fang Chang, and Yan Zhang. "A Personalized Music Recommender Based on Potential Preference Learning Dynamically." Advanced Engineering Forum 1 (September 2011): 395–99. http://dx.doi.org/10.4028/www.scientific.net/aef.1.395.
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An intelligent musical recommendation system for multi-users in network context is presented. The system is based on a comprehensive user profile described by feature-weight-like_degree-scene vectors. According different scenes, the system can filter the music that user may like in the internet, and form a music recommendation list which will be sent to the user. The Preference Learning Agent updates the users’ profile dynamically based on explicit feedback or the hidden preference obtained from the users’ behavior. The learning rate of like_degree, original like_degree and the weight of feature type are important for the improvement of the feature’s learning efficiency. The recommendation system can capture the users’ potential interest and the evolvement of preferences. Experiment results show that the algorithm can learn users’ preferences effectively.
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Liu, Chunyang, Chao Liu, Haiqiang Xin, Jian Wang, Jiping Liu, and Shenghua Xu. "Joint Geosequential Preference and Distance Metric Factorization for Point-of-Interest Recommendation." Mathematical Problems in Engineering 2020 (October 30, 2020): 1–14. http://dx.doi.org/10.1155/2020/6582676.
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Point-of-interest (POI) recommendation is a valuable service to help users discover attractive locations in location-based social networks (LBSNs). It focuses on capturing users’ movement patterns and location preferences by using massive historical check-in data. In the past decade, matrix factorization has become a mature and widely used technology in POI recommendation. However, the inner product of latent vectors adopted in matrix factorization methods does not satisfy the triangle inequality property, which may limit the expressiveness and lead to suboptimal solutions. Besides, the extreme sparsity of check-in data makes it challenging to capture users’ movement preferences accurately. In this paper, we propose a joint geosequential preference and distance metric factorization framework, called GeoSeDMF, for POI recommendation. First, we introduce a distance metric factorization method that is capable of learning users’ personalized preferences from a position and distance perspective in the metric space. Specifically, we convert the user-POI interaction matrix into a distance matrix and factorize it into user and POI dense embeddings. Additionally, we measure users’ personalized preference for the POI by using the Euclidean distance metric instead of the inner product. Then, we model the users’ geospatial preference by applying a geographic weight coefficient and model the users’ sequential preference by using the Euclidean distance of continuous check-in locations. Moreover, a pointwise loss strategy and AdaGrad algorithm are adopted to optimize the positions and relationships of users and POIs in a metric space. Finally, experimental results on three large-scale real-world datasets demonstrate the effectiveness and superiority of the proposed method.
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Liu, Duen-Ren, Chuen-He Liou, Chi-Chieh Peng, and Huai-Chun Chi. "Hybrid content filtering and reputation-based popularity for recommending blog articles." Online Information Review 38, no. 6 (September 9, 2014): 788–805. http://dx.doi.org/10.1108/oir-12-2013-0273.
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Purpose – Social bookmarking is a system which allows users to share, organise, search and manage bookmarks of web resources. However, with the rapid growth in the production of online documents, people are facing the problem of information overload. Social bookmarking web sites offer a solution to this by providing push counts, which are counts of users’ recommendations of articles, and thus indicate the popularity and interest thereof. In this way, users can use the push counts to find popular and interesting articles. A measure of popularity-based solely on push counts, however, cannot be considered a true reflection of popularity. The paper aims to discuss these issues. Design/methodology/approach – In this paper, the authors propose to derive the degree of popularity of an article by considering the reputation of the users who push the article. Moreover, the authors propose a novel personalised blog article recommendation approach which combines reputation-based group popularity with content-based filtering (CBF), for the recommendation of popular blog articles which satisfy users’ personal preferences. Findings – The experimental results show that the proposed approach outperforms conventional CBF, item-based and user-based collaborative filtering approaches. The proposed approach considering reputation-based group popularity scores on neighbouring articles indeed can improve the recommendation quality of traditional CBF method. Originality/value – The recommendation approach modifies CBF method by considering the target user's group preferences, to overcome the limitation of CBF which arises from the recommending only items similar to those the user has previously liked. Users with similar article preferences (profiles) may form a group of users with similar interests. A group's preferences may also reflect an individual's preferences. The reputation-based group preferences of the target user's group can be used to complement the target user's preferences.
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Wei, Feng, Shuyu Chen, Jie Jin, Shuai Zhang, Hongwei Zhou, and Yingbo Wu. "Adaptive Alleviation for Popularity Bias in Recommender Systems with Knowledge Graph." Security and Communication Networks 2022 (April 7, 2022): 1–9. http://dx.doi.org/10.1155/2022/4264489.
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Recommender systems are known to suffer from the popularity bias problem: popular items are recommended frequently, and nonpopular ones rarely, if at all. Prior studies focused on tackling this issue by increasing the number of recommended nonpopular (long-tail) items. However, these methods ignore the users’ personal popularity preferences and increase the exposure rate of the nonpopular items indiscriminately, which may hurt the user experience because different users have diverse interests in popularity. In this work, we propose a novel debias framework with knowledge graph (AWING), which adaptively alleviates popularity bias from the users’ perspective. Concretely, we explore fine-grained preferences (including popularity preference) behind a user-item interaction by using the heterogeneous graph transformer over the knowledge graph embedded with popularity nodes and endow the preferences with explicit semantics. Based on this idea, we can manipulate how much popularity preference affects recommendation results and improves the exposure rate of nonpopular items while considering the popularity preferences of different users. Experiments on public datasets show that the proposed method AWING can effectively alleviate popularity bias and ensure the user experience at the same time. The case study further demonstrates the feasibility of AWING on the explainable recommendation task.
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Jun, Han Jong, Jae Hee Kim, Deuk Young Rhee, and Sun Woo Chang. "“SeoulHouse2Vec”: An Embedding-Based Collaborative Filtering Housing Recommender System for Analyzing Housing Preference." Sustainability 12, no. 17 (August 26, 2020): 6964. http://dx.doi.org/10.3390/su12176964.
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Housing preference is the subjective and relative preference of users toward housing alternatives and studies in the field have been conducted to analyze the housing preferences of groups with sharing the same socio-demographic attributes. However, previous studies may not suggest the preference of individuals. In this regard, this study proposes “SeoulHouse2Vec,” an embedding-based collaborative filtering housing recommendation system for analyzing atypical and nonlinear housing preference of individuals. The model maps users and items in each dense vector space which are called embedding layers. This model may reflect trade-offs between the alternatives and recommend unexpected housing items and thus improve rational housing decision-making. The model expanded the search scope of housing alternatives to the entire city of Seoul utilizing public big data and GIS data. The preferences derived from the results can be used by suppliers, individual investors, and policymakers. Especially for architects, the architectural planning and design process will reflect users’ perspective and preferences, and provide quantitative data in the housing decision-making process for urban planning and administrative units.
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Telemala, Joseph P., and Hussein Suleman. "Exploring Topic-language Preferences in Multilingual Swahili Information Retrieval in Tanzania." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 6 (November 30, 2021): 1–30. http://dx.doi.org/10.1145/3458671.
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Habitual switching of languages is a common behaviour among polyglots when searching for information on the Web. Studies in information retrieval (IR) and multilingual information retrieval (MLIR) suggest that part of the reason for such regular switching of languages is the topic of search. Unlike survey-based studies, this study uses query and click-through logs. It exploits the querying and results selection behaviour of Swahili MLIR system users to explore how topic of search (query) is associated with language preferences—topic-language preferences. This article is based on a carefully controlled study using Swahili-speaking Web users in Tanzania who interacted with a guided multilingual search engine. From the statistical analysis of queries and click-through logs, it was revealed that language preferences may be associated with the topics of search. The results also suggest that language preferences are not static; they vary along the course of Web search from query to results selection. In most of the topics, users either had significantly no language preference or preferred to query in Kiswahili and changed their preference to either English or no preference for language when selecting/clicking on the results. The findings of this study might provide researchers with more insights in developing better MLIR systems that support certain types of users and in certain scenarios.
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Pu, Pearl, and Li Chen. "User-Involved Preference Elicitation for Product Search and Recommender Systems." AI Magazine 29, no. 4 (December 28, 2008): 93. http://dx.doi.org/10.1609/aimag.v29i4.2200.
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We address user system interaction issues in product search and recommender systems: how to help users select the most preferential item from a large collection of alternatives. As such systems must crucially rely on an accurate and complete model of user preferences, the acquisition of this model becomes the central subject of our paper. Many tools used today do not satisfactorily assist users to establish this model because they do not adequately focus on fundamental decision objectives, help them reveal hidden preferences, revise conflicting preferences, or explicitly reason about tradeoffs. As a result, users fail to find the outcomes that best satisfy their needs and preferences. In this article, we provide some analyses of common areas of design pitfalls and derive a set of design guidelines that assist the user in avoiding these problems in three important areas: user preference elicitation, preference revision, and explanation interfaces. For each area, we describe the state-of-the-art of the developed techniques and discuss concrete scenarios where they have been applied and tested.
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Luo, Jianhong, Xuwei Pan, Shixiong Wang, and Yujing Huang. "Identifying target audience on enterprise social network." Industrial Management & Data Systems 119, no. 1 (February 4, 2019): 111–28. http://dx.doi.org/10.1108/imds-01-2018-0007.
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Purpose Delivering messages and information to potentially interested users is one of the distinguishing applications of online enterprise social network (ESN). The purpose of this paper is to provide insights to better understand the repost preferences of users and provide personalized information service in enterprise social media marketing. Design/methodology/approach It is accomplished by constructing a target audience identification framework. Repost preference latent Dirichlet allocation (RPLDA) topic model topic model is proposed to understand the mass user online repost preferences toward different contents. A topic-oriented preference metric is proposed to measure the preference degree of individual users. And the function of reposting forecasting is formulated to identify target audience. Findings The empirical research shows the following: a total of 20 percent of the repost users in ESN represent the key active users who are particularly interested in the latent topic of messages in ESN and fits Pareto distribution; and the target audience identification framework can successfully identify different target key users for messages with different latent topics. Practical implications The findings should motivate marketing managers to improve enterprise brand by identifying key target audience in ESN and marketing in a way that truthfully reflects personalized preferences. Originality/value This study runs counter to most current business practices, which tend to use simple popularity to seek important users. Adaptively and dynamically identifying target audience appears to have considerable potential, especially in the rapidly growing area of enterprise social media information service.
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Tewell, Eamon C. "Frequent Internet Users May Prefer More Health Care Information and Participation in Decision-Making." Evidence Based Library and Information Practice 9, no. 1 (March 5, 2014): 51. http://dx.doi.org/10.18438/b8990n.
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Objective – To determine whether there is a significant relationship between patients’ frequency of Internet use and their health care information and decision-making preferences.
Design – Cross-sectional questionnaire survey.
Settings – Undergraduate classes at a large state university and senior-oriented computer classes at public libraries and senior centers.
Subjects – 438 respondents, including 226 undergraduates (mean age 20) and 212 community-dwelling older adults (mean age 72).
Methods – Respondents were administered the Health Information Wants Questionnaire (HIWQ), a 21-item instrument designed to measure preferences for 7 types of health information and decision-making, in group settings.
Main Results – The younger age group spent significantly more time online compared to the older age group. Frequent Internet users in both populations expressed an overall preference for more information regarding diagnosis, but less information for psychosocial and health care provider concerns. Internet use was positively correlated to the overall preference rating, leading the researchers to suggest that, as a whole, regular Internet users prefer more information and independence in decision-making.
Conclusions – The study concludes that Internet use frequency is associated with an overall preference for obtaining health information and participating in decision making. Internet use as related to different types of preferences is inconsistent. Age was not found to be associated with the overall preference rating, and time spent online is proposed to be a stronger indicator of respondents’ health information preferences. The authors suggest that future studies utilizing the HIWQ take a longitudinal approach in order to better track how patient preferences for information may evolve over time.
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Almu, Abba, Aliyu Ahmad, Abubakar Roko, and Mansur Aliyu. "Incorporating Preference Changes through Users’ Input in Collaborative Filtering Movie Recommender System." International Journal of Information Technology and Computer Science 14, no. 4 (August 8, 2022): 48–56. http://dx.doi.org/10.5815/ijitcs.2022.04.05.
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The usefulness of Collaborative filtering recommender system is affected by its ability to capture users' preference changes on the recommended items during recommendation process. This makes it easy for the system to satisfy users' interest over time providing good and quality recommendations. The Existing system studied fails to solicit for user inputs on the recommended items and it is also unable to incorporate users' preference changes with time which lead to poor quality recommendations. In this work, an Enhanced Movie Recommender system that recommends movies to users is presented to improve the quality of recommendations. The system solicits for users' inputs to create a user profiles. It then incorporates a set of new features (such as age and genre) to be able to predict user's preference changes with time. This enabled it to recommend movies to the users based on users new preferences. The experimental study conducted on Netflix and Movielens datasets demonstrated that, compared to the existing work, the proposed work improved the recommendation results to the users based on the values of Precision and RMSE obtained in this study which in turn returns good recommendations to the users.
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Sakurai, Keigo, Ren Togo, Takahiro Ogawa, and Miki Haseyama. "Controllable Music Playlist Generation Based on Knowledge Graph and Reinforcement Learning." Sensors 22, no. 10 (May 13, 2022): 3722. http://dx.doi.org/10.3390/s22103722.
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In this study, we propose a novel music playlist generation method based on a knowledge graph and reinforcement learning. The development of music streaming platforms has transformed the social dynamics of music consumption and paved a new way of accessing and listening to music. The playlist generation is one of the most important multimedia techniques, which aims to recommend music tracks by sensing the vast amount of musical data and the users’ listening histories from music streaming services. Conventional playlist generation methods have difficulty capturing the target users’ long-term preferences. To overcome the difficulty, we use a reinforcement learning algorithm that can consider the target users’ long-term preferences. Furthermore, we introduce the following two new ideas: using the informative knowledge graph data to promote efficient optimization through reinforcement learning, and setting the flexible reward function that target users can design the parameters of itself to guide target users to new types of music tracks. We confirm the effectiveness of the proposed method by verifying the prediction performance based on listening history and the guidance performance to music tracks that can satisfy the target user’s unique preference.
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Andreozzi, Sergio, Danilo Montesi, and Rocco Moretti. "XMatch: A Language for Satisfaction-Based Selection of Grid Services." Scientific Programming 13, no. 4 (2005): 299–316. http://dx.doi.org/10.1155/2005/294529.
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Grid systems enable the sharing of a large number of geographically-dispersed resources among different communities of users. They require a mapping functionality for the association of users requests expressed in terms of requirements and preferences to actual resources. This functionality should deal with a potentially high number of similar resources and with the diversity of the perceived satisfactions of users. We propose XMatch, a query language enabling the expression of the user request in terms of the expected satisfaction over XML-based representation of available resources. This language offers a compact way for users to express their preferences for Grid resources and enable the maximization of the global preference.
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J, Jose Immanuvel. "Movie Recommendation System." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 2611–15. http://dx.doi.org/10.22214/ijraset.2022.44430.
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Abstract: Movie recommendation system proposed whose primary objective is to suggest a recommended list through singular value decomposition collaborative filtering and cosine similarity. The present work improves these approaches by taking the movies’ content information into account during the item similarity calculations. The proposed approach recommends the top n recommendation list of movies to users on user’s interest preferences that were not already rated. Graphically shows the percentage of already viewed movies by user and movies recommended to User. Now a day’s recommendation system has changed the fashion of looking the items of our interest. OTT Movie Application Recommendation for mobile users is crucial. It performs a complete aggregation of user preferences, reviews and emotions to help you make suitable movies. It needs every precision and timeliness, however, this can be info filtering approach that’s accustomed predict the preference of that user. Recommender System may be a system that seeks to predict or filter preferences in keeping with the user’s selections. The very common purpose where recommender system is applied are OTT platforms, search engines, articles, music, videos etc. during this work we tend to propose a Collaborative approach-based Movie Recommendation system. It is supported collaborative filtering approach that creates use of the knowledge provided by users, analyzes them so recommends the flick that’s best suited to the user at that point.
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Rigi, Mohammad Amin, and Farid Khoshalhan. "Eliciting User Preferences in Multi-Agent Meeting Scheduling Problem." International Journal of Intelligent Information Technologies 7, no. 2 (April 2011): 45–62. http://dx.doi.org/10.4018/jiit.2011040103.
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Meeting Scheduling Problem (MSP) arranges meetings between a number of participants. Reaching consensus in arranging a meeting is very diffuclt and time-consuming when the number of participants is large. One efficient approach for overcoming this problem is the use of multi-agent systems. In a multi-agent system, agents are deciding on behalf of their users. They must be able to elicite their users’ preferences in an effective way. This paper focuses on the elicitation of users’ preferences. Analytical hierarchy process (AHP) - which is known for its ability to determine preferences - is used in this research. Specifically, an adaptive preference modeling technique based on AHP is developed and implemented in a system and the initial validation results are encouraging.
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Sun, Ke, Tieyun Qian, Tong Chen, Yile Liang, Quoc Viet Hung Nguyen, and Hongzhi Yin. "Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 214–21. http://dx.doi.org/10.1609/aaai.v34i01.5353.
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Point-of-Interest (POI) recommendation has been a trending research topic as it generates personalized suggestions on facilities for users from a large number of candidate venues. Since users' check-in records can be viewed as a long sequence, methods based on recurrent neural networks (RNNs) have recently shown promising applicability for this task. However, existing RNN-based methods either neglect users' long-term preferences or overlook the geographical relations among recently visited POIs when modeling users' short-term preferences, thus making the recommendation results unreliable. To address the above limitations, we propose a novel method named Long- and Short-Term Preference Modeling (LSTPM) for next-POI recommendation. In particular, the proposed model consists of a nonlocal network for long-term preference modeling and a geo-dilated RNN for short-term preference learning. Extensive experiments on two real-world datasets demonstrate that our model yields significant improvements over the state-of-the-art methods.
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Alshehri, Aziz, and Fayez Alotaibi. "Predicting Users Mobile App Privacy Preferences." Journal of Computer and Communications 07, no. 10 (2019): 147–56. http://dx.doi.org/10.4236/jcc.2019.710014.
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Kasztelan, Kamil, and Jakub Smołka. "Preferences of modern mobile app users." Journal of Computer Sciences Institute 23 (June 30, 2022): 71–76. http://dx.doi.org/10.35784/jcsi.2820.
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Each user group has its own preferences for mobile applications. A better app will increase the satisfaction of existing users and encourage new people to download it. People are used to it that it's hard to get rid of them. A survey was conducted in the Włodawa district in the first quarter of 2020, in which 150 random people took part. It has been noticed that life with a mobile device in hand has become a habit. Users more willingly and more often use the help of mobile devices during shopping while looking for product information and promotions. It has been observed that users pay more attention to application security, wanting to be sure that their data is safe. A small group of people would give up their mobile device and start using traditional methods
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Vitali, Fabio. "The next frontier of users' preferences." Interactions 13, no. 1 (January 2006): 38–39. http://dx.doi.org/10.1145/1109069.1109093.
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Ozer, Basak, and Mehmet Emin Baris. "Landscape Design and Park Users’ Preferences." Procedia - Social and Behavioral Sciences 82 (July 2013): 604–7. http://dx.doi.org/10.1016/j.sbspro.2013.06.317.
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Oguego, C. L., J. C. Augusto, A. Muñoz, and M. Springett. "Using argumentation to manage users’ preferences." Future Generation Computer Systems 81 (April 2018): 235–43. http://dx.doi.org/10.1016/j.future.2017.09.040.
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Krizek, Kevin, and Ahmed El-Geneidy. "Segmenting Preferences and Habits of Transit Users and Non-Users." Journal of Public Transportation 10, no. 3 (September 2007): 71–94. http://dx.doi.org/10.5038/2375-0901.10.3.5.
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Peters, Dominik, and Ariel D. Procaccia. "Preference Elicitation as Average-Case Sorting." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 6 (May 18, 2021): 5647–55. http://dx.doi.org/10.1609/aaai.v35i6.16709.
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Many decision making systems require users to indicate their preferences via a ranking. It is common to elicit such rankings through pairwise comparison queries. By using sorting algorithms, this can be achieved by asking at most O(m log m) adaptive comparison queries. However, in many cases we have some advance (probabilistic) information about the user's preferences, for instance if we have a learnt model of the user's preferences or if we expect the user's preferences to be correlated with those of previous users. For these cases, we design elicitation algorithms that ask fewer questions in expectation, by building on results for average-case sorting. If the user's preferences are drawn from a Mallows phi model, O(m) queries are enough; for a mixture of k Mallows models, log k + O(m) queries are enough; for Plackett-Luce models, the answer varies with the alternative weights. Our results match information-theoretic lower bounds. We also provide empirical evidence for the benefits of our approach.
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Becher, Stefan, and Armin Gerl. "ConTra Preference Language: Privacy Preference Unification via Privacy Interfaces." Sensors 22, no. 14 (July 20, 2022): 5428. http://dx.doi.org/10.3390/s22145428.
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After the enactment of the GDPR in 2018, many companies were forced to rethink their privacy management in order to comply with the new legal framework. These changes mostly affect the Controller to achieve GDPR-compliant privacy policies and management.However, measures to give users a better understanding of privacy, which is essential to generate legitimate interest in the Controller, are often skipped. We recommend addressing this issue by the usage of privacy preference languages, whereas users define rules regarding their preferences for privacy handling. In the literature, preference languages only work with their corresponding privacy language, which limits their applicability. In this paper, we propose the ConTra preference language, which we envision to support users during privacy policy negotiation while meeting current technical and legal requirements. Therefore, ConTra preferences are defined showing its expressiveness, extensibility, and applicability in resource-limited IoT scenarios. In addition, we introduce a generic approach which provides privacy language compatibility for unified preference matching.
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Khadka, Salu, Pragya Shrestha Chaise, and Sujin Shrestha. "Restaurant Recommendation System Using User Based Collaborative Filtering." Asian Journal of Electrical Sciences 9, no. 2 (May 30, 2021): 17–24. http://dx.doi.org/10.51983/ajes-2020.9.2.2552.
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A recommendation system is an application that can identify entities of interest for a person and provide suggestions based on the past record of person’s likes and preferences. The entity of interest can be anything, for example it can be a product, a movie or a news article. Recommender system is an effective way to help users to obtain the personalized and useful information. However, due to complexity and dynamic, the traditional recommender system cannot work well in mobile environment. Keeping such things into consideration, this recommendation system aims to recommend restaurants to users using their past preferences so they do not need to go through a list of choices. The recommender system adopts a user preference model by using the features of user's visited restaurants, and utilizes the location information of user via GPS(Global Positioning System) using LBS(Location Based System) and restaurants to dynamically generate the recommendation results using collaborative filtering technique. The suggestions will be based on the user preferences obtained from the past ratings and reviews given by the user, frequently visited cuisines of the user and the time preference of the user. Moreover, a brief analysis of reviews is also made to provide user a computed synopsis of the restaurant using text mining algorithm.
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Tian, Yaru, Hua Peng, Xiaoxia Dong, Liwang Li, and Wenqi Zhu. "Consumers’ Brand Preferences for Infant Formula: A Grounded Theory Approach." Sustainability 14, no. 13 (June 22, 2022): 7600. http://dx.doi.org/10.3390/su14137600.
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In recent years, the market pattern of infant formula in China has changed dramatically. The market share of domestic infant formula has exceeded that of imports. The essence of the market share change of domestic and foreign brands is the change of consumers’ brand preferences. To explore which factors affected consumers’ brand preferences, our study conducted a qualitative research method based on the grounded theory, through in-depth interviews with 60 mothers in the Beijing-Tianjin-Hebei region, systematically identifying the factors which affect consumers’ brand preferences for infant formula, which allowed us to establish a theoretical model for them. We found that product characteristics and external environmental factors could directly affect the formation of consumers’ brand preferences, or indirectly through the two intermediary factors of buyers and users. In addition, in the consumption of infant formula, buyers and users were separated, and infants, as actual users, were an important factor that could not be ignored in brand preference.
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Stach, Jens. "How memorable experiences influence brand preference." Qualitative Market Research: An International Journal 20, no. 4 (September 11, 2017): 394–415. http://dx.doi.org/10.1108/qmr-03-2016-0023.
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Purpose This paper aims to illuminate mechanisms through which memorable experiences with brands create lasting preferences. It is based on the proposition that intense positive (negative) affective consumption in the consumer’s youth creates powerful imprints, which influence brand preference (distaste) throughout life. Design/methodology/approach Autobiographical memories with Nutella are retrieved from three different user groups, i.e. heavy-, light- and non-users. The retrieved memory narratives are analysed using conditioning theory, i.e. operant, classical or no conditioning are identified and compared across groups. Findings The research’s central proposition is affirmed, yet the dominant form of conditioning mechanism differs per group. Operant conditioning outperforms classical conditioning in creating strong and lasting preferences. Heavy- and non-users predominantly exhibit in-tensely positive and negative operant conditioning, respectively. Light-users on the other hand recall less affectively intense consumption experiences, mainly featuring classical conditioning. The light-users’ recollections suggest a mere exposure effect to be more appropriate in describing the preference formation in this user group. Research limitations/implications Users not having experienced affectively intense consumption, i.e. light-users, are likely to be influenced in their preference over time through other factors, which this paper does not focus on. Practical implications Memory elicitation and exploration provides valuable insights to shape both promotional as well as advertising strategies. Originality/value The study extends existing theory on conditioning in marketing by first using a novel qualitative approach to analyse conditioning procedures in real-life settings, and second, it highlights operant conditioning’s superior ability in creating lasting preferences.
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Faria, João Roberto Gomes de, Aline Yurika Inskava, and Sven Thomas Planitzer. "Lighting preferences in individual offices." Ambiente Construído 17, no. 1 (March 2017): 39–53. http://dx.doi.org/10.1590/s1678-86212017000100122.
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Abstract Workplaces with good daylighting offer visual comfort to users, give them a series of physiological and psychological benefits and allow good performance of visual activities, besides saving energy. However, this solution is not always adopted: lighting type preferences involve many variables besides the availability of daylight. This paper explores a case study through the analysis of questionnaire answers and computer simulations of a series of metrics related to quality of lighting with the aim of finding explanations for the lighting preferences of individual office users. The results show that, although the offices present good daylighting conditions and no glare potential, and users are satisfied with daylighting, these parameters are not sufficient to explain the predominant lighting preferences. The findings have also shown that there is no consensus about which parameters potentially cause visual comfort, while the parameters that cause discomfort are clearly identified. In addition, in this study, 49% of the preference for mixed lighting (daylight plus electrical light) can be explained by the fact that mixed lighting produces better modeling than daylighting alone.