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نتیجه جستجو - Personalization

تعداد مقالات یافته شده: 33
ردیف عنوان نوع
1 An extensive study on the evolution of context-aware personalized travel recommender systems
یک مطالعه گسترده در مورد تکامل سیستمهای توصیه گر سفر شخصی آگاه از متن-2020
Ever since the beginning of civilization, travel for various causes exists as an essential part of human life so as travel recommendations, though the early form of recommendations were the accrued experiences shared by the community. Modern recommender systems evolved along with the growth of Information Technology and are contributing to all industry and service segments inclusive of travel and tourism. The journey started with generic recommender engines which gave way to personalized recommender systems and further advanced to contextualized personalization with advent of artificial intelligence. Current era is also witnessing a boom in social media usage and the social media big data is acting as a critical input for various analytics with no exception for recommender systems. This paper details about the study conducted on the evolution of travel recommender systems, their features and current set of limitations. We also discuss on the key algorithms being used for classification and recommendation processes and metrics that can be used to evaluate the performance of the algorithms and thereby the recommenders.
Keywords: Recommender system | Personalization | Context aware | Big data | Travel and tourism
مقاله انگلیسی
2 Modelling and simulation of security-aware task scheduling in cloud computing based on Blockchain technology
مدل سازی و شبیه سازی برنامه ریزی کار وظیفه آگاهانه در محاسبات ابری مبتنی بر فناوری بلاکچین -2019
Although a lot of work has been done in the domain, tasks scheduling and resource allocation in cloud computing remain the challenging problems for both industry and academia. Security in scheduling in highly distributed computing environments is one of the most important criteria in the era of personalization of the cloud services. Blockchain became recently a promising technology for integration with the cloud clusters and improvement of the security of cloud transactions and access to data and application codes. In this paper, we developed a new model of the cloud scheduler based on the blockchain technology. Differently to the other similar models, we tried to offload the implementation of the blockchain modules. We developed a novel ’proof–- of–schedule’ consensus algorithm (instead of ’proof–of–work’) and used the Stackelberg games for the improvement of the approval of the generated schedules. The developed model has been experimentally simulated and validated by using the new original cloud simulator. The proposed Blockchain Scheduler was also compared with other selected cloud schedulers. The experiments shows that the applied approach improved significantly the efficiency of prepared schedules, in most cases, simulator returns a schedule with better makespan than existing individual scheduling modules.
Keywords: Cloud scheduling | Blockchain | Stackelberg game | Proof of schedule
مقاله انگلیسی
3 Multiobjective e-commerce recommendations based on hypergraph ranking
توصیه های تجارت الکترونیکی چندوجهی مبتنی بر رتبه بندی هایپرگراف-2019
Recommender systems are emerging in e-commerce as important promotion tools to assist customers to discover potentially interesting items. Currently, most of these are single- objective and search for items that fit the overall preference of a particular user. In real applications, such as restaurant recommendations, however, users often have multiple ob- jectives such as group preferences and restaurant ambiance. This paper highlights the need for multi-objective recommendations and provides a solution using hypergraph ranking. A general User–Item–Attribute–Context data model is proposed to summarize different in- formation resources and high-order relationships for the construction of a multipartite hy- pergraph. This study develops an improved balanced hypergraph ranking method to rank different types of objects in hypergraph data. An overall framework is then proposed as a guideline for the implementation of multi-objective recommender systems. Empirical ex- periments are conducted with the dataset from a review site Yelp.com, and the outcomes demonstrate that the proposed model performs very well for multi-objective recommenda- tions. The experiments also demonstrate that this framework is still compatible for tradi- tional single-objective recommendations and can improve accuracy significantly. In conclu- sion, the proposed multi-objective recommendation framework is able to handle complex and changing demands for e-commerce customers.
Keywords: Recommender systems | E-commerce | User personalization | Hypergraph
مقاله انگلیسی
4 Online discrete choice models: Applications in personalized recommendations
مدل های انتخاب گسسته آنلاین: برنامه های کاربردی در توصیه های شخصی شده-2019
This paper presents a framework for estimating and updating user preferences in the context of app-based recommender systems. We specifically consider recommender systems which provide personalized menus of options to users. A Hierarchical Bayes procedure is applied in order to account for inter- and intra-consumer heterogeneity, representing random taste variations among individuals and among choice situations (menus) for a given individual, respectively. Three levels of preference parameters are estimated: population-level, individual- level and menu-specific. In the context of a recommender system, the estimation of these parameters is repeated periodically in an offline process in order to account for trends, such as changing market conditions. Furthermore, the individual-level parameters are updated in real-time as users make choices in order to incorporate the latest information from the users. This online update is computationally efficient which makes it feasible to embed it in a real-time recommender system. The estimated individual-level preferences are stored for each user and retrieved as inputs to a menu optimization model in order to provide recommendations. The proposed methodology is applied to both Monte-Carlo and real data. It is observed that the online update of the parameters is successful in improving the parameter estimates in real-time. This framework is relevant to various recommender systems that generate personalized recommendations ranging from transportation to e-commerce and online marketing, but is particularly useful when the attributes of the alternatives vary over time.
Keywords: Personalization | Intra-consumer heterogeneity | Hierarchical Bayes | Preference updates | recommender systems
مقاله انگلیسی
5 An extensive study on the evolution of context-aware personalized travel recommender systems
یک مطالعه گسترده در مورد تکامل سیستمهای توصیه گر سفر شخصی آگاه از متن-2019
Ever since the beginning of civilization, travel for various causes exists as an essential part of human life so as travel recommendations, though the early form of recommendations were the accrued experiences shared by the community. Modern recommender systems evolved along with the growth of Information Technology and are contributing to all industry and service segments inclusive of travel and tourism. The journey started with generic recommender engines which gave way to personalized recommender systems and further advanced to contextualized personalization with advent of artificial intelligence. Current era is also witnessing a boom in social media usage and the social media big data is acting as a critical input for various analytics with no exception for recommender systems. This paper details about the study conducted on the evolution of travel recommender systems, their features and current set of limitations. We also discuss on the key algorithms being used for classification and recommendation processes and metrics that can be used to evaluate the performance of the algorithms and thereby the recommenders.
Keywords: Recommender system | Personalization | Context aware | Big data | Travel and tourism
مقاله انگلیسی
6 Review of ontology-based recommender systems in e-learning
مرور سیستمهای پیشنهادی مبتنی بر هستی شناسی در یادگیری الکترونیکی-2019
In recent years there has been an enormous increase in learning resources available online through massive open online courses and learning management systems. In this context, personalized resource recommendation has become an even more significant challenge, thereby increasing research in that direction. Recommender systems use ontology, artificial intelligence, among other techniques to provide personalized recommendations. Ontology is a way to model learners and learning resources, among others, which helps to retrieve details. This, in turn, generates more relevant materials to learners. Ontologies have benefits of reusability, reasoning ability, and supports inference mechanisms, which helps to provide enhanced recommendations. The comprehensive survey in this paper gives an overview of the research in progress using ontology to achieve personalization in recommender systems in the e-learning domain.
Keywords: Human-computer interface | Intelligent tutoring systems | Computer-mediated communication | Cooperative/collaborative learning
مقاله انگلیسی
7 Proposing a novel method for improving the performance of collaborative filtering systems regarding the priority of similar users
پیشنهاد یک روش جدید برای بهبود عملکرد سیستم های فیلتر مشترک در رابطه با اولویت کاربران مشابه-2019
Recommender systems (RS) are an efficient and useful tool for personalizing services and providing efficient recommendations to users in different applications. One of the most successful techniques used in Recommender Systems is Collaborative Filtering (CF) which uses the rating matrix to find users with similar interests as the active user. The issue which usually arises with this method is the sparsity of the rating matrix which affects the process of finding similar users and the quality of the recommendations greatly. In this paper, a new method has been provided to increase the efficiency of the system against sparse data domains. The basis of the proposed method is extracting preference patterns from the rating matrix in the way that for each active user, a threelevel tree of neighboring users is constructed. The active user is situated in the root of the tree, direct neighbors of the active user in the second level and indirect neighbors of the active user are situated in the third level. Then the similarity level of the active user with its direct and indirect neighbors is calculated. Finally the calculated similarity value is used in the process of predicting the ratings. This factor affects the quality of the ratings given by the neighbors. Results of the experiments on Movielens and Jester datasets indicate that in most cases, the proposed method provides better results than other widely utilized methods.
Keywords: Recommender systems | Collaborative filtering | Users’ priority | Personalization | Similarity value
مقاله انگلیسی
8 New perspectives on gray sheep behavior in E-commerce recommendations
دیدگاه های جدید در مورد رفتار گوسفند خاکستری در توصیه های تجارت الکترونیکی-2019
With the exponential rise in the size of data being generated, personalization based on recommender systems has become an important aspect of digital marketing strategy of E-Commerce companies. Recommender systems also help these companies in cross-selling, up-selling and to increase the customer loyalty. However, presence of certain users, known as gray sheep users, with eccentric taste, minimizes the overall efficiency of the recommender systems. Hence, their identification and removal from the computation system is critical for more efficient recommendations. This work presents psychographic models-based approaches for gray sheep user identification with improved performance. It also studies gray sheep behavior across different domains and contexts, apart from introducing the idea of gray sheep items
Keywords: Personalization | Gray Sheep Users | Psychographic models | Cross Domain Recommender Systems | Context Aware Recommender Systems | Collaborative Filtering
مقاله انگلیسی
9 Proactive vs: reactive personalization: Can customization of privacy enhance user experience?
شخصی سازی فعالانه در مقابل واکنشی: آیا شخصی سازی حریم شخصی می تواند تجربه کاربر را افزایش دهد؟-2019
Online recommender systems have triggered widespread privacy concerns due to their reliance on personal user data for providing personalized services. To address these concerns, some systems have started allowing users to express their preferences before receiving personalized content (i.e., reactive personalization) rather than automatically pushing it to them (i.e., proactive personalization). However, this would mean constant calls for user action, which can adversely affect user experience. One potential solution is to offer users the ability to customize their privacy settings at the outset, thus obviating the need for constant consultation. We conducted a 2 (Personalization: Reactive vs. Proactive) X 3 (Customization of Settings: Absence vs. Action vs. Cue) factorial experiment (N=299) with a movie recommendation system. Findings show that interface cues suggesting customization enhance user experience, even in the presence of proactive personalization. They also highlight the important role played by negative privacy experiences in the past..
Keywords: Customization | Personalization | Recommender systems | Privacy | User experience | Experiment
مقاله انگلیسی
10 Serious games for rehabilitation: Gestural interaction in personalized gamified exercises through a recommender system
بازی های جدی برای توانبخشی: تعامل حرکاتی در تمرینات شخصی سازی شده شخصی از طریق سیستم توصیه کننده-2019
One of the principal problems of rehabilitation is that therapy sessions can be boring due the repetition of exercises. Serious games, and in particular exergames in rehabilitation, can motivate, engage and increase patients’ adherence to their treatment. Also, the automatic personalization of exercises to each patient can help therapists. Thus, the main objective of this work is to build an intelligent exergame-based rehabilitation system consisting of a platform with an exergame player and a designer tool. The intelligent platform includes a recommender system which analyzes user interactions, along with the user’s history, to select new gamified exercises for the user. The main contributions of this paper focus, first, on defining a recommender system based on different difficulty levels and user skills. The recommender system offers the ability to provide the user with a personalized game mode based on his own history and preferences. The results of a triple validation with experts, users and rehabilitation center professionals reveal a positive impact on gestural interaction and rehabilitation uses. Also, different methods are presented for testing the rehabilitation recommender system.
Keywords: Recommender systems | Exergames | Rehabilitation | Gamification | Serious games
مقاله انگلیسی
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