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نتیجه جستجو - Personalized recommendation

تعداد مقالات یافته شده: 20
ردیف عنوان نوع
1 A sub-one quasi-norm-based similarity measure for collaborative filtering in recommender systems
اندازه گیری شباهت sub-one مبتنی بر شبه هنجار برای فیلتر مشترک در سیستم های توصیه گر-2019
Collaborative filtering (CF) is one of the most successful approaches for an online store to make personalized recommendations through its recommender systems. A neighborhoodbased CF method makes recommendations to a target customer based on the similar preference of the target customer and those in the database. Similarity measuring between users directly contributes to an effective recommendation. In this paper, we propose a sub-one quasi-norm-based similarity measure for collaborative filtering in a recommender system. The proposed similarity measure shows its advantages over those commonly used similarity measures in the literature by making better use of rating values and deemphasizing the dissimilarity between users. Computational experiments using various real-life datasets clearly indicate the superiority of the proposed similarity measure, no matter in fully co-rated, sparsely co-rated or cold-start scenarios.
Keywords: Recommender system | Collaborative filtering | Neighborhood-based CF | Similarity measure | p quasi-norm
مقاله انگلیسی
2 A personalized electricity tariff recommender system based on advanced metering infrastructure and collaborative filtering
یک سیستم توصیه گر تعرفه برق شخصی مبتنی بر زیرساخت های اندازه گیری پیشرفته و فیلترهای مشترک-2019
Deregulation of electricity retail markets and the advancement of energy informatics have been supporting the transition of electricity retail business into an electronic business, where different electricity retailers can provide different electricity tariff plans to end users through digital media. In this context, end users are facing an information filtering challenge of choosing the most suitable tariff plans from a set of candidate tariff plans. This paper proposes a new personalized recommendation system that makes intelligent electricity tariff recommendations to end users. The proposed approach starts by collecting a group of end users’ electricity consumption profiles through the advanced metering infrastructure and, based on this information, it infers the preference of individual users on each tariff plan. Based on the inferred preference degree, a new matrix factorization is established based on a collaborative filtering algorithm that is capable of recommending most suitable tariff plans to an arbitrary target user. The proposed recommendation system is validated against a number of scenarios that are generated based on simulated tariff plan sets and on a modified Australian “Smart Grid, Smart City” dataset.
Keywords: Collaborative filtering | Power market | Recommendation system | Demand side management | Smart grid
مقاله انگلیسی
3 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
4 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
5 An integrated recommender system for improved accuracy and aggregate diversity
یک سیستم توصیه گر یکپارچه برای بهبود دقت و تنوع کل-2019
Information explosion creates dilemma in finding preferred products from the digital marketplaces. Thus, it is challenging for online companies to develop an efficient recommender system for large portfolio of products. The aim of this research is to develop an integrated recommender system model for online companies, with the ability of providing personalized services to their customers. The K-nearest neighbors (KNN) algorithm uses similarity matrices for performing the recommendation system; however, multiple drawbacks associated with the conventional KNN algorithm have been identified. Thus, an algorithm considering weight metric is used to select only significant nearest neighbors (SNN). Using secondary dataset on MovieLens and combining four types of prediction models, the study develops an integrated recommender system model to identify SNN and predict accurate personalized recommendations at lower computation cost. A timestamp used in the integrated model improves the performance of the personalized recommender system. The research contributes to behavioral analytics and recommender system literature by providing an integrated decision-making model for improved accuracy and aggregate diversity. The proposed prediction model helps to improve the profitability of online companies by selling diverse and preferred portfolio of products to their customers.
Keywords: Recommender system | Behavioral analytics | Extreme learning | Aggregate diversity | E-business | Decision support system
مقاله انگلیسی
6 Context-aware recommender systems using hierarchical hidden Markov model
سیستم های توصیه گر آگاه از زمینه با استفاده از مدل مارکوف مخفی سلسله مراتبی-2019
Recommender systems often generate recommendations based on user’s prior preferences. Users’ preferences may change over time due to user mode change or context change, identification of such a change is important for generating personalized recommendations. Many earlier methods have been developed under the assumption that each user has a fixed pattern. Regardless of these changes, the recommendation may not match the user’s personal preference and this recommendation will not be useful to the user based on the current context of the user. Context-aware recommender systems deal with this problem by utilizing contextual information that affects user preferences and states. Using contextual information is challenging because it is not always possible to obtain all the contextual information. Also, adding various types of contexts to recommender systems increases its dimensionality and sparsity. This paper presents a novel hierarchical hidden Markov model to identify changes in user’s preferences over time by modeling the latent context of users. Using the user-selected items, the proposed method models the user as a hidden Markov process and considers the current context of the user as a hidden variable. The latent contexts are automatically learned for each user utilizing hidden Markov model on the data collected from the user’s feedback sequences. The results of the experiments, on the benchmark data sets, show that the proposed model has a better performance compared to other methods.
Keywords: Context-aware recommender system | Hidden Markov model | Latent context | Recommender systems
مقاله انگلیسی
7 User interest dynamics on personalized recommendation
پویایی علاقه کاربر بر روی توصیه شخصی سازی شده-2019
Four real recommender system datasets, the Netflix, SMovieLens, LMovieLens and RYM datasets, are analyzed to gain an insight into their user interest characteristics. A preference of active users to cold objects and a diverse interest of inactive users are revealed, which characteristics are introduced to improve the personalized recommendation algorithms. Based on seven different algorithms, we propose a general improvement formula for them, and finally four new algorithms are obtained. Tested on the four datasets, all the new algorithms are found to outperform the original ones in recommendation accuracy, diversity and novelty, except for the diversity and novelty compared with a heat conduction algorithm. And the recommendation accuracy for the cold objects, referring to the objects with small degrees, is also improved. Moreover, one of the new algorithms shows better performance than two other excellent algorithms in many aspects, i.e., the hybrid algorithm of heat conduction and mass diffusion, and the biased heat conduction algorithm. Our work may shed a new light on personalized recommendation from the perspective of connecting empirical study with algorithm design
Keywords: Infophysics | User interest dynamics | Recommender system
مقاله انگلیسی
8 Enhancing the long-term performance of recommender system
افزایش عملکرد بلند مدت سیستم توصیه گر-2019
The recommender system is a critically important tool in the online commercial system and provides users with personalized recommendations on items. So far, numerous recommendation algorithms have been made to further improve the recommendation performance in a single-step recommendation, while the long-term recommendation performance is neglected. In this paper, we proposed an approach called Adjustment of Recommendation List (ARL) to enhance the long-term recommendation accuracy. In order to observe the long-term accuracy, we developed an evolution model of network to simulate the interaction between the recommender system and user’s behavior. The result shows that not only long-term recommendation accuracy can be enhanced significantly but the diversity of items in an online system maintains healthy. Notably, an optimal parameter n∗ of ARL existed in the long-term recommendation, indicating that there is a trade-off between keeping the diversity of item and user’s preference to maximize the long-term recommendation accuracy. Finally, we confirmed that the optimal parameter n∗ is stable during the evolving network, which reveals the robustness of the ARL method.
Keywords:Long-term | Recommender system | Evolution model | Robustness
مقاله انگلیسی
9 A big-data oriented recommendation method based on multi-objective optimization
یک روش توصیه داده های بزرگ گرا برای بهینه سازی چند هدفی-2019
Due to its successful application in recommender systems, collaborative filtering (CF) has become a hot research topic in data mining. For traditional CF-based recommender systems, the accuracy of recommendation results can be guaranteed while the diversity will be lost. An ideal recommender system should be built with both accurate and diverse performance. Faced with accuracy–diversity dilemma, we propose a novel recommendation method based on MapReduce framework. In MapReduce framework, a block computational technique is used to shorten the operational time. And an improved collaborative filtering model is refined with a novel similarity computational process which considers many factors. By translating the procedure of generating personalized recommendation results into a multi-objective optimization problem, the multiple conflicts between accuracy and diversity are well handled. The experimental results demonstrate that our method outperforms other state-of-the-art methods.
Keywords: Recommender systems | Multi-objective optimization | MapReduce | Accuracy | Diversity
مقاله انگلیسی
10 A new similarity measure for collaborative filtering based recommender systems
یک اندازه گیری شباهت جدید برای سیستم های توصیه گر مبتنی بر فیلتر مشترک-2019
The objective of a recommender system is to provide customers with personalized recommendations while selecting an item among a set of products (movies, books, etc.). The collaborative filtering is the most used technique for recommender systems. One of the main components of a recommender system based on the collaborative filtering technique, is the similarity measure used to determine the set of users having the same behavior with regard to the selected items. Several similarity functions have been proposed, with different performances in terms of accuracy and quality of recommendations. In this paper, we propose a new simple and efficient similarity measure. Its mathematical expression is determined through the following paper contributions: 1) transforming some intuitive and qualitative conditions, that should be satisfied by the similarity measure, into relevant mathematical equations namely: the integral equation, the linear system of differential equations and a non-linear system and 2) resolving the equations to achieve the kernel function of the similarity measure. The extensive experimental study driven on a benchmark datasets shows that the proposed similarity measure is very competitive, especially in terms of accuracy, with regards to some representative similarity measures of the literature.
Keywords: Recommendation systems | Collaborative filtering | Neighborhood based CF | Similarity measure
مقاله انگلیسی
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