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ردیف | عنوان | نوع |
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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 |