کارابرن عزیز، مقالات isi بالاترین کیفیت ترجمه را دارند، ترجمه آنها کامل و دقیق می باشد (محتوای جداول و شکل های نیز ترجمه شده اند) و از بهترین مجلات isi انتخاب گردیده اند. همچنین تمامی ترجمه ها دارای ضمانت کیفیت بوده و در صورت عدم رضایت کاربر مبلغ عینا عودت داده خواهد شد.
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Social network data to alleviate cold-start in recommender system: A systematic review
داده های شبکه های اجتماعی برای سبک کردن شروع دوباره در سیستم توصیه گر: یک مرور سیستماتیک-2018
Recommender Systems are currently highly relevant for helping users deal with the information overload they suffer from the large volume of data on the web, and automatically suggest the most appropriate items that meet users needs. However, in cases in which a user is new to Recommender System, the system cannot recommend items that are relevant to her/him because of lack of previous information about the user and/or the user-item rating history that helps to determine the users preferences. This problem is known as cold-start, which remains open because it does not have a final solution. Social networks have been employed as a good source of information to determine users preferences to mitigate the cold-start problem. This paper presents the results of a Systematic Literature Review on Collaborative Filtering-based Recommender System that uses social network data to mitigate the cold-start problem. This Systematic Literature Review compiled the papers published between 2011–2017, to select the most recent studies in the area. Each selected paper was evaluated and classified according to the depth which social networks used to mitigate the cold-start problem. The final results show that there are several publications that use the information of the social networks within the Recommender System; however, few research papers currently use this data to mitigate the cold-start problem.
keywords: Cold start| Social network| Collaborative filtering| Recommender system| Systematic literature review
A social recommendation method based on an adaptive neighbor selection mechanism
یک روش توصیه اجتماعی برمبنای یک مکانیزم انتخاب همسایه سازگار-2018
Recommender systems are techniques to make personalized recommendations of items to users. In e-commerce sites and online sharing communities, providing high quality recommendations is an important issue which can help the users to make effective decisions to select a set of items. Collaborative filtering is an important type of the recommender systems that produces user specific recommendations of the items based on the patterns of ratings or usage (e.g. purchases). However, the quality of predicted ratings and neighbor selection for the users are important problems in the recommender systems. Selecting suitable neighbors set for the users leads to improve the accuracy of ratings prediction in recommendation process. In this paper, a novel social recommendation method is proposed which is based on an adaptive neighbor selection mechanism. In the proposed method first of all, initial neighbors set of the users is calculated using clustering algorithm. In this step, the combination of historical ratings and social information between the users are used to form initial neighbors set for the users. Then, these neighbor sets are used to predict initial ratings of the unseen items. Moreover, the quality of the initial predicted ratings is evaluated using a reliability measure which is based on the historical ratings and social information between the users. Then, a confidence model is proposed to remove useless users from the initial neighbors of the users and form a new adapted neighbors set for the users. Finally, new ratings of the unseen items are predicted using the new adapted neighbors set of the users and the interested items are recommended to the active user. Experimental results on three real-world datasets show that the proposed method significantly outperforms several state-of-the-art recommendation methods.
keywords: Recommender systems| Adaptive neighbor selection| Confidence| Reliability| Trust
Computing Hierarchical Summary from Two-Dimensional Big Data Streams
خلاصه سازی محاسبات سلسله مراتبی از جریانهای داده های بزرگ دو بعدی-2018
There are many application domains, where hierarchical data is inherent, but surprisingly, there are few techniques for mining patterns from such important data. Hierarchical Heavy Hitters (HHH) and multilevel and Cross-Level Association Rules (CLAR) mining are well-known hierarchical pattern mining techniques. The problem in these techniques; however, is that they focus on capturing only global patterns from data but cannot identify local contextual patterns. Another problem in these techniques is that they treat all data items in the transaction equally and do not consider the sequential nature of the relationship among items within a transaction; hence, they cannot capture the correlation semantic within the transactions of the data items. There are many applications such as clickstream mining, healthcare data mining, network monitoring, and recommender systems, which require to identify local contextual patterns and correlation semantics. In this work, we introduce a new concept, which can capture the sequential nature of the relationship between pairs of hierarchical items at multiple concept levels and can capture local contextual patterns within the context of the global patterns. We call this notion Hierarchically Correlated Heavy Hitters (HCHH). Specifically, the proposed approach finds the correlation between items corresponding to hierarchically discounted frequency counts. We have provided formal definitions of the proposed concept and developed algorithmic approaches for computing HCHH in data streams efficiently. The proposed HCHH algorithm have deterministic error guarantees, and space bounds. It requires O(η/ϵpϵs) memory, where h is a small constant, and ϵp ∈ [0,1], ϵs ∈ [0,1] are user defined parameters on upper bounds of estimation error. We have compared the proposed HCHH concept with existing hierarchical pattern mining approaches both theoretically as well as experimentally.
Index Terms: Hierarchical patterns, association rules, frequent patterns
Emerging Trends, Issues, and Challenges in Big Data and Its Implementation toward Future Smart Cities: Part 2
مسائل و چالش های نو ظهور در داده های بزرگ و پیاده سازی آن در مسیر آینده شهرهای هوشمند: قسمت 2-2018
Due to urbanization, smart cities are emerging as a priority for research and development across the world. However, the rapid progress in smart cities research is posing enormous challenges in terms of the large amounts and various types of data at an unprecedented granularity, speed, and complexity that are increasingly produced by IoT sensors via emerging communication technologies. Meanwhile, the accumulation of huge amounts of data can be used to support intelligent decisions for better lives. Therefore, smart cities are data-driven. But effective computing, like distributed and parallel computing, artificial intelligence, and cloud/fog computing are the basic infrastructure for data processing, especially big data processing, and are the key factors for success in future smart cities. The use of big data can certainly help in creating cities where infrastructure and resources are used in a more efficient manner. The articles in this special section explore emerging trends, issues, and challenges in big data and its implementation toward future smart cities.
Keywords: Special issues and sections,Big Data, Smart cities,Quality of experience,Recommender systems,Resource management,Systems architecture, Urban areas
A novel recommendation method based on social network using matrix factorization technique
یک روش نوین توصیه برمبنای شبکه اجتماعی با اسفاده از روش عامل سازی ماتریسی-2018
The rapid development of information technology and the fast growth of Internet have facilitated an explosion of information which has accentuated the information overload problem. Recommender systems have emerged in response to this problem and helped users to find their interesting contents. With increasingly complicated social context, how to fulfill personalized needs better has become a new trend in personalized recommendation service studies. In order to alleviate the sparsity problem of recommender systems meanwhile increase their accuracy and diversity in complex contexts, we propose a novel recommendation method based on social network using matrix factorization technique. In this method, we cluster users and consider a variety of complex factors. The simulation results on two benchmark data sets and a real data set show that our method achieves superior performance to existing methods.
keywords: Recommendation method |Social network |K-harmonic means |Particle swarm optimization |Matrix factorization
An empirical evaluation of high utility itemset mining algorithms
ارزیابی تجربی از الگوریتم های استخراج ابزارها با کاربرد زیاد-2018
High utility itemset mining (HUIM) has emerged as an important research topic in data mining, with applications to retail-market data analysis, stock market prediction, and recommender systems, etc. How ever, there are very few empirical studies that systematically compare the performance of state-of-the-art HUIM algorithms. In this paper, we present an experimental evaluation on 10 major HUIM algorithms, using 9 real world and 27 synthetic datasets to evaluate their performance. Our experiments show that EFIM and d2HUP are generally the top two performers in running time, while EFIM also consumes the least memory in most cases. In order to compare these two algorithms in depth, we use another 45 synthetic datasets with varying parameters so as to study the influence of the related parameters, in par ticular the number of transactions, the number of distinct items and average transaction length, on the running time and memory consumption of EFIM and d2HUP. In this work, we demonstrate that, d2HUP is more efficient than EFIM under low minimum utility values and with large sparse datasets, in terms of running time; although EFIM is the fastest in dense real datasets, it is among the slowest algorithms in sparse datasets. We suggest that, when a dataset is very sparse or the average transaction length is large, and running time is favoured over memory consumption, d2HUP should be chosen. Finally, we compare d2HUP and EFIM with two newest algorithms, mHUIMiner and ULB-Miner, and find these two algorithms have moderate performance. This work has reference value for researchers and practitioners when choosing the most appropriate HUIM algorithm for their specific applications.
Keywords: Itemset mining ، High utility itemsets ، State-of-the-art high utility itemset mining
FUCL mining technique for book recommender system in library service
تکنیک کاوش FUCL برای سیستم توصیف کتاب در خدمات کتابخانه-2018
Recommender systems are important tools in library websites that assists the user to find the appropriate books. With the rapid development of internet technologies and the number of books has varied which waste of time and difficulty for finding from library searching system. This research presents a book recommendation system for university libraries to support user interests which are related in the same topic and faculty. The main motive of this research is to develop the technique which recommends the most suitable books to users according to the faculty of the user profile with book category, and book loan or FUCL technique. This is based on the combined features of association rule mining. The results show that FUCL mining technique is suitable to apply for the recommender book tool in the library and has a higher accuracy value than other technique.
Keywords: Recommendation System; data mining; association rule; user profile
Building hybrid Scientific similarity networks using research papers and social networks
شبکه های همبستگی علمی شباهت های علمی با استفاده از مقالات پژوهشی و شبکه های اجتماعی-2017
Research collaboration is very important in the modern scientific world, because it provides a wide range of opportunities, from the knowledge transfer between departments, institutions or even countries, to experimental research, where scientists also share materials and equipment. Because of that the field of building and analyzing collaboration networks grows in popularity. In this work we propose the methodology of building a hybrid scientific similarity network based on the features obtained from two leading scientific platforms – ResearchGate and Scopus. Experimental evaluation demonstrates good Research collaboration is very important in the modern scientific world, because it provides a wide range of opportunities, from the knowledge transfer between departments, institutions or even countries, to experimental research, where scientists also share materials and equipment. Because of that the field of building and analyzing collaboration networks grows in popularity. In this work we propose the methodology of building a hybrid scientific similarity network based on the features obtained from two leading scientific platforms – ResearchGate and Scopus. Experimental evaluation demonstrates good quality of the proposed approach and ability of the hybrid network to generate interconnections not distinguishable when using a single network.
Keywords: collaboration network | recommender systems| hybrid network | Scopus , ResearchGate
Investigating the role of the rating prediction task in granularity-based group recommender systems and big data scenarios
بررسی نقش کار پیش بینی ارزیابی در سیستم های پیشنهاد دهنده گروه مبتنی بر دانه دانه ها و سناریوهای داده های بزرگ-2017
Nowadays, one important issue for companies is the efficient dealing of the big data problem, which means that their business intelligence has to manage huge amounts of data. An interesting case in point is flyers distribution. Research and market figures prove that the distribution of advertising flyers still represents a valuable tool to attract potential customers to a company. It goes without saying that including personalized content in a company’s flyer is more likely to yield better results than offering the same flyer to all potential clients. However, producing personalized flyers would imply unaffordable costs for a company. An efficient trade-off solution between accuracy and costs could be to define a maximum number of different flyers addressing different groups of users interested in their content. In order to systematically support this and similar trade-off solutions, we propose a novel type of group recommendations, which is able to detect a number of groups of end-users equal to the number of recommendation lists (e.g., flyers) that can be produced (i.e., the granularity with which the system can operate). Moreover, it can provide suggestions to the detected specific groups of users. In particular, we focus on the rating prediction for those items users do not evaluate. Indeed, rating prediction represents the main task that a recommender system is asked to perform and it becomes even more central if included into a group recommender system, since the predictions might be built for each user or for each group. Our approach also gives the possibility to efficiently manage the curse of the dimensionality phenomena caused by the sparsity of the ratings arising from big data handling. We present four granularity-based group recommender systems using different rating prediction algorithms and architectures. These systems employ the same algorithms to carry out other tasks (i.e., those that do not predict the ratings) and this allows us to evaluate which rating prediction approach is the most effective in terms of accuracy. Experiments on two real-world datasets show that, unlike group predictions, single user predictions can lead to improvements in the recommendation accuracy and the dealing of the curse of the dimensionality phenomena.
Keywords: Group recommendation | Clustering | Rating prediction | Big data
Towards a social and context-aware mobile recommendation system for tourism
به سوی یک سیستم توصیه ای سیار اجتماعی و آگاه از متن برای گردشگری-2017
Loyalty in tourism is one of the main concerns for tourist organizations and researchers alike. Recently, technology in general and CRM and social networks in particular have been identified as important enablers for loyalty in tourism. This paper presents POST-VIA 360, a platform devoted to support the whole life-cycle of tourism loyalty after the first visit. The system is designed to collect data from the initial visit by means of pervasive approaches. Once data is analysed, POST-VIA 360 produces accurate after visit data and, once returned, is able to offer relevant recommendations based on positioning and bio-inspired recommender systems. To validate the system, a case study comparing recommendations from the POST-VIA 360 and a group of experts was conducted. Results show that the accuracy of system’s recommendations is remarkable compared to previous efforts in the field.
Keywords: Tourism | Customer relationship management | Bio-inspired algorithms | Pervasive | Geographic information systems