scholarly journals The Recommender Systems Model for Smart Cities

2019 ◽  
Vol 8 (2S7) ◽  
pp. 451-456 ◽  
Keyword(s):  
Marketing Strategy ◽  
Recommender Systems ◽  
Recommendation System ◽  
Online Shopping ◽  
Smart Cities ◽  
Systems Model ◽  
Paper Review ◽  
Systems Models ◽  
System Project ◽  
Organization Marketing

Recommender systems were introduced in the early 1990s. They did not get too much attention and were limited to a narrow domain implemented by only a few companies until the outburst of E-commerce. As online shopping became popular, the recommender system started becoming an integral part of an organization marketing strategy and since then they have completely evolved a lot. This give an opportunity to start with a recommendation System project by collecting information from news of users to provide a best recommendation. The cities become smatter so, this paper review different methods of implementing Recommender systems models for smart cities along with their drawbacks and possible improvements.

scholarly journals Fashion Recommendation Systems, Models and Methods: A Review

Informatics ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 49
Author(s):  
Samit Chakraborty ◽  
Md. Saiful Hoque ◽  
Naimur Rahman Jeem ◽  
Manik Chandra Biswas ◽  
Deepayan Bardhan ◽  
...  
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
Fast Fashion ◽  
Potential Models ◽  
Scholarly Article ◽  
Enormous Amount ◽  
Shopping Experience ◽  
Tremendous Amount ◽  
Systems Models ◽  
Filtering Techniques

In recent years, the textile and fashion industries have witnessed an enormous amount of growth in fast fashion. On e-commerce platforms, where numerous choices are available, an efficient recommendation system is required to sort, order, and efficiently convey relevant product content or information to users. Image-based fashion recommendation systems (FRSs) have attracted a huge amount of attention from fast fashion retailers as they provide a personalized shopping experience to consumers. With the technological advancements, this branch of artificial intelligence exhibits a tremendous amount of potential in image processing, parsing, classification, and segmentation. Despite its huge potential, the number of academic articles on this topic is limited. The available studies do not provide a rigorous review of fashion recommendation systems and the corresponding filtering techniques. To the best of the authors’ knowledge, this is the first scholarly article to review the state-of-the-art fashion recommendation systems and the corresponding filtering techniques. In addition, this review also explores various potential models that could be implemented to develop fashion recommendation systems in the future. This paper will help researchers, academics, and practitioners who are interested in machine learning, computer vision, and fashion retailing to understand the characteristics of the different fashion recommendation systems.

Legal document recommendation system: A cluster based pairwise similarity computation

2021 ◽  
pp. 1-13
Author(s):  
Jenish Dhanani ◽  
Rupa Mehta ◽  
Dipti Rana
Keyword(s):  
Recommender Systems ◽  
Recommendation System ◽  
Real Life ◽  
Citation Network ◽  
Search Space ◽  
Pairwise Similarity ◽  
Large Numbers ◽  
Legal Document ◽  
Legal Domain ◽  
Similarity Scores

Legal practitioners analyze relevant previous judgments to prepare favorable and advantageous arguments for an ongoing case. In Legal domain, recommender systems (RS) effectively identify and recommend referentially and/or semantically relevant judgments. Due to the availability of enormous amounts of judgments, RS needs to compute pairwise similarity scores for all unique judgment pairs in advance, aiming to minimize the recommendation response time. This practice introduces the scalability issue as the number of pairs to be computed increases quadratically with the number of judgments i.e., O (n2). However, there is a limited number of pairs consisting of strong relevance among the judgments. Therefore, it is insignificant to compute similarities for pairs consisting of trivial relevance between judgments. To address the scalability issue, this research proposes a graph clustering based novel Legal Document Recommendation System (LDRS) that forms clusters of referentially similar judgments and within those clusters find semantically relevant judgments. Hence, pairwise similarity scores are computed for each cluster to restrict search space within-cluster only instead of the entire corpus. Thus, the proposed LDRS severely reduces the number of similarity computations that enable large numbers of judgments to be handled. It exploits a highly scalable Louvain approach to cluster judgment citation network, and Doc2Vec to capture the semantic relevance among judgments within a cluster. The efficacy and efficiency of the proposed LDRS are evaluated and analyzed using the large real-life judgments of the Supreme Court of India. The experimental results demonstrate the encouraging performance of proposed LDRS in terms of Accuracy, F1-Scores, MCC Scores, and computational complexity, which validates the applicability for scalable recommender systems.

Semantic Web mining for Content-Based Online Shopping Recommender Systems

2019 ◽  
Vol 15 (4) ◽  
pp. 41-56 ◽  
Author(s):  
Ibukun Tolulope Afolabi ◽  
Opeyemi Samuel Makinde ◽  
Olufunke Oyejoke Oladipupo
Keyword(s):  
Semantic Web ◽  
Recommender Systems ◽  
Web Mining ◽  
Online Shopping ◽  
Semantic Analysis ◽  
Second Phase ◽  
Web Content ◽  
Textual Data ◽  
Content Mining ◽  
Bayes Algorithm

Currently, for content-based recommendations, semantic analysis of text from webpages seems to be a major problem. In this research, we present a semantic web content mining approach for recommender systems in online shopping. The methodology is based on two major phases. The first phase is the semantic preprocessing of textual data using the combination of a developed ontology and an existing ontology. The second phase uses the Naïve Bayes algorithm to make the recommendations. The output of the system is evaluated using precision, recall and f-measure. The results from the system showed that the semantic preprocessing improved the recommendation accuracy of the recommender system by 5.2% over the existing approach. Also, the developed system is able to provide a platform for content-based recommendation in online shopping. This system has an edge over the existing recommender approaches because it is able to analyze the textual contents of users feedback on a product in order to provide the necessary product recommendation.

scholarly journals A Predictive Vehicle Ride Sharing Recommendation System for Smart Cities Commuting

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 177-191
Author(s):  
Theodoros Anagnostopoulos
Keyword(s):  
Recommendation System ◽  
Smart Cities ◽  
Urban Pollution ◽  
Real Data ◽  
Movement Behavior ◽  
Daily Movement ◽  
Passenger Transportation ◽  
Ride Sharing ◽  
Mobility Behavior ◽  
User Movement

Smart Cities (or Cities 2.0) are an evolution in citizen habitation. In such cities, transport commuting is changing rapidly with the proliferation of contemporary vehicular technology. New models of vehicle ride sharing systems are changing the way citizens commute in their daily movement schedule. The use of a private vehicle per single passenger transportation is no longer viable in sustainable Smart Cities (SC) because of the vehicles’ resource allocation and urban pollution. The current research on car ride sharing systems is widely expanding in a range of contemporary technologies, however, without covering a multidisciplinary approach. In this paper, the focus is on performing a multidisciplinary research on car riding systems taking into consideration personalized user mobility behavior by providing next destination prediction as well as a recommender system based on riders’ personalized information. Specifically, it proposes a predictive vehicle ride sharing system for commuting, which has impact on the SC green ecosystem. The adopted system also provides a recommendation to citizens to select the persons they would like to commute with. An Artificial Intelligence (AI)-enabled weighted pattern matching model is used to assess user movement behavior in SC and provide the best predicted recommendation list of commuting users. Citizens are then able to engage a current trip to next destination with the more suitable user provided by the list. An experimented is conducted with real data from the municipality of New Philadelphia, in SC of Athens, Greece, to implement the proposed system and observe certain user movement behavior. The results are promising for the incorporation of the adopted system to other SCs.

DSS for Web Mining Using Recommendation System

2017 ◽  
pp. 22-34 ◽  
Author(s):  
Varaprasad Rao M ◽  
Vishnu Murthy G
Keyword(s):  
Recommender Systems ◽  
Web Mining ◽  
Recommendation System ◽  
User Behavior ◽  
Web Content ◽  
Web Structure ◽  
Web Structure Mining ◽  
Content Mining ◽  
Computer Based ◽  
The Many

Decision Supports Systems (DSS) are computer-based information systems designed to help managers to select one of the many alternative solutions to a problem. A DSS is an interactive computer based information system with an organized collection of models, people, procedures, software, databases, telecommunication, and devices, which helps decision makers to solve unstructured or semi-structured business problems. Web mining is the application of data mining techniques to discover patterns from the World Wide Web. Web mining can be divided into three different types – Web usage mining, Web content mining and Web structure mining. Recommender systems (RS) aim to capture the user behavior by suggesting/recommending users with relevant items or services that they find interesting in. Recommender systems have gained prominence in the field of information technology, e-commerce, etc., by inferring personalized recommendations by effectively pruning from a universal set of choices that directed users to identify content of interest.

Enhancing Recommender Diversity Using Gaussian Cloud Transformation

2015 ◽  
Vol 23 (04) ◽  
pp. 521-544 ◽  
Author(s):  
Jinpeng Chen ◽  
Yu Liu ◽  
Deyi Li
Keyword(s):  
Recommender Systems ◽  
Recommendation System ◽  
Data Sets ◽  
Complete Spectrum ◽  
Unified Framework ◽  
Real World Data ◽  
Balance Accuracy ◽  
Average Accuracy ◽  
Novel Method ◽  
Attention To Diversity

The recommender systems community is paying great attention to diversity as key qualities beyond accuracy in real recommendation scenarios. Multifarious diversity-increasing approaches have been developed to enhance recommendation diversity in the related literature while making personalized recommendations to users. In this work, we present Gaussian Cloud Recommendation Algorithm (GCRA), a novel method designed to balance accuracy and diversity personalized top-N recommendation lists in order to capture the user's complete spectrum of tastes. Our proposed algorithm does not require semantic information. Meanwhile we propose a unified framework to extend the traditional CF algorithms via utilizing GCRA for improving the recommendation system performance. Our work builds upon prior research on recommender systems. Though being detrimental to average accuracy, we show that our method can capture the user's complete spectrum of interests. Systematic experiments on three real-world data sets have demonstrated the effectiveness of our proposed approach in learning both accuracy and diversity.

scholarly journals DISCOVER POINTS OF INTEREST BASED ON USERS’ INTERNET SEARCHES THROUGH AN ONLINE SHOPPING WEBSITE

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 975-980
Author(s):  
G. Shahriari Mehr ◽  
M. R. Delavar ◽  
C. Claramunt ◽  
B. N. Araabi ◽  
M. R. A. Dehaqani
Keyword(s):  
Spatial Analysis ◽  
Recommendation System ◽  
Online Shopping ◽  
Contextual Information ◽  
Information Source ◽  
Primary Source ◽  
The Internet ◽  
Points Of Interest ◽  
Internet Searches ◽  
The City

Abstract. In recent years, the development of the Internet plays a significant role in human's daily activities. One of the most important effects of the Internet is the change in the process of shopping. The advent of online shopping leads to establish a new channel for customers to obtain information about their desired goods and demands. Although many customers collect information from online channel, they also wish to try and search for their required goods at the stores. Besides, discovering this data leads to a new source for spatial analysis to find the users’ interests. Therefore, we can consider this data as a contextual information source for spatial analysis or primary source for recommending points of interest (POIs). In this research, our aim is to discover a relation among the users' internet searches and the goods at the stores to recommend the best store to the users. Euclidean distance is used to calculate the similarity between users' searches and the available goods at the stores. The proposed method has been implemented in the city of Tehran, capital of Iran. The results show that the users’ internet search behavior plays an essential role in the recommendation system which provides stores to the users based on the similarity among the users’ internet searches and the available goods at the stores.

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