scholarly journals Data-Driven Interaction Review of an Ed-Tech Application

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1910 ◽  
Author(s):  
Baldominos ◽  
Quintana
Keyword(s):  
Recommender System ◽  
Exploratory Behavior ◽  
Data Driven ◽  
Direct Connection ◽  
Recommendation Algorithm ◽  
Data Points

Smile and Learn is an Ed-Tech company that runs a smart library with more that100 applications, games and interactive stories, aimed at children aged two to 10 and their families.The platform gathers thousands of data points from the interaction with the system to subsequentlyoffer reports and recommendations. Given the complexity of navigating all the content, the libraryimplements a recommender system. The purpose of this paper is to evaluate two aspects of such systemfocused on children: the influence of the order of recommendations on user exploratory behavior, andthe impact of the choice of the recommendation algorithm on engagement. The assessment, based ondata collected between 15 October 2018 and 1 December 2018, required the analysis of the number ofclicks performed on the recommendations depending on their ordering, and an A/B/C testing wheretwo standard recommendation algorithmswere comparedwith a randomrecommendation that servedas baseline. The results suggest a direct connection between the order of the recommendation and theinterest raised, and the superiority of recommendations based on popularity against other alternatives.

Identifying Attack Models for Securing Cluster-Based Recommendation System

2020 ◽  
Vol 14 ◽  
Author(s):  
Amreen Ahmad ◽  
Tanvir Ahmad ◽  
Ishita Tripathi
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Relevant Information ◽  
System Function ◽  
Recommendation Algorithm ◽  
Underlying Network ◽  
Entire Dataset ◽  
Attack Models ◽  
Wide Usage

: The immense growth of information has led to the wide usage of recommender systems for retrieving relevant information. One of the widely used methods for recommendation is collaborative filtering. However, such methods suffer from two problems, scalability and sparsity. In the proposed research, the two issues of collaborative filtering are addressed and a cluster-based recommender system is proposed. For the identification of potential clusters from the underlying network, Shapley value concept is used, which divides users into different clusters. After that, the recommendation algorithm is performed in every respective cluster. The proposed system recommends an item to a specific user based on the ratings of the item’s different attributes. Thus, it reduces the running time of the overall algorithm, since it avoids the overhead of computation involved when the algorithm is executed over the entire dataset. Besides, the security of the recommender system is one of the major concerns nowadays. Attackers can come in the form of ordinary users and introduce bias in the system to force the system function that is advantageous for them. In this paper, we identify different attack models that could hamper the security of the proposed cluster-based recommender system. The efficiency of the proposed research is validated by conducting experiments on student dataset.

scholarly journals Application of Improved Recommendation System Based on Spark Platform in Big Data Analysis

2016 ◽  
Vol 16 (6) ◽  
pp. 245-255 ◽  
Author(s):  
Li Xie ◽  
Wenbo Zhou ◽  
Yaosen Li
Keyword(s):  
Big Data ◽  
Recommender System ◽  
Recommendation System ◽  
Parallel Implementation ◽  
Implementation Process ◽  
Recommendation Algorithm ◽  
Filtration Problem ◽  
Information Filtration ◽  
Similarity Information ◽  
Better Than

Abstract In the era of big data, people have to face information filtration problem. For those cases when users do not or cannot express their demands clearly, recommender system can analyse user’s information more proactive and intelligent to filter out something users want. This property makes recommender system play a very important role in the field of e-commerce, social network and so on. The collaborative filtering recommendation algorithm based on Alternating Least Squares (ALS) is one of common algorithms using matrix factorization technique of recommendation system. In this paper, we design the parallel implementation process of the recommendation algorithm based on Spark platform and the related technology research of recommendation systems. Because of the shortcomings of the recommendation algorithm based on ALS model, a new loss function is designed. Before the model is trained, the similarity information of users and items is fused. The experimental results show that the performance of the proposed algorithm is better than that of algorithm based on ALS.

Pre-Filling Collaborative Filtering Algorithm Based on Matrix Factorization

2013 ◽  
Vol 411-414 ◽  
pp. 2223-2228
Author(s):  
Dong Liang Su ◽  
Zhi Ming Cui ◽  
Jian Wu ◽  
Peng Peng Zhao
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Matrix Factorization ◽  
Nearest Neighbor ◽  
User Profile ◽  
Personalized Recommendation ◽  
System Quality ◽  
Recommendation Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Personalized Recommender System

Nowadays personalized recommendation algorithm of e-commerce can hardly meet the needs of users as an ever-increasing number of users and items in personalized recommender system has brought about sparsity of user-item rating matrix and the emergence of more and more new users has threatened recommender system quality. This paper puts forward a pre-filled collaborative filtering recommendation algorithm based on matrix factorization, pre-filling user-item matrixes by matrix factorization and building nearest-neighbor models according to new user profile information, thus mitigating the influence of matrix sparsity and new users and improving the accuracy of recommender system. The experimental results suggest that this algorithm is more precise and effective than the traditional one under the condition of extremely sparse user-item rating matrix.

CAN DISSIMILAR USERS CONTRIBUTE TO ACCURACY AND DIVERSITY OF PERSONALIZED RECOMMENDATION?

2010 ◽  
Vol 21 (10) ◽  
pp. 1217-1227 ◽  
Author(s):  
WEI ZENG ◽  
MING-SHENG SHANG ◽  
QIAN-MING ZHANG ◽  
LINYUAN LÜ ◽  
TAO ZHOU
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
High Accuracy ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Recommendation Algorithms ◽  
Collaborative Filtering Algorithm ◽  
High Diversity ◽  
Better Than

Recommender systems are becoming a popular and important set of personalization techniques that assist individual users with navigating through the rapidly growing amount of information. A good recommender system should be able to not only find out the objects preferred by users, but also help users in discovering their personalized tastes. The former corresponds to high accuracy of the recommendation, while the latter to high diversity. A big challenge is to design an algorithm that provides both highly accurate and diverse recommendation. Traditional recommendation algorithms only take into account the contributions of similar users, thus, they tend to recommend popular items for users ignoring the diversity of recommendations. In this paper, we propose a recommendation algorithm by considering both the effects of similar and dissimilar users under the framework of collaborative filtering. Extensive analyses on three datasets, namely MovieLens, Netflix and Amazon, show that our method performs much better than the standard collaborative filtering algorithm for both accuracy and diversity.

scholarly journals Personality or Value: A Comparative Study of Psychographic Segmentation Based on an Online Review Enhanced Recommender System

2019 ◽  
Vol 9 (10) ◽  
pp. 1992 ◽  
Author(s):  
Hui Liu ◽  
Yinghui Huang ◽  
Zichao Wang ◽  
Kai Liu ◽  
Xiangen Hu ◽  
...  
Keyword(s):  
Big Data ◽  
Language Processing ◽  
Recommender System ◽  
Consumer Preferences ◽  
Explanatory Power ◽  
Marketing Research ◽  
User Preferences ◽  
User Preference ◽  
Data Driven ◽  
Product Categories

Big consumer data promises to be a game changer in applied and empirical marketing research. However, investigations of how big data helps inform consumers’ psychological aspects have, thus far, only received scant attention. Psychographics has been shown to be a valuable market segmentation path in understanding consumer preferences. Although in the context of e-commerce, as a component of psychographic segmentation, personality has been proven to be effective for prediction of e-commerce user preferences, it still remains unclear whether psychographic segmentation is practically influential in understanding user preferences across different product categories. To the best of our knowledge, we provide the first quantitative demonstration of the promising effect and relative importance of psychographic segmentation in predicting users’ online purchasing preferences across different product categories in e-commerce by using a data-driven approach. We first construct two online psychographic lexicons that include the Big Five Factor (BFF) personality traits and Schwartz Value Survey (SVS) using natural language processing (NLP) methods that are based on behavior measurements of users’ word use. We then incorporate the lexicons in a deep neural network (DNN)-based recommender system to predict users’ online purchasing preferences considering the new progress in segmentation-based user preference prediction methods. Overall, segmenting consumers into heterogeneous groups surprisingly does not demonstrate a significant improvement in understanding consumer preferences. Psychographic variables (both BFF and SVS) significantly improve the explanatory power of e-consumer preferences, whereas the improvement in prediction power is not significant. The SVS tends to outperform BFF segmentation, except for some product categories. Additionally, the DNN significantly outperforms previous methods. An e-commerce-oriented SVS measurement and segmentation approach that integrates both BFF and the SVS is recommended. The strong empirical evidence provides both practical guidance for e-commerce product development, marketing and recommendations, and a methodological reference for big data-driven marketing research.

The identification of stages in diachronic data: variability-based neighbour clustering

Corpora ◽  
2008 ◽  
Vol 3 (1) ◽  
pp. 59-81 ◽  
Author(s):  
Stefan Th. Gries ◽  
Martin Hilpert
Keyword(s):  
Case Studies ◽  
Hierarchical Clustering ◽  
The Other ◽  
Data Driven ◽  
Clustering Method ◽  
Chronological Order ◽  
Bottom Up ◽  
Data Variability ◽  
Data Points ◽  
Corpus Data

In this paper, we introduce a data-driven bottom-up clustering method for the identification of stages in diachronic corpus data that differ from each other quantitatively. Much like regular approaches to hierarchical clustering, it is based on identifying and merging the most cohesive groups of data points, but, unlike regular approaches to clustering, it allows for the merging of temporally adjacent data, thus, in effect, preserving the chronological order. We exemplify the method with two case studies, one on verbal complementation of shall, the other on the development of the perfect in English.

Big Data-driven Electricity Plan Recommender System

2018 ◽  
Author(s):  
Yuan Zhang ◽  
Weicong Kong ◽  
Zhao Yang Dong ◽  
Ke Meng ◽  
Jin Qiu
Keyword(s):  
Big Data ◽  
Recommender System ◽  
Data Driven

scholarly journals Ontological Modeling of Certification and Inspection Process to Support Smart Disclosure of Product Information

2016 ◽  
Vol 3 (2) ◽  
pp. 86-108 ◽  
Author(s):  
Djoko S. Sayogo ◽  
Weijia Ran ◽  
Giri Kumar Tayi ◽  
Joanne S. Luciano ◽  
Luis F. Luna-Reyes ◽  
...  
Keyword(s):  
Information Integration ◽  
Product Information ◽  
Policy Formulation ◽  
Data Driven ◽  
Private Regulation ◽  
Inspection Process ◽  
Data Points ◽  
Regulation Mechanisms ◽  
Disparate Data ◽  
Ontological Modeling

The increasing number of certification schemes diminishes the utility of certifications as private regulation and creates several policy challenges. The undergoing efforts to help consumers verify the accuracy of information created by private regulation mechanisms such as certification are currently confronted with the complexities of certification and labeling systems and the difficulties in linking data points across various certification schemes. This paper presents the development of certification and inspection ontology to support smart disclosure of product information. This study proposes that the resulting ontology enables information integration and standardization thus supporting knowledge discovery and sharing by synthesizing information across disparate data sources that is valuable for informing data-driven policy formulation. The ontology also supports standardization of an agreed set of terms and semantics for currently fragmented certification and inspection schemes to support comparability across different certification schemes. The accuracy and consistency of the proposed ontology are verified by using current reasoning tools to run queries based on a set of predefined competency questions.

Research on Recommendation Algorithm in Social Networks

2014 ◽  
Vol 496-500 ◽  
pp. 1865-1868
Author(s):  
Hu Xin Tang ◽  
Xu Qian
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Recommender System ◽  
Recommendation System ◽  
Simulation Experiment ◽  
Recommendation Algorithm ◽  
Simulation Experiments ◽  
Correlation Algorithm ◽  
Rapid Calculation ◽  
Relative Attractiveness

Research status and development of the recommendation system are studied, the focus of evaluation of recommender system and recommender system based on social network in two aspects, and puts forward some improved algorithm, and achieved certain results. KDD Cup 2012 Track data for the simulation experiments on the correlation algorithm based on search engine, has been shown in different positions on the relative attractiveness of advertising, numerical user. At the same time, rapid calculation of the degree of correlation between a user and other users of an algorithm is given, and then quickly given the recommendation results. KDD Cup 2012 Track data for the simulation experiment of the algorithm, and the analysis result is given.

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