scholarly journals Canonical tensor scaling

2021 ◽  
Vol 13 (1) ◽  
pp. 13-21
Author(s):  
Tung Nguyen ◽  
Jeffrey Uhlmann
Keyword(s):  
Recommender System ◽  
Geographical Location ◽  
Tensor Completion ◽  
Multidimensional Arrays ◽  
Item Attributes ◽  
Product Ratings ◽  
Arbitrary Tensor

In this paper we generalize the canonical positive scaling of rows and columns of a matrix to the scaling of selected-rank subtensors of an arbitrary tensor. We expect our results and framework will prove useful for sparse-tensor completion required for generalizations of the recommender system problem beyond a matrix of user-product ratings to multidimensional arrays involving coordinates based both on user attributes (e.g., age, gender, geographical location, etc.) and product/item attributes (e.g., price, size, weight, etc.).

A Dynamic Recommender System with Fused Time and Location Factors

2019 ◽  
Vol 23 (1) ◽  
pp. 25-33
Author(s):  
Xinhua Wang ◽  
Peng Yin ◽  
Yukai Gao ◽  
Lei Guo ◽  
◽  
...  
Keyword(s):  
Recommender System ◽  
User Behavior ◽  
Geographical Location ◽  
User Preferences ◽  
Dynamic Features ◽  
Fusion Model ◽  
User Interest ◽  
Location Factors ◽  
User Behavior Analysis ◽  
Real World Datasets

A recommender system is an important tool to help users obtain content and overcome information overload. It can predict users’ interests and offer recommendations by analyzing their history behaviors. However, traditional recommender systems focus primarily on static user behavior analysis. Recently, with the promotion of the Netflix recommendation prize and the open dataset with location and time information, many researchers have focused on the dynamic characteristics of the recommender system (including the changes in the dynamic model of user interest), and begun to offer recommendations based on these dynamic features. Intuitively, these dynamic user features provide us with an effective method to learn user interests deeply. Based on the observations above, we present a dynamic fusion model by integrating geographical location, user preferences, and the time factor based on the Gibbs sampling process to provide better recommendations. To evaluate the performance of our proposed method, we conducted experiments on real-world datasets. The experimental results indicate that our proposed dynamic recommender system with fused time and location factors not only performs well in traditional scenarios, but also in sparsity situations where users appear at the first time.

scholarly journals CoupledCF: Learning Explicit and Implicit User-item Couplings in Recommendation for Deep Collaborative Filtering

2018 ◽  
Author(s):  
Quangui Zhang ◽  
Longbing Cao ◽  
Chengzhang Zhu ◽  
Zhiqiang Li ◽  
Jinguang Sun
Keyword(s):  
Collaborative Filtering ◽  
Performance Improvement ◽  
Recommender System ◽  
Real World ◽  
Matrix Factorization ◽  
Cold Start ◽  
Large Datasets ◽  
Empirical Results ◽  
The Rich ◽  
Item Attributes

Non-IID recommender system discloses the nature of recommendation and has shown its potential in improving recommendation quality and addressing issues such as sparsity and cold start. It leverages existing work that usually treats users/items as in- dependent while ignoring the rich couplings within and between users and items, leading to limited performance improvement. In reality, users/items are related with various couplings existing within and between users and items, which may better ex- plain how and why a user has personalized pref- erence on an item. This work builds on non- IID learning to propose a neural user-item cou- pling learning for collaborative filtering, called CoupledCF. CoupledCF jointly learns explicit and implicit couplings within/between users and items w.r.t. user/item attributes and deep features for deep CF recommendation. Empirical results on two real-world large datasets show that CoupledCF significantly outperforms two latest neural recom- menders: neural matrix factorization and Google’s Wide&Deep network.

Evaluating Prediction Accuracy, Developmental Challenges, and Issues of Recommender Systems

2018 ◽  
Vol 10 (2) ◽  
pp. 61-79 ◽  
Author(s):  
J. Sharon Moses ◽  
L.D. Dhinesh Babu
Keyword(s):  
Social Media ◽  
Recommender Systems ◽  
Recommender System ◽  
Prediction Accuracy ◽  
Recommendation Systems ◽  
Geographical Location ◽  
The Internet ◽  
The People ◽  
Implicit And Explicit ◽  
The Internet Of Things

Modern ways of living have made the people to depend on internet services for everything. The mounting information from various sources like social media, implicit and explicit information, user's geographical location, and the internet of things had increased the need of a recommender system. From e-governance to e-shopping, a recommender system helps people in finding the needed item or information and also boosts sales in the market of those items. Though many studies elaborate about recommendation systems, challenges in developing the recommendation systems, prevailing issues of recommendation systems and discussions on prediction accuracy are not detailed in any of the earlier works. Therefore, in this article, in order to increase the accuracy of the recommender system, the developmental challenges and issues in constructing recommender systems and for evaluation metrics in prediction accuracy are identified and detailed.

scholarly journals A Data-Driven Personalized Lighting Recommender System

2021 ◽  
Vol 4 ◽  
Author(s):  
Atousa Zarindast ◽  
Jonathan Wood
Keyword(s):  
Recommender System ◽  
Geographical Location ◽  
User Preferences ◽  
Geographical Information ◽  
User Interest ◽  
Smart Lighting ◽  
Home Based ◽  
Product Service ◽  
Color Scheme ◽  
Do So

Recommender systems attempt to identify and recommend the most preferable item (product-service) to individual users. These systems predict user interest in items based on related items, users, and the interactions between items and users. We aim to build an auto-routine and color scheme recommender system for home-based smart lighting that leverages a wealth of historical data and machine learning methods. We utilize an unsupervised method to recommend a routine for smart lighting. Moreover, by analyzing users’ daily logs, geographical location, temporal and usage information, we understand user preferences and predict their preferred light colors. To do so, users are clustered based on their geographical information and usage distribution. We then build and train a predictive model within each cluster and aggregate the results. Results indicate that models based on similar users increases the prediction accuracy, with and without prior knowledge about user preferences.

scholarly journals Addressing Complete New Item Cold-Start Recommendation: A Niche Item-Based Collaborative Filtering via Interrelationship Mining

2019 ◽  
Vol 9 (9) ◽  
pp. 1894 ◽  
Author(s):  
Zhi-Peng Zhang ◽  
Yasuo Kudo ◽  
Tetsuya Murai ◽  
Yong-Gong Ren
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Cold Start ◽  
Binary Relations ◽  
Excellent Performance ◽  
Satisfactory Accuracy ◽  
Available Information ◽  
Item Attributes ◽  
New Item ◽  
Cold Start Problem

Recommender system (RS) can be used to provide personalized recommendations based on the different tastes of users. Item-based collaborative filtering (IBCF) has been successfully applied to modern RSs because of its excellent performance, but it is susceptible to the new item cold-start problem, especially when a new item has no rating records (complete new item cold-start). Motivated by this, we propose a niche approach which applies interrelationship mining into IBCF in this paper. The proposed approach utilizes interrelationship mining to extract new binary relations between each pair of item attributes, and constructs interrelated attributes to rich the available information on a new item. Further, similarity, computed using interrelated attributes, can reflect characteristics between new items and others more accurately. Some significant properties, as well as the usage of interrelated attributes, are provided in detail. Experimental results obtained suggest that the proposed approach can effectively solve the complete new item cold-start problem of IBCF and can be used to provide new item recommendations with satisfactory accuracy and diversity in modern RSs.

A tes

2018 ◽  
Vol 1 (3) ◽  
Author(s):  
IJE Manager
Keyword(s):  
Energy Supply ◽  
Fossil Fuels ◽  
Geographical Location ◽  
Weather Conditions ◽  
Past Century ◽  
Solar Pv ◽  
Pv Systems ◽  
Trade Offs ◽  
A Site ◽  
Selection Methodology

In the past century, fossil fuels have dominated energy supply in Indonesia. However, concerns over emissions are likely to change the future energy supply. As people become more conscious of environmental issues, alternatives for energy are sought to reduce the environmental impacts. These include renewable energy (RE) sources such as solar photovoltaic (PV) systems. However, most RE sources like solar PV are not available continuously since they depend on weather conditions, in addition to geographical location. Bali has a stable and long sunny day with 12 hours of daylight throughout the year and an average insolation of 5.3 kWh/m2 per day. This study looks at the potential for on-grid solar PV to decarbonize energy in Bali. A site selection methodology using GIS is applied to measure solar PV potential. Firstly, the study investigates the boundaries related to environmental acceptability and economic objectives for land use in Bali. Secondly, the potential of solar energy is estimated by defining the suitable areas, given the technical assumptions of solar PV. Finally, the study extends the analysis to calculate the reduction in emissions when the calculated potential is installed. Some technical factors, such as tilting solar, and intermittency throughout the day, are outside the scope of this study. Based on this model, Bali has an annual electricity potential for 32-53 TWh from solar PV using amorphous thin-film silicon as the cheapest option. This potential amount to three times the electricity supply for the island in 2024 which is estimated at 10 TWh. Bali has an excessive potential to support its own electricity demand with renewables, however, some limitations exist with some trade-offs to realize the idea. These results aim to build a developmental vision of solar PV systems in Bali based on available land and the region’s irradiation.

Visual Elements of "Semar Calligraphy" on Cirebon Glass Painting of Kusdono’s Work

2020 ◽  
Vol 1 ◽  
pp. 43-47
Author(s):  
Yayah Rukiah
Keyword(s):  
Qualitative Research ◽  
Research Methods ◽  
Geographical Location ◽  
Qualitative Research Methods ◽  
Visual Elements ◽  
Arabic Calligraphy ◽  
Main Figure ◽  
Descriptive Qualitative

This research is focused on the visual elements contained on Kusdono's Cirebon glass painting. The writer uses descriptive qualitative research methods to examine the visual elements, with the technique of collecting data from books and journals that relates to the research object. The purpose of this study is to examine and find the meaning of Cirebon glass paintings. The results of this research are Semar as the main figure in the clown who always do good, keep the truth and obey the tenet that closely related to Islam. Arabic calligraphy reinforces Semar's figure. On the Cirebon glass painting, there are many mega mendung ornaments and wadasan which are Cirebon batik motifs, as well as the colors used in the coastal colors due to the geographical location of Cirebon City near the beach.  

Shifting perspectives: method, media and the complex image

1998 ◽  
Vol 10 (1-3) ◽  
pp. 100-108 ◽  
Author(s):  
Alicia Colson ◽  
Ross Parry
Keyword(s):  
Image Processing ◽  
Three Dimensional ◽  
Geographical Location ◽  
Spatial Relationship ◽  
Two Dimensional ◽  
Dimensional Approach ◽  
Complex Image ◽  
Cultural Resonance

This article argues that the analysis of a threedimensional image demanded a three-dimensional approach. The authors realise that discussions of images and image processing inveterately conceptualise representation as being flat, static, and finite. The authors recognise the need for a fresh acuteness to three-dimensionality as a meaningful – although problematic – element of visual sources. Two dramatically different examples are used to expose the shortcomings of an ingrained two-dimensional approach and to facilitate a demonstration of how modern (digital) techniques could sanction new historical/anthropological perspectives on subjects that have become all too familiar. Each example could not be more different in their temporal and geographical location, their cultural resonance, and their historiography. However, in both these visual spectacles meaning is polysemic. It is dependent upon the viewer's spatial relationship to the artifice as well as the spirito-intellectual viewer within the community. The authors postulate that the multi- faceted and multi-layered arrangement of meaning in a complex image could be assessed by working beyond the limitations of the two-dimensional methodological paradigm and by using methods and media that accommodated this type of interconnectivity and representation.

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