A scalable and robust trust-based nonnegative matrix factorization recommender using the alternating direction method

Parvin, Hashem; Moradi, Parham; Esmaeili, Shahrokh; Qader, Nooruldeen Nasih

doi:10.1016/j.knosys.2018.12.016

Cited by 55 publications

(15 citation statements)

References 38 publications

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“…For ex-ample, a user relationship graph-based information between users called "trust" can be used together. Previously, there have been many studies that have used trust together make more personalized and accurate recommendations [24]- [27]. However, these studies did not consider temporal properties such as sequential information and temporal interval.…”

Section: Discussionmentioning

confidence: 99%

Recommendation System With Hierarchical Recurrent Neural Network for Long-Term Time Series

2021

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Nowadays, recommendation systems are widely used on various services. This system predicts what item a user will use next using large amounts of stored user history. Recommendation systems are commonly applied in various fields as movies, e-commerce, and social services. However, previous researches on recommendation systems commonly overlooked the importance of item usage sequence and time intervals of the time series data from users. We provide a novel recommendation system that incorporates these temporal properties of the user history. We design a recurrent neural network (RNN) model with a hierarchical structure so that the sequence and time intervals of the user's item usage history can be considered. The model is divided into two layers: a layer for a long time and a layer for a short time. We conduct experiments on real-world data such as Movielens and Steam datasets, which has a longtime range, and show that our new model outperforms the previous widely used recommendation methods, including RNN-based models. We also conduct experiments to find out the influence of the length and time interval of sequences in our model. These experimental results show that both sequence length and time interval are influential, indicating that it is important to consider the temporal properties for long-term sequences.

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Section: Discussionmentioning

confidence: 99%

Recommendation System With Hierarchical Recurrent Neural Network for Long-Term Time Series

2021

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“…To tackle the data sparsity problem and the cold-start problem, in [24], the authors propose a probabilistic matrix factorization framework for an online recommendation. The authors in [25], present a non-negative variant of matrix factorization that integrates social trust information in a model that addresses data sparsity and cold start issues. With a similar trend, the authors in [26] integrate social information into their recommender system based on a matrix factorization method.…”

Section: B Model-based Methodsmentioning

confidence: 99%

A Bayesian Inference Based Hybrid Recommender System

2020

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The large mass of various products/services accessible on the Internet has motivated the development of recommender systems to refine the selection of items aligned with users' expectations. Recommender systems have been developed to tackle the item targeting problem. They are crucial tools that quickly target items fitting users' needs, thus allowing them to easily identify the items that fit their tastes and preferences. Following state-of-the-art methods, a distinction is made between content-based recommender approaches and collaborative filtering-based recommender approaches. Collaborative filtering-based recommender approaches are the most widely adopted methods. They are divided into memory-based methods that show the advantage of their easy-understandability, and model-based methods that are data sparsity resilient and high-accurate. In this paper, we propose a hybrid model-based recommendation approach, a combination of a user-based approach and an item-based approach. Our method estimates the probability with which a user would rate an item. It performs a Bayesian inference of future end-user interests and shows the advantage of the easy-understandability of memory-based methods and the effectiveness of modelbased methods. Experiments are conducted on real-world datasets and show that our method outperforms several state-of-the-art recommendation methods regarding the prediction accuracy and the recommendation quality.

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“…While CF employs algorithms and models based on users' previous behavior and the behavior of their neighbors who share similar preferences, the core of CB is to identify users' preferences and determine a cluster of objects with similar properties (Alyari and Navimipour, 2018). However, this research has evident limitations, such as the scalability problem, cold-start problem and selection problem (Gondaliya and Amin, 2019;Konstan and Riedl, 2012;Park et al, 2012;Parvin et al, 2019;Shani and Gunawardana, 2013). For example, the cold-start problem means that the systems cannot provide sufficient information for new users, which is a common issue in almost all recommendation systems (Liu et al, 2014).…”

Section: Literature Reviewmentioning

confidence: 99%

The quality of user experiences for mobile recommendation systems: an end-user perspective

Chong¹,

2021

IMDS

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PurposeThis paper attempts to identify key factors (i.e., personalization, privacy awareness and social norms) that affect user experiences (UXs) of mobile recommendation systems according to the user involvement theory (push-based and pull-based) and their relationships.Design/methodology/approachThe study is based on an online survey with students from an international business school located in southwestern China. The sample population for the study included randomly selected 600 university students who are active mobile phone users. A total of 470 questionnaires were returned; 456 were valid (14 were invalid due to the incompleteness of their responses), providing a response rate of 65%.FindingsSocial norms have the largest impact on user experience quality, followed by personalization and privacy awareness. User involvement in mobile recommendation systems has mediating effects on the above relationships, with larger effects on pull-based systems than on push-based systems.Originality/valueThis study provides an integrated framework for researchers to measure the effects of social, personal and risk factors on the quality of user experience. The results enrich the literature on user involvement, mobile recommendation systems and UX. The findings provide significant implications for both retailers and developers of mobile recommendation systems.

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A scalable and robust trust-based nonnegative matrix factorization recommender using the alternating direction method

Cited by 55 publications

References 38 publications

Recommendation System With Hierarchical Recurrent Neural Network for Long-Term Time Series

Recommendation System With Hierarchical Recurrent Neural Network for Long-Term Time Series

A Bayesian Inference Based Hybrid Recommender System

The quality of user experiences for mobile recommendation systems: an end-user perspective

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