scholarly journals Recommender System Based on Temporal Models: A Systematic Review

2020 ◽  
Vol 10 (7) ◽  
pp. 2204 ◽  
Author(s):  
Idris Rabiu ◽  
Naomie Salim ◽  
Aminu Da’u ◽  
Akram Osman
Keyword(s):  
Recommender Systems ◽  
Temporal Dynamics ◽  
Concept Drift ◽  
Time Dependent ◽  
Rating Data ◽  
Neighborhood Models ◽  
Temporal Models ◽  
Tensor Models ◽  
Time Aware ◽  
Concept Drifts

Over the years, the recommender systems (RS) have witnessed an increasing growth for its enormous benefits in supporting users’ needs through mapping the available products to users based on their observed interests towards items. In this setting, however, more users, items and rating data are being constantly added to the system, causing several shifts in the underlying relationship between users and items to be recommended, a problem known as concept drift or sometimes called temporal dynamics in RS. Although the traditional techniques of RS have attained significant success in providing recommendations, they are insufficient in providing accurate recommendations due to concept drift problems. These issues have triggered a lot of researches on the development of dynamic recommender systems (DRSs) which is focused on the design of temporal models that will account for concept drifts and ensure more accurate recommendations. However, in spite of the several research efforts on the DRSs, only a few secondary studies were carried out in this field. Therefore, this study aims to provide a systematic literature review (SLR) of the DRSs models that can guide researchers and practitioners to better understand the issues and challenges in the field. To achieve the aim of this study, 87 papers were selected for the review out of 875 total papers retrieved between 2010 and 2019, after carefully applying the inclusion/exclusion and the quality assessment criteria. The results of the study show that concept drift is mostly applied in the multimedia domain, then followed by the e-commerce domain. Also, the results showed that time-dependent neighborhood models are the popularly used temporal models for DRS followed by the Time-dependent Matrix Factorization (TMF) and time-aware factors models, specifically Tensor models, respectively. In terms of evaluation strategy, offline metrics such as precision and recalls are the most commonly used approaches to evaluate the performance of DRS.

scholarly journals Time Dependent Lyotropic Chromonic Textures in Microfluidic Confinements

Crystals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 35 ◽  
Author(s):  
Anshul Sharma ◽  
Irvine Lian Hao Ong ◽  
Anupam Sengupta
Keyword(s):  
Phase Transition ◽  
Aspect Ratio ◽  
Temporal Dynamics ◽  
Initial Conditions ◽  
Flow Behavior ◽  
Medical Diagnostics ◽  
Time Dependent ◽  
Disodium Cromoglycate ◽  
M Phase ◽  
Static Conditions

Nematic and columnar phases of lyotropic chromonic liquid crystals (LCLCs) have been long studied for their fundamental and applied prospects in material science and medical diagnostics. LCLC phases represent different self-assembled states of disc-shaped molecules, held together by noncovalent interactions that lead to highly sensitive concentration and temperature dependent properties. Yet, microscale insights into confined LCLCs, specifically in the context of confinement geometry and surface properties, are lacking. Here, we report the emergence of time dependent textures in static disodium cromoglycate (DSCG) solutions, confined in PDMS-based microfluidic devices. We use a combination of soft lithography, surface characterization, and polarized optical imaging to generate and analyze the confinement-induced LCLC textures and demonstrate that over time, herringbone and spherulite textures emerge due to spontaneous nematic (N) to columnar M-phase transition, propagating from the LCLC-PDMS interface into the LCLC bulk. By varying the confinement geometry, anchoring conditions, and the initial DSCG concentration, we can systematically tune the temporal dynamics of the N- to M-phase transition and textural behavior of the confined LCLC. Overall, the time taken to change from nematic to the characteristic M-phase textures decreased as the confinement aspect ratio (width/depth) increased. For a given aspect ratio, the transition to the M-phase was generally faster in degenerate planar confinements, relative to the transition in homeotropic confinements. Since the static molecular states register the initial conditions for LC flows, the time dependent textures reported here suggest that the surface and confinement effects—even under static conditions—could be central in understanding the flow behavior of LCLCs and the associated transport properties of this versatile material.

Neighborhood Evaluation in Recommender Systems Using the Realization Based Entropy Approach

2014 ◽  
Vol 1 (4) ◽  
pp. 34-50
Author(s):  
Roee Anuar ◽  
Yossi Bukchin ◽  
Oded Maimon ◽  
Lior Rokach
Keyword(s):  
Recommender Systems ◽  
Entropy Measure ◽  
Continuous Data ◽  
System Evaluation ◽  
Rating Data ◽  
New Approach ◽  
Selection Of ◽  
Classical Entropy ◽  
Better Than

The task of a recommender system evaluation has often been addressed in the literature, however there exists no consensus regarding the best metrics to assess its performance. This research deals with collaborative filtering recommendation systems, and proposes a new approach for evaluating the quality of neighbor selection. It theorizes that good recommendations emerge from good selection of neighbors. Hence, measuring the quality of the neighborhood may be used to predict the recommendation success. Since user neighborhoods in recommender systems are often sparse and differ in their rating range, this paper designs a novel measure to asses a neighborhood quality. First it builds the realization based entropy (RBE), which presents the classical entropy measure from a different angle. Next it modifies the RBE and propose the realization based distance entropy (RBDE), which considers also continuous data. Using the RBDE, it finally develops the consent entropy, which takes into account the absence of rating data. The paper compares the proposed approach with common approaches from the literature, using several recommendation evaluation metrics. It presents offline experiments using the Netflix database. The experimental results confirm that consent entropy performs better than commonly used metrics, particularly with high sparsity neighborhoods. This research is supported by The Israel Science Foundation, Grant #1362/10. This research is supported by NHECD EC, Grant #218639.

Detecting Anomalous Ratings in Collaborative Filtering Recommender Systems

2016 ◽  
Vol 8 (2) ◽  
pp. 16-26 ◽  
Author(s):  
Zhihai Yang ◽  
Zhongmin Cai
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Graph Mining ◽  
Scale Up ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Rating Systems ◽  
Rating Data ◽  
Online Rating ◽  
Two Stages

Online rating data is ubiquitous on existing popular E-commerce websites such as Amazon, Yelp etc., which influences deeply the following customer choices about products used by E-businessman. Collaborative filtering recommender systems (CFRSs) play crucial role in rating systems. Since CFRSs are highly vulnerable to “shilling” attacks, it is common occurrence that attackers contaminate the rating systems with malicious rates to achieve their attack intentions. Despite detection methods based on such attacks have received much attention, the problem of detection accuracy remains largely unsolved. Moreover, few can scale up to handle large networks. This paper proposes a fast and effective detection method which combines two stages to find out abnormal users. Firstly, the manuscript employs a graph mining method to spot automatically suspicious nodes in a constructed graph with millions of nodes. And then, this manuscript continue to determine abnormal users by exploiting suspected target items based on the result of first stage. Experiments evaluate the effectiveness of the method.

Two Phase Recommendation Algorithm Based on Clustering User Interest and Trust

2014 ◽  
Vol 543-547 ◽  
pp. 1856-1859
Author(s):  
Xiang Cui ◽  
Gui Sheng Yin
Keyword(s):  
Social Networks ◽  
Electronic Commerce ◽  
Recommender Systems ◽  
Information Overload ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Two Phase ◽  
Rating Data ◽  
User Interests ◽  
The Future

Recommender systems have been proven to be valuable means for Web online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. We need a method to solve such as what items to buy, what music to listen, or what news to read. The diversification of user interests and untruthfulness of rating data are the important problems of recommendation. In this article, we propose to use two phase recommendation based on user interest and trust ratings that have been given by actors to items. In the paper, we deal with the uncertain user interests by clustering firstly. In the algorithm, we compute the between-class entropy of any two clusters and get the stable classes. Secondly, we construct trust based social networks, and work out the trust scoring, in the class. At last, we provide some evaluation of the algorithms and propose the more improve ideas in the future.

scholarly journals Spatio-temporal modelling of the first Chikungunya epidemic in an intra-urban setting: The role of socioeconomic status, environment and temperature

2021 ◽  
Vol 15 (6) ◽  
pp. e0009537
Author(s):  
Laís Picinini Freitas ◽  
Alexandra M. Schmidt ◽  
William Cossich ◽  
Oswaldo Gonçalves Cruz ◽  
Marilia Sá Carvalho
Keyword(s):  
Temperature Effect ◽  
Temporal Dynamics ◽  
Urban Setting ◽  
Time Varying ◽  
Varying Coefficients ◽  
Temporal Models ◽  
Green Areas ◽  
Spatio Temporal ◽  
Time Varying Coefficients

Three key elements are the drivers of Aedes-borne disease: mosquito infestation, virus circulating, and susceptible human population. However, information on these aspects is not easily available in low- and middle-income countries. We analysed data on factors that influence one or more of those elements to study the first chikungunya epidemic in Rio de Janeiro city in 2016. Using spatio-temporal models, under the Bayesian framework, we estimated the association of those factors with chikungunya reported cases by neighbourhood and week. To estimate the minimum temperature effect in a non-linear fashion, we used a transfer function considering an instantaneous effect and propagation of a proportion of such effect to future times. The sociodevelopment index and the proportion of green areas (areas with agriculture, swamps and shoals, tree and shrub cover, and woody-grass cover) were included in the model with time-varying coefficients, allowing us to explore how their associations with the number of cases change throughout the epidemic. There were 13627 chikungunya cases in the study period. The sociodevelopment index presented the strongest association, inversely related to the risk of cases. Such association was more pronounced in the first weeks, indicating that socioeconomically vulnerable neighbourhoods were affected first and hardest by the epidemic. The proportion of green areas effect was null for most weeks. The temperature was directly associated with the risk of chikungunya for most neighbourhoods, with different decaying patterns. The temperature effect persisted longer where the epidemic was concentrated. In such locations, interventions should be designed to be continuous and to work in the long term. We observed that the role of the covariates changes over time. Therefore, time-varying coefficients should be widely incorporated when modelling Aedes-borne diseases. Our model contributed to the understanding of the spatio-temporal dynamics of an urban Aedes-borne disease introduction in a tropical metropolitan city.

scholarly journals Towards Cognitive Recommender Systems

Algorithms ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 176
Author(s):  
Amin Beheshti ◽  
Shahpar Yakhchi ◽  
Salman Mousaeirad ◽  
Seyed Mohssen Ghafari ◽  
Srinivasa Reddy Goluguri ◽  
...  
Keyword(s):  
Recommender Systems ◽  
State Of The Art ◽  
User Preferences ◽  
Data Driven ◽  
New Approach ◽  
Domain Experts ◽  
Adaptive Mechanisms ◽  
New Type ◽  
Cognitive Activities ◽  
Time Aware

Intelligence is the ability to learn from experience and use domain experts’ knowledge to adapt to new situations. In this context, an intelligent Recommender System should be able to learn from domain experts’ knowledge and experience, as it is vital to know the domain that the items will be recommended. Traditionally, Recommender Systems have been recognized as playlist generators for video/music services (e.g., Netflix and Spotify), e-commerce product recommenders (e.g., Amazon and eBay), or social content recommenders (e.g., Facebook and Twitter). However, Recommender Systems in modern enterprises are highly data-/knowledge-driven and may rely on users’ cognitive aspects such as personality, behavior, and attitude. In this paper, we survey and summarize previously published studies on Recommender Systems to help readers understand our method’s contributions to the field in this context. We discuss the current limitations of the state of the art approaches in Recommender Systems and the need for our new approach: A vision and a general framework for a new type of data-driven, knowledge-driven, and cognition-driven Recommender Systems, namely, Cognitive Recommender Systems. Cognitive Recommender Systems will be the new type of intelligent Recommender Systems that understand the user’s preferences, detect changes in user preferences over time, predict user’s unknown favorites, and explore adaptive mechanisms to enable intelligent actions within the compound and changing environments. We present a motivating scenario in banking and argue that existing Recommender Systems: (i) do not use domain experts’ knowledge to adapt to new situations; (ii) may not be able to predict the ratings or preferences a customer would give to a product (e.g., loan, deposit, or trust service); and (iii) do not support data capture and analytics around customers’ cognitive activities and use it to provide intelligent and time-aware recommendations.

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