scholarly journals Presentation a Trust Walker for rating prediction in recommender system with Biased Random Walk: Effects of H-index centrality, similarity in items and friends

2021 ◽  
Vol 104 ◽  
pp. 104325
Author(s):  
Saman Forouzandeh ◽  
Mehrdad Rostami ◽  
Kamal Berahmand
Keyword(s):  
Random Walk ◽  
Recommender System ◽  
H Index ◽  
Biased Random Walk ◽  
Rating Prediction

A Biased Random Walk with Symmetry

1992 ◽  
Vol 65 (5) ◽  
pp. 330
Author(s):  
J. N. Boyd ◽  
P. N. Raychowdhury
Keyword(s):  
Random Walk ◽  
Biased Random Walk

COMPUTATIONAL INVESTIGATION OF THE EFFECTS OF SPHERICAL FILLER MORPHOLOGY AND LOADING ON DIFFUSION TORTUOSITY AND RUBBER PERMEABILITY

2013 ◽  
Vol 86 (2) ◽  
pp. 175-189 ◽  
Author(s):  
T. C. Gruber ◽  
S. D. Crossley ◽  
A. P. Smith
Keyword(s):  
Random Walk ◽  
Rolling Resistance ◽  
Diffusion Path ◽  
Filler Loading ◽  
Pressure Losses ◽  
Biased Random Walk ◽  
Gas Molecule ◽  
Path Lengths ◽  
Path Tortuosity ◽  
Spherical Filler

ABSTRACT Inflation pressure loss in tires degrades performance, raises rolling resistance, and reduces fuel economy. The incorporation of solid fillers, such as carbon black, at relatively high loadings in tire innerliners helps minimize these pressure losses by reducing innerliner permeability due to increases in average gas molecule diffusion path lengths (tortuosity), as well as reductions in diffusion pathway density (capacity). The effects of filler morphology and loading on diffusion path tortuosity can be explored by modeling biased random-walk diffusion through impermeable sphere-filled matrices. Modeled diffusion rate was found to decrease with increased filler loading, reduced filler sphere sizes, increased random-walk step sizes, and the aggregation of filler spheres. Initial correlations with limited empirical permeability measurements are used to validate the model approach.

A Biased Random Walk with Symmetry

1992 ◽  
Vol 65 (5) ◽  
pp. 330-333
Author(s):  
J. N. Boyd ◽  
P. N. Raychowdhury
Keyword(s):  
Random Walk ◽  
Biased Random Walk

scholarly journals Einstein relation for biased random walk on Galton–Watson trees

2013 ◽  
Vol 49 (3) ◽  
pp. 698-721 ◽  
Author(s):  
Gerard Ben Arous ◽  
Yueyun Hu ◽  
Stefano Olla ◽  
Ofer Zeitouni
Keyword(s):  
Random Walk ◽  
Einstein Relation ◽  
Biased Random Walk

Review-Based Recommender Systems: A Proposed Rating Prediction Scheme Using Word Embedding Representation of Reviews

2020 ◽  
Author(s):  
S Hasanzadeh ◽  
S M Fakhrahmad ◽  
M Taheri
Keyword(s):  
Vector Space ◽  
Recommender Systems ◽  
Recommender System ◽  
Word Embedding ◽  
Bag Of Words ◽  
Prediction System ◽  
User Reviews ◽  
Prediction Scheme ◽  
Rating Prediction ◽  
The Given

Abstract Recommender systems nowadays play an important role in providing helpful information for users, especially in ecommerce applications. Many of the proposed models use rating histories of the users in order to predict unknown ratings. Recently, users’ reviews as a valuable source of knowledge have attracted the attention of researchers in this field and a new category denoted as review-based recommender systems has emerged. In this study, we make use of the information included in user reviews as well as available rating scores to develop a review-based rating prediction system. The proposed scheme attempts to handle the uncertainty problem of the rating histories, by fuzzifying the given ratings. Another advantage of the proposed system is the use of a word embedding representation model for textual reviews, instead of using traditional models such as binary bag of words and TFIDF 1 vector space. It also makes use of the helpfulness voting scores, in order to prune data and achieve better results. The effectiveness of the rating prediction scheme as well as the final recommender system was evaluated against the Amazon dataset. Experimental results revealed that the proposed recommender system outperforms its counterparts and can be used as a suitable tool in ecommerce environments.

scholarly journals Effective Exploration Behavior for Chemical-Sensing Robots

Biomimetics ◽  
2019 ◽  
Vol 4 (4) ◽  
pp. 69 ◽  
Author(s):  
Kevin Nickels ◽  
Hoa Nguyen ◽  
Duncan Frasch ◽  
Timothy Davison
Keyword(s):  
Random Walk ◽  
Mobile Robots ◽  
Sensory System ◽  
Chemical Signaling ◽  
Entire Region ◽  
Local Maxima ◽  
E Coli ◽  
Biased Random Walk ◽  
Robotic Exploration ◽  
Exploration Behavior

Mobile robots that can effectively detect chemical effluents could be useful in a variety of situations, such as disaster relief or drug sniffing. Such a robot might mimic biological systems that exhibit chemotaxis, which is movement towards or away from a chemical stimulant in the environment. Some existing robotic exploration algorithms that mimic chemotaxis suffer from the problems of getting stuck in local maxima and becoming “lost”, or unable to find the chemical if there is no initial detection. We introduce the use of the RapidCell algorithm for mobile robots exploring regions with potentially detectable chemical concentrations. The RapidCell algorithm mimics the biology behind the biased random walk of Escherichia coli (E. coli) bacteria more closely than traditional chemotaxis algorithms by simulating the chemical signaling pathways interior to the cell. For comparison, we implemented a classical chemotaxis controller and a controller based on RapidCell, then tested them in a variety of simulated and real environments (using phototaxis as a surrogate for chemotaxis). We also added simple obstacle avoidance behavior to explore how it affects the success of the algorithms. Both simulations and experiments showed that the RapidCell controller more fully explored the entire region of detectable chemical when compared with the classical controller. If there is no detectable chemical present, the RapidCell controller performs random walk in a much wider range, hence increasing the chance of encountering the chemical. We also simulated an environment with triple effluent to show that the RapidCell controller avoided being captured by the first encountered peak, which is a common issue for the classical controller. Our study demonstrates that mimicking the adapting sensory system of E. coli chemotaxis can help mobile robots to efficiently explore the environment while retaining their sensitivity to the chemical gradient.

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