Learning users' interests by quality classification in market-based recommender systems

Recommender systems are widely used to cope with the problem of information overload and, to date, many recommendation methods have been developed. However, no one technique is best for all users in all situations. To combat this, we have previously developed a market-based recommender system that allows multiple agents (each representing a different recommendation method or system) to compete with one another to present their best recommendations to the user. In our system, the marketplace encourages good recommendations by rewarding the corresponding agents who supplied them according to the users’ ratings of their suggestions. Moreover, we have theoretically shown how our system incentivises the agents to bid in a manner that ensures only the best recommendations are presented. To do this effectively in practice, however, each agent needs to be able to classify its recommendations into different internal quality levels, learn the users’ interests for these different levels, and then adapt its bidding behaviour for the various levels accordingly. To this end, in this paper we develop a reinforcement learning and Boltzmann exploration strategy that the recommending agents can exploit for these tasks. We then demonstrate that this strategy does indeed help the agents to effectively obtain information about the users’ interests which, in turn, speeds up the market convergence and enables the system to rapidly highlight the best recommendations

User evaluation of a market-based recommender system

Recommender systems have been developed for a wide variety of applications (ranging from books, to holidays, to web pages). These systems have used a number of different approaches, since no one technique is best for all users in all situations. Given this, we believe that to be effective, systems should incorporate a wide variety of such techniques and then some form of overarching framework should be put in place to coordinate them so that only the best recommendations (from whatever source) are presented to the user. To this end, in our previous work, we detailed a market-based approach in which various recommender agents competed with one another to present their recommendations to the user. We showed through theoretical analysis and empirical evaluation with simulated users that an appropriately designed marketplace should be able to provide effective coordination. Building on this, we now report on the development of this multi-agent system and its evaluation with real users. Specifically, we show that our system is capable of consistently giving high quality recommendations, that the best recommendations that could be put forward are actually put forward, and that the combination of recommenders performs better than any constituent recommende

Surface Waves Extraction And Their Effect On Effective Material Parameters Of Metamaterials

We investigate the influence of the finite width of the metamaterial slabs. The surface waves excited in finite-width slabs are numerically extracted by the rigorous coupled-wave analysis method. The magnitudes and decay rates of surface waves are analyzed for different materials and geometries. A general homogenization method based on the analysis of appropriately defined macroscopic fields in metamaterial cells is proposed to retrieve the effective material parameters as functions of position. The effects of surface waves on effective material parameters are studied

Learning Users’ Interests in a Market-Based Recommender System

Recommender systems are widely used to cope with the problem of information overload and, consequently, many recommendation methods have been developed. However, no one technique is best for all users in all situations. To combat this, we have previously developed a market-based recommender system that allows multiple agents (each representing a different recommendation method or system) to compete with one another to present their best recommendations to the user. Our marketplace thus coordinates multiple recommender agents and ensures only the best recommendations are presented. To do this effectively, however, each agent needs to learn the users’ interests and adapt its recommending behaviour accordingly. To this end, in this paper, we develop a reinforcement learning and Boltzmann exploration strategy that the recommender agents can use for these tasks. We then demonstrate that this strategy helps the agents to effectively obtain information about the users’ interests which, in turn, speeds up the market convergence and enables the system to rapidly highlight the best recommendations

Relaxing the Cosmological Constraints on Unparticle Dark Component

Unparticle physics has been an active field since the seminal work of Georgi. Recently, many constraints on unparticles from various observations have been considered in the literature. In particular, the cosmological constraints on the unparticle dark component put it in a serious situation. In this work, we try to find a way out of this serious situation, by including the possible interaction between dark energy and the unparticle dark component.Comment: 11 pages, 5 figures, revtex4; v2: discussions added, accepted by Eur. Phys. J. C; v3: published versio

Statefinder diagnostic and stability of modified gravity consistent with holographic and new agegraphic dark energy

Recently one of us derived the action of modified gravity consistent with the holographic and new-agegraphic dark energy. In this paper, we investigate the stability of the Lagrangians of the modified gravity as discussed in [M. R. Setare, Int. J. Mod. Phys. D 17 (2008) 2219; M. R. Setare, Astrophys. Space Sci. 326 (2010) 27]. We also calculate the statefinder parameters which classify our dark energy model.Comment: 12 pages, 2 figures, accepted by Gen. Relativ. Gravi

A Protocol for a Distributed Recommender System

Trusting Agents for Trusting Electronic Societie

Cosmological evolution and statefinder diagnostic for new holographic dark energy model in non flat universe

In this paper, the holographic dark energy model with new infrared cut-off proposed by Granda and Oliveros has been investigated in spatially non flat universe. The dependency of the evolution of equation of state, deceleration parameter and cosmological evolution of Hubble parameter on the parameters of new HDE model are calculated. Also, the statefinder parameters $r$ and $s$ in this model are derived and the evolutionary trajectories in $s-r$ plane are plotted. We show that the evolutionary trajectories are dependent on the model parameters of new HDE model. Eventually, in the light of SNe+BAO+OHD+CMB observational data, we plot the evolutionary trajectories in $s-r$ and $q-r$ planes for best fit values of the parameters of new HDE model.Comment: 11 pages, 5 figures, Accepted by Astrophys. Space Sc

A fucoidan plant drink reduces Helicobacter pylori load in the stomach: a real-world study

BACKGROUND: Helicobacter pylori (Hp) infection is highly prevalent globally and is predominantly managed by antibiotics. Recently, the anti-adhesive, antioxidant, antitoxin, immunomodulatory, anti-coagulant, and anti-infective activities of fucoidan, a polysaccharide extracted from brown seaweeds, have been widely studied, and the results showed promise. Fucoidan has the potential to be utilized in Hp eradication therapy. Our present clinical study was designed to evaluate the efficiency of Lewuyou®, a fucoidan plant drink (FPD) in eradicating Hp in humans. METHODS: This multi-center, clinical study was conducted between October 2020 and July 2021. Hp infection was confirmed by urea breath test (UBT). A total of 122 patients with confirmed Hp infection were enrolled; after exclusion of incomplete data, 85 eligible patients (37 males and 48 females aged 20–81 years) were included in the final analysis. FPD (50 mL per vial) was orally administered twice daily for a 4-week cycle, and 41 patients completed an 8-week cycle. RESULTS: No adverse event (AE) was reported in all 122 participants who had consumed FPD. The Hp eradication rate and clearance rate were 77.6% (66/85) and 20.0% (17/85), respectively, after 4 weeks of FPD consumption and 80.5% (33/41) and 26.8 (11/41) , respectively, after 8 weeks of consumption. CONCLUSIONS: The 4- and 8-week protocols of FPD consumption were safe and effective at reducing Hp load on the gastric mucosa, with Hp eradicated in the majority of participants

Holographic dark energy with time varying c2c^2 parameter

We consider the holographic dark energy model in which the model parameter $c^2$ evolves slowly with time. First we calculate the evolution of EoS parameter as well as the deceleration parameter in this generalized version of holographic dark energy (GHDE). Depending on the parameter $c^2$, the phantom regime can be achieved earlier or later compare with original version of holographic dark energy. The evolution of energy density of GHDE model is investigated in terms of parameter $c^2$. We also show that the time-dependency of $c^2$ can effect on the transition epoch from decelerated phase to accelerated expansion. Finally, we perform the statefinder diagnostic for GHDE model and show that the evolutionary trajectories of the model in $s-r$ plane are strongly depend on the parameter $c^2$.Comment: 16 pages, 4 figures, accepted by Astrophys Space Sc