Whole-Chain Recommendations

With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems. In practical recommendation sessions, users will sequentially access multiple scenarios, such as the entrance pages and the item detail pages, and each scenario has its specific characteristics. However, the majority of existing RL-based recommender systems focus on optimizing one strategy for all scenarios or separately optimizing each strategy, which could lead to sub-optimal overall performance. In this paper, we study the recommendation problem with multiple (consecutive) scenarios, i.e., whole-chain recommendations. We propose a multi-agent RL-based approach (DeepChain), which can capture the sequential correlation among different scenarios and jointly optimize multiple recommendation strategies. To be specific, all recommender agents (RAs) share the same memory of users' historical behaviors, and they work collaboratively to maximize the overall reward of a session. Note that optimizing multiple recommendation strategies jointly faces two challenges in the existing model-free RL model - (i) it requires huge amounts of user behavior data, and (ii) the distribution of reward (users' feedback) are extremely unbalanced. In this paper, we introduce model-based RL techniques to reduce the training data requirement and execute more accurate strategy updates. The experimental results based on a real e-commerce platform demonstrate the effectiveness of the proposed framework.Comment: 29th ACM International Conference on Information and Knowledge Managemen

Rhodiola rosea L extract shows protective activity against Alzheimer’s disease in 3xTg-AD mice

Purpose: To investigate the protective effect of Rhodiola rosea L. extract (RRLE) against Alzheimer's disease in 3xTg-AD mice.Methods: The cognitive function of 3xTg-AD mice was assessed using Morris water maze test. The levels of amyloid beta deposits and NeuN in the hippocampus were evaluated by immunohistochemistry. Brain neurotrophic-derived factor (BDNF) and tyrosine kinase B (TrkB) expressions were examined by western blot analysis.Results: RRLE treatment significantly ameliorated learning and memory deficits in AD mice, as shown by increased time spent in the target zone during probe tests. The escape latency in animals treated with 400 mg/kg RRLE (20.5 ± 1.3 s) was significantly increased compared to the untreated mice (12.4 ± 1.3 s, p < 0.01). In addition, RRLE significantly decreased Aβ deposits, increased NeuN-positive cells, and upregulated the expression of BDNF (1.4 ± 0.2, p < 0.05) and TrkB (1.1 ± 0.2, p < 0.05) in the mice.Conclusion: The findings suggest that RRLE treatment may be a useful strategy for treating memory impairment induced by several neurodegenerative diseases.Keywords: Rhodiola rosea L., Alzheimer's disease, Neurodegenerative diseases, Memory impairment, NeuN-positive cells, Amyloid beta deposit

Developing Fairness Rules for Talent Intelligence Management System

Talent management is an important business strategy, but inherently expensive due to the unique, subjective, and developing nature of each talent. Applying artificial intelligence (AI) to analyze large-scale data, talent intelligence management system (TIMS) is intended to address the talent management problems of organizations. While TIMS has greatly improved the efficiency of talent management, especially in the processes of talent selection and matching, high-potential talent discovery and talent turnover prediction, it also brings new challenges. Ethical issues, such as how to maintain fairness when designing and using TIMS, are typical examples. Through the Delphi study in a leading global AI company, this paper proposes eight fairness rules to avoid fairness risks when designing TIMS

Chiral symmetry analysis and rigid rotational invariance for the lattice dynamics of single-wall carbon nanotubes

In this paper, we provide a detailed expression of the vibrational potential for the lattice dynamics of the single-wall carbon nanotubes (SWCNT) satisfying the requirements of the exact rigid translational as well as rotational symmetries, which is a nontrivial generalization of the valence force model for the planar graphene sheet. With the model, the low frequency behavior of the dispersion of the acoustic modes as well as the flexure mode can be precisely calculated. Based upon a comprehensive chiral symmetry analysis, the calculated mode frequencies (including all the Raman and infrared active modes), velocities of acoustic modes and the polarization vectors are systematically fitted in terms of the chiral angle and radius, where the restrictions of various symmetry operations of the SWCNT are fulfilled

Raman and Infra-red properties and layer dependence of the phonon dispersions in multi-layered graphene

The symmetry group analysis is applied to classify the phonon modes of $N$-stacked graphene layers (NSGL's) with AB- and AA-stacking, particularly their infra-red and Raman properties. The dispersions of various phonon modes are calculated in a multi-layer vibrational model, which is generalized from the lattice vibrational potentials of graphene to including the inter-layer interactions in NSGL's. The experimentally reported red shift phenomena in the layer number dependence of the intra-layer optical C-C stretching mode frequencies are interpreted. An interesting low frequency inter-layer optical mode is revealed to be Raman or Infra-red active in even or odd NSGL's respectively. Its frequency shift is sensitive to the layer number and saturated at about 10 layers.Comment: enlarged versio