Factorizing LambdaMART for cold start recommendations

Recommendation systems often rely on point-wise loss metrics such as the mean squared error. However, in real recommendation settings only few items are presented to a user. This observation has recently encouraged the use of rank-based metrics. LambdaMART is the state-of-the-art algorithm in learning to rank which relies on such a metric. Despite its success it does not have a principled regularization mechanism relying in empirical approaches to control model complexity leaving it thus prone to overfitting. Motivated by the fact that very often the users' and items' descriptions as well as the preference behavior can be well summarized by a small number of hidden factors, we propose a novel algorithm, LambdaMART Matrix Factorization (LambdaMART-MF), that learns a low rank latent representation of users and items using gradient boosted trees. The algorithm factorizes lambdaMART by defining relevance scores as the inner product of the learned representations of the users and items. The low rank is essentially a model complexity controller; on top of it we propose additional regularizers to constraint the learned latent representations that reflect the user and item manifolds as these are defined by their original feature based descriptors and the preference behavior. Finally we also propose to use a weighted variant of NDCG to reduce the penalty for similar items with large rating discrepancy. We experiment on two very different recommendation datasets, meta-mining and movies-users, and evaluate the performance of LambdaMART-MF, with and without regularization, in the cold start setting as well as in the simpler matrix completion setting. In both cases it outperforms in a significant manner current state of the art algorithms

Acid/base-triggered switching of circularly polarized luminescence and electronic circular dichroism in organic and organometallic helicenes.

Electronic circular dichroism and circularly polarized luminescence acid/base switching activity has been demonstrated in helicene-bipyridine proligand 1 a and in its “rollover” cycloplatinated derivative 2 a. Whereas proligand 1 a displays a strong bathochromic shift (>160 nm) of the nonpolarized and circularly polarized luminescence upon protonation, complex 2 a displays slightly stronger emission. This strikingly different behavior between singlet emission in the organic helicene and triplet emission in the organometallic derivative has been rationalized by using quantum-chemical calculations. The very large bathochromic shift of the emission observed upon protonation of azahelicene-bipyridine 1 a has been attributed to the decrease in aromaticity (promoting a charge-transfer-type transition rather than a π–π* transition) as well as an increase in the HOMO–LUMO character of the transition and stabilization of the LUMO level upon protonation

Supplementary data for the article: Jevtić, I. I.; Savić Vujović, K.; Srebro, D.; Vučković, S.; Ivanović, M. D.; Kostić-Rajačić, S. V. Synthesis and Pharmacological Evaluation of Novel Cis and Trans 3-Substituted Anilidopiperidines. Pharmacol. Rep 2020, 72 (4), 1069–1075. https://doi.org/10.1007/s43440-020-00121-2

Supplementary material for: [ https://doi.org/10.1007/s43440-020-00121-2]Related to published version: [https://cherry.chem.bg.ac.rs/handle/123456789/4211

Fast Differentially Private Matrix Factorization

Differentially private collaborative filtering is a challenging task, both in terms of accuracy and speed. We present a simple algorithm that is provably differentially private, while offering good performance, using a novel connection of differential privacy to Bayesian posterior sampling via Stochastic Gradient Langevin Dynamics. Due to its simplicity the algorithm lends itself to efficient implementation. By careful systems design and by exploiting the power law behavior of the data to maximize CPU cache bandwidth we are able to generate 1024 dimensional models at a rate of 8.5 million recommendations per second on a single PC

μ-opioid/D2 dopamine receptor pharmacophore containing ligands: Synthesis and pharmacological evaluation

Herein, the synthesis and pharmacological evaluation of 13 novel compounds, designed as potential heterobivalent ligands for μ-opioid receptor (MOR) and dopamine D2 receptors (D2DAR), are reported. The compounds consisted of anilido piperidine and N-aryl piperazine moieties, joined by a variable-length methylene linker. The two moieties represent MOR and D2DAR pharmacophores, respectively. The synthesis encompassed four steps, securing the final products in 28–42 % overall yields. The approach has a considerable synthetic potential, providing access to various related structures. Pharmacological tests involved in vitro competitive assay for D2DAR using [3H] spiperon, as a standard radioligand, and in vivo antinociceptive tests for MOR. The measured dopamine affinities were modest to low, while antinociceptive activity was completely absent. Therefore, the compounds of the general structure prepared in this research are unlikely to be useful as opioid–dopamine receptor heterobivalent ligands

Scalable Tensor Factorizations for Incomplete Data

The problem of incomplete data - i.e., data with missing or unknown values - in multi-way arrays is ubiquitous in biomedical signal processing, network traffic analysis, bibliometrics, social network analysis, chemometrics, computer vision, communication networks, etc. We consider the problem of how to factorize data sets with missing values with the goal of capturing the underlying latent structure of the data and possibly reconstructing missing values (i.e., tensor completion). We focus on one of the most well-known tensor factorizations that captures multi-linear structure, CANDECOMP/PARAFAC (CP). In the presence of missing data, CP can be formulated as a weighted least squares problem that models only the known entries. We develop an algorithm called CP-WOPT (CP Weighted OPTimization) that uses a first-order optimization approach to solve the weighted least squares problem. Based on extensive numerical experiments, our algorithm is shown to successfully factorize tensors with noise and up to 99% missing data. A unique aspect of our approach is that it scales to sparse large-scale data, e.g., 1000 x 1000 x 1000 with five million known entries (0.5% dense). We further demonstrate the usefulness of CP-WOPT on two real-world applications: a novel EEG (electroencephalogram) application where missing data is frequently encountered due to disconnections of electrodes and the problem of modeling computer network traffic where data may be absent due to the expense of the data collection process

Acid/base-triggered switching of circularly polarized luminescence and electronic circular dichroism in organic and organometallic helicenes

Electronic circular dichroism and circularly polarized luminescence acid/base switching activity has been demonstrated in helicene-bipyridine proligand 1 a and in its “rollover” cycloplatinated derivative 2 a. Whereas proligand 1 a displays a strong bathochromic shift (>160 nm) of the nonpolarized and circularly polarized luminescence upon protonation, complex 2 a displays slightly stronger emission. This strikingly different behavior between singlet emission in the organic helicene and triplet emission in the organometallic derivative has been rationalized by using quantum-chemical calculations. The very large bathochromic shift of the emission observed upon protonation of azahelicene-bipyridine 1 a has been attributed to the decrease in aromaticity (promoting a charge-transfer-type transition rather than a π–π* transition) as well as an increase in the HOMO–LUMO character of the transition and stabilization of the LUMO level upon protonation

Thermal Resonance in Signal Transmission

We use temperature tuning to control signal propagation in simple one-dimensional arrays of masses connected by hard anharmonic springs and with no local potentials. In our numerical model a sustained signal is applied at one site of a chain immersed in a thermal environment and the signal-to-noise ratio is measured at each oscillator. We show that raising the temperature can lead to enhanced signal propagation along the chain, resulting in thermal resonance effects akin to the resonance observed in arrays of bistable systems.Comment: To appear in Phys. Rev.