arXiv subjects

Alexey Vasilev

Publications and source records attributed to Alexey Vasilev.

22 records · Page 2Linked to original sources

arXiv2024

Low-Threshold Surface-Emitting Whispering-Gallery Mode Microlasers

We report on microlasers based on high-quality micropillars with lasing on whispering-gallery modes. Usage of low-absorbing Al$\scriptsize 0.2$Ga$\scriptsize 0.8$As\Al$\scriptsize 0.9$Ga$\scriptsize 0.1$As distributed Bragg reflectors as well as the smooth pillar sidewalls allows us to realize whispering-gallery modes lasing by excitation and collection of emission in the pillar axis direction. As a result, simultaneous whispering gallery modes lasing (comb-like structure) in the wavelength range of 930-970 nm is observed for 3-7 $μ$m pillar diameters. Increase the temperature up to 130 K results single-mode lasing for 5 $μ$m pillars with about 8000 cold cavity quality-factor and 240 $μ$W estimated threshold excitation power.

cond-mat.mes-hall↗

arXiv2023

Turning Dross Into Gold Loss: is BERT4Rec really better than SASRec?

Recently sequential recommendations and next-item prediction task has become increasingly popular in the field of recommender systems. Currently, two state-of-the-art baselines are Transformer-based models SASRec and BERT4Rec. Over the past few years, there have been quite a few publications comparing these two algorithms and proposing new state-of-the-art models. In most of the publications, BERT4Rec achieves better performance than SASRec. But BERT4Rec uses cross-entropy over softmax for all items, while SASRec uses negative sampling and calculates binary cross-entropy loss for one positive and one negative item. In our work, we show that if both models are trained with the same loss, which is used by BERT4Rec, then SASRec will significantly outperform BERT4Rec both in terms of quality and training speed. In addition, we show that SASRec could be effectively trained with negative sampling and still outperform BERT4Rec, but the number of negative examples should be much larger than one.

cs.IR↗

arXiv2022

The Long Tail of Context: Does it Exist and Matter?

Context has been an important topic in recommender systems over the past two decades. A standard representational approach to context assumes that contextual variables and their structures are known in an application. Most of the prior CARS papers following representational approach manually selected and considered only a few crucial contextual variables in an application, such as time, location, and company of a person. This prior work demonstrated significant recommendation performance improvements when various CARS-based methods have been deployed in numerous applications. However, some recommender systems applications deal with a much bigger and broader types of contexts, and manually identifying and capturing a few contextual variables is not sufficient in such cases. In this paper, we study such ``context-rich'' applications dealing with a large variety of different types of contexts. We demonstrate that supporting only a few most important contextual variables, although useful, is not sufficient. In our study, we focus on the application that recommends various banking products to commercial customers within the context of dialogues initiated by customer service representatives. In this application, we managed to identify over two hundred types of contextual variables. Sorting those variables by their importance forms the Long Tail of Context (LTC). In this paper, we empirically demonstrate that LTC matters and using all these contextual variables from the Long Tail leads to significant improvements in recommendation performance.

cs.IR↗

arXiv2022

Quality Metrics in Recommender Systems: Do We Calculate Metrics Consistently?

Offline evaluation is a popular approach to determine the best algorithm in terms of the chosen quality metric. However, if the chosen metric calculates something unexpected, this miscommunication can lead to poor decisions and wrong conclusions. In this paper, we thoroughly investigate quality metrics used for recommender systems evaluation. We look at the practical aspect of implementations found in modern RecSys libraries and at the theoretical aspect of definitions in academic papers. We find that Precision is the only metric universally understood among papers and libraries, while other metrics may have different interpretations. Metrics implemented in different libraries sometimes have the same name but measure different things, which leads to different results given the same input. When defining metrics in an academic paper, authors sometimes omit explicit formulations or give references that do not contain explanations either. In 47% of cases, we cannot easily know how the metric is defined because the definition is not clear or absent. These findings highlight yet another difficulty in recommender system evaluation and call for a more detailed description of evaluation protocols.

cs.IR↗