LightGBM using Enhanced and De-biased Item Representation for Better Session-based Fashion Recommender Systems

Luo, Jiangwei; Zhao, Wenxuan; Tang, Ye; Zhou, Zhou; Xiong, Huimin; Tao, Zhulin

doi:10.1145/3556702.3556839

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…We chose this machine learning approach as our data is abundant in sample size and tabular. While its application to animal behavioral work is to our knowledge novel, this scenario of structured, dense data is ideal for gradient-boosted decision trees, as this type of method has often been used in recommender systems (Luo et al 2022) as well as economic predictive modeling for human behavior in customer loyalty (Machado, Karray, and Sousa 2019). A machine learning approach is ideal because it can uncover non-linear dependencies in the data without users being required to predetermine interaction effects in their model.…”

Section: Resultsmentioning

confidence: 99%

Gradient boosted decision trees reveal nuances of auditory discrimination behavior

Griffiths,

Lebert,

Sollini

et al. 2023

Preprint

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Perceptual constancy remains an elusive feature to measure in psychophysical animal studies due to the intractability of correlating trial parameters to trial outcomes and response times. Moreover, the nonlinear nature of our data constrains the potential that traditional statistical models have to elucidate the sensory and non-sensory factors that ultimately shape animal performance. In this study, we present an implementation of gradient-boosted regression and decision trees, a machine learning method, to gain insights into animal behavioural data. We implement this computational modelling method on a task in which 5 ferrets were trained to recognize a target word (“instruments”) against a stream of 54 other English non-target word tokens. To test perceptual constance to pitch, we manipulated the mean fundamental frequency (F0) of each trial across the whole series of word tokens (inter-trial F0 roving) or between word tokens (intra-trial F0 roving). We implemented Shapley Additive Explanations and permutation importance for model explainability to determine which factors were significant in our gradient-boosted regression and decision tree model performance. We found that, despite high performance on both intra-trial F0 roved and inter-trial F0 roved task paradigms, F0s that our subjects were introduced to after their initial control F0 period increased the rate of false alarms, and an F0 difference between the target word and the preceding non-target word caused slower reaction times. We also found a subset of non-target distractor words that increased the rate of false alarms and that shared spectrotemporal properties with the target word. Together, our results demonstrate that ferrets, like humans, are capable of showing tolerance to changes in pitch when discriminating complex sounds and that they rely on pitch as a cue to link sounds together over time. Finally, we illustrate the utility of gradient-boosted decision trees to gain insights into behavioural datasets.

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Section: Resultsmentioning

confidence: 99%

Gradient boosted decision trees reveal nuances of auditory discrimination behavior

Griffiths,

Lebert,

Sollini

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Shahbazi et al [ 22 ] proposed an XGBoost-based RS using user-clicked information from an online shopping mall dataset, and verified it with accuracy of 89.6%. Luo et al [ 23 ] proposed a LightGBM-based RS using fashion items, such as color and length, from the Dressipi dataset and verified it with an MRR of 0.206. In contrast to conventional filtering-based RS, AI-based RS can dynamically utilize the data size or features because they use patterns that are not similar between items [ 35 ].…”

Section: Related Workmentioning

confidence: 99%

Real-Time Context-Aware Recommendation System for Tourism

Yoon

Choi

2023

Sensors

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Recently, the tourism trend has been shifting towards the Tourism 2.0 paradigm due to increased travel experiences and the increase in acquiring and sharing information through the Internet. The Tourism 2.0 paradigm requires developing intelligent tourism service tools for positive effects such as time savings and marketing utilization. Existing tourism service tools recommend tourist destinations based on the relationship between tourists and tourist destinations or tourism patterns, so it is difficult to make recommendations in situations where information is insufficient or changes in real time. In this paper, we propose a real-time recommendation system for tourism (R2Tour) that responds to changing situations in real time, such as external factors and distance information, and recommends customized tourist destinations according to the type of tourist. R2Tour trains a machine learning model with situational information such as temperature and precipitation and tourist profiles such as gender and age to recommend the top five nearby tourist destinations. To verify the recommendation performance of R2Tour, six machine learning models, including K-NN and SVM, and information on tourist attractions in Jeju Island were used. As a result of the experiment, R2Tour was verified with accuracy of 77.3%, micro-F1 0.773, and macro-F1 0.415. Since R2Tour trains tourism patterns based on situational information, it is possible to recommend new tourist destinations and respond to changing situations in real time. In the future, R2Tour can be installed in vehicles to recommend nearby tourist destinations or expanded to tasks in the tourism industry, such as a smart target advertising system.

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Building a Multi-Objective Recommender System Using Machine Learning Based on Light Gradient-Boosting Machine

2023

Proceedings of the 2023 6th International Conference on Big Data Technologies

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LightGBM using Enhanced and De-biased Item Representation for Better Session-based Fashion Recommender Systems

Cited by 4 publications

References 8 publications

Gradient boosted decision trees reveal nuances of auditory discrimination behavior

Gradient boosted decision trees reveal nuances of auditory discrimination behavior

Real-Time Context-Aware Recommendation System for Tourism

Building a Multi-Objective Recommender System Using Machine Learning Based on Light Gradient-Boosting Machine

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