9,388 research outputs found
Multimodal Recommender Systems in the Prediction of Disease Comorbidity
While deep-learning based recommender systems utilizing collaborative
filtering have been commonly used for recommendation in other domains, their
application in the medical domain have been limited. In addition to modeling
user-item interactions, we show that deep-learning based recommender systems
can be used to model subject-disease code interactions. Two novel applications
of deep learning-based recommender systems using Neural Collaborative Filtering
(NCF) and Deep Hybrid Filtering (DHF) were utilized for disease diagnosis based
on known past patient comorbidities. Two datasets, one incorporating all
subject-disease code pairs present in the MIMIC-III database, and the other
incorporating the top 50 most commonly occurring diseases, were used for
prediction. Accuracy and Hit Ratio@10 were utilized as metrics to estimate
model performance. The performance of the NCF model making use of the reduced
"top 50" ICD-9 code dataset was found to be lower (accuracy of ~80% and hit
ratio@10 of 35%) as compared to the performance of the NCF model trained on all
ICD-9 codes (accuracy of ~90% and hit ratio@10 of ~80%). Reasons for the
superior performance of the sparser dataset with all ICD codes can be mainly
attributed to the higher volume of data and the robustness of deep-learning
based recommender systems with modeling sparse data. Additionally, results from
the DHF models reflect better performance than the NCF models, with a better
accuracy of 94.4% and hit ratio@10 of 85.36%, reflecting the importance of the
incorporation of clinical note information. Additionally, compared to
literature reports utilizing primarily natural language processing-based
predictions for the task of ICD-9 code co-occurrence, the novel deep
learning-based recommender systems approach performed better. Overall, the deep
learning-based recommender systems have shown promise in predicting disease
comorbidity.Comment: 2022 Fourth International Conference on Transdisciplinary AI
(TransAI
Improved movie recommendations based on a hybrid feature combination method
Recommender systems help users find relevant items efficiently based on their interests and historical interactions with other users. They are beneficial to businesses by promoting the sale of products and to user by reducing the search burden. Recommender systems can be developed by employing different approaches, including collaborative filtering (CF), demographic filtering (DF), content-based filtering (CBF) and knowledge-based filtering (KBF). However, large amounts of data can produce recommendations that are limited in accuracy because of diversity and sparsity issues. In this paper, we propose a novel hybrid method that combines user–user CF with the attributes of DF to indicate the nearest users, and compare four classifiers against each other. This method has been developed through an investigation of ways to reduce the errors in rating predictions based on users’ past interactions, which leads to improved prediction accuracy in all four classification algorithms. We applied a feature combination method that improves the prediction accuracy and to test our approach, we ran an offline evaluation using the 1M MovieLens dataset, well-known evaluation metrics and comparisons between methods with the results validating our proposed method
Trust-Networks in Recommender Systems
Similarity-based recommender systems suffer from significant limitations, such as data sparseness and scalability. The goal of this research is to improve recommender systems by incorporating the social concepts of trust and reputation. By introducing a trust model we can improve the quality and accuracy of the recommended items. Three trust-based recommendation strategies are presented and evaluated against the popular MovieLens [8] dataset
On Recommendation of Learning Objects using Felder-Silverman Learning Style Model
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The e-learning recommender system in learning institutions is increasingly becoming the preferred mode of delivery, as it enables learning anytime, anywhere. However, delivering personalised course learning objects based on learner preferences is still a challenge. Current mainstream recommendation algorithms, such as the Collaborative Filtering (CF) and Content-Based Filtering (CBF), deal with only two types of entities, namely users and items with their ratings. However, these methods do not pay attention to student preferences, such as learning styles, which are especially important for the accuracy of course learning objects prediction or recommendation. Moreover, several recommendation techniques experience cold-start and rating sparsity problems. To address the challenge of improving the quality of recommender systems, in this paper a novel recommender algorithm for machine learning is proposed, which combines students actual rating with their learning styles to recommend Top-N course learning objects (LOs). Various recommendation techniques are considered in an experimental study investigating the best technique to use in predicting student ratings for e-learning recommender systems. We use the Felder-Silverman Learning Styles Model (FSLSM) to represent both the student learning styles and the learning object profiles. The predicted rating has been compared with the actual student rating. This approach has been experimented on 80 students for an online course created in the MOODLE Learning Management System, while the evaluation of the experiments has been performed with the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results of the experiment verify that the proposed approach provides a higher prediction rating and significantly increases the accuracy of the recommendation
Personality in Computational Advertising: A Benchmark
In the last decade, new ways of shopping online have increased the
possibility of buying products and services more easily and faster
than ever. In this new context, personality is a key determinant
in the decision making of the consumer when shopping. A person’s
buying choices are influenced by psychological factors like
impulsiveness; indeed some consumers may be more susceptible
to making impulse purchases than others. Since affective metadata
are more closely related to the user’s experience than generic
parameters, accurate predictions reveal important aspects of user’s
attitudes, social life, including attitude of others and social identity.
This work proposes a highly innovative research that uses a personality
perspective to determine the unique associations among the
consumer’s buying tendency and advert recommendations. In fact,
the lack of a publicly available benchmark for computational advertising
do not allow both the exploration of this intriguing research
direction and the evaluation of recent algorithms. We present the
ADS Dataset, a publicly available benchmark consisting of 300 real
advertisements (i.e., Rich Media Ads, Image Ads, Text Ads) rated
by 120 unacquainted individuals, enriched with Big-Five users’
personality factors and 1,200 personal users’ pictures
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