Social tags are an important information source in Web 2.0.They can be used to describe users' topic preferences as well the content of items make personalized recommendations.However, since arbitrary words given by users, they contain a lot noise such tag synonyms, semantic ambiguities and personal tags.Such brings difficulties improve accuracy item recommendations.To eliminate tags, this paper we propose use multiple relationships among find meaning each for user individually.With proposed...

Sentiment Analysis can help to extract knowledge related opinions and emotions from user generated text information. It be applied in medical field for patients monitoring purposes. With the availability of large datasets, deep learning algorithms have become a state art also sentiment analysis. However, models drawback not being non human-interpretable, raising various problems model's interpretability. Very few work been proposed build that explain their decision making process actions. In...

Sentiment analysis is one of the key tasks natural language understanding. Evolution models dynamics sentiment orientation over time. It can help people have a more profound and deep understanding opinion implied in user generated content. Existing work mainly focuses on classification, while how topic has been influenced by other topics or dynamic interaction from aspect ignored. In this paper, we propose to construct Gaussian Process Dynamic Bayesian Network model interactions social media...

Abstract Background Redox signaling caused by knockdown (KD) of Glutathione Peroxidase 2 (GPx2) in the PyMT mammary tumour model promotes metastasis via phenotypic and metabolic reprogramming. However, cell subpopulations transcriptional regulators governing these processes remained unknown. Methods We used single-cell transcriptomics to decipher stimulated GPx2 KD paired pulmonary metastases. analyzed EMT spectrum across various clusters using pseudotime trajectory analysis elucidated...

Topic recommendation can help users deal with the information overload issue in micro-blogging communities. This paper proposes to use implicit network formed by multiple relationships among users, topics and micro-blogs, temporal of micro-blogs find semantically temporally relevant each topic, profile users' time-drifting topic interests. The Content based, Nearest Neighborhood based Matrix Factorization models are used make personalized recommendations. effectiveness proposed approaches is...

In this paper, we propose a semantic-aware blocking framework for entity resolution (ER). The proposed is built using locality-sensitive hashing (LSH) techniques, which efficiently unifies both textual and semantic features into an ER process. order to understand how similarity metrics may affect the effectiveness of blocking, study robustness their properties in terms LSH families. Then, present records can be captured, measured, integrated with techniques over multiple spaces. doing so,...

The clinical diagnosis and treatment of motor dysarthria in post-stroke patients is often subjective neglects the impact psychological emotional disorders on disease progression. This study aims to analyze correlation among expression, state, facial severity dedicated construction a prediction model. We first designed THE-POSSD, novel Chinese multimodal pathology expression database, which collected acoustic, glottal, data under stimuli from at different stages healthy controls. Emotional...

Objective This study is part of a series initiatives at UK university designed to cultivate deep understanding students' perspectives on analytics that resonate with their unique learning needs. It explores collaborative data processing undertaken by postgraduate students who examined an Open University Learning Analytics Dataset (OULAD). Methods A qualitative approach was adopted, integrating Retrieval-Augmented Generation (RAG) and Large Language Model (LLM) technique human-led content...

This paper describes our sentiment classification system for microblog-sized documents, and documents where a topic is present. The consists of softvoting ensemble word2vec language model adapted to classification, convolutional neural network (CNN), longshort term memory (LSTM). Our main contribution way introduce information into this model, by concatenating embedding, consisting the averaged word embedding that topic, each vector in networks. When we apply models SemEval 2016 Task 4...

Recommender systems (RSs) are designed to provide personalized recommendations users. Recently, knowledge graphs (KGs) have been widely introduced in RSs improve recommendation accuracy. In this study, however, we demonstrate that do not necessarily perform worse even if the KG is downgraded user-item interaction graph only (or removed). We propose an evaluation framework KG4RecEval systematically evaluate how much a contributes accuracy of KG-based RS, using our defined metric KGER (...

Recommender Systems is one of the effective tools to deal with information overload issue. Similar explicit rating and other implicit behaviors such as purchase behavior, click streams, browsing history etc., tagging implies userpsilas important personal interests preferences information, which can be used recommend personalized items users. This paper explore how utilize do recommendations. Based on distinctive three dimensional relationships among users, tags items, a new user profiling...

User profiling is a key component of personalized recommender systems, and used to generate user profiles that describe individual interests preferences. The increasing availability big data driving the urgent need for algorithms are able accurate from large-scale behavior data. In this paper, we propose probabilistic rating auto-encoder perform unsupervised feature learning latent Based on generated profiles, neighbourhood based collaborative filtering approaches have been adopted make...

Real-time Entity Resolution (ER) is the process of matching query records in subsecond time with a database that represent same real-world entity. Indexing techniques are generally used to efficiently extract set candidate from similar record, and be compared record more detail. The sorted neighborhood indexing method, which sorts compares within sliding window, has been successfully for ER large static databases. However, because it based on arrays designed batch resolves all rather than...

Recommender systems are popular for personalization in online communities. Users, items, and other affiliated information such as tags, item genres, user friends of an community form a heterogenous network. User profiling is the foundation personalized recommender systems. It provides basis to discover knowledge about individual user’s interests items. Typically, users profiled with their direct explicit or implicit ratings, which ignored inter-connections among users, entity nodes This...

The social tags in web 2.0 are becoming another important information source to profile users' interests and preferences make personalized recommendations. To solve the problem of low sharing caused by free-style vocabulary long tails distribution items, this paper proposes an approach integrate given users item taxonomy with standard hierarchical structure provided experts experimental results show that proposed can effectively improve recommendation accuracy.

Item folksonomy or tag information is popularly available on the web now. However, since tags are arbitrary words given by users, they contain a lot of noise such as synonyms, semantic ambiguities and personal tags. Such brings difficulties to improve accuracy item recommendations. In this paper, we propose combine taxonomy reduce make personalized The experiments conducted dataset collected from Amazon.com demonstrated effectiveness proposed approaches. results suggested that recommendation...

The Large scaled emerging user created information in web 2.0 such as tags, reviews, comments and blogs can be used to profile users' interests preferences make personalized recommendations. To solve the scalability problem of current profiling recommender systems, this paper proposes a parallel approach scalable system. advanced cloud computing techniques including Hadoop, MapReduce Cascading are employed implement proposed approaches. experiments were conducted on Amazon EC2 Elastic S3...