scholarly journals Bi-Labeled LDA: Inferring Interest Tags for Non-famous Users in Social Network

2019 ◽  
Vol 5 (1) ◽  
pp. 27-47 ◽  
Author(s):  
Jun He ◽  
Hongyan Liu ◽  
Yiqing Zheng ◽  
Shu Tang ◽  
Wei He ◽  
...  
Keyword(s):  
Social Network ◽  
Social Relationship ◽  
Web Search ◽  
Topic Model ◽  
Online Advertising ◽  
User Interest ◽  
Probabilistic Topic Model ◽  
Proposed Model ◽  
Text Information

AbstractUser tags in social network are valuable information for many applications such as Web search, recommender systems and online advertising. Thus, extracting high quality tags to capture user interest has attracted many researchers’ study in recent years. Most previous studies inferred users’ interest based on text posted in social network. In some cases, ordinary users usually only publish a small number of text posts and text information is not related to their interest very much. Compared with famous user, it is more challenging to find non-famous (ordinary) user’s interest. In this paper, we propose a probabilistic topic model, Bi-Labeled LDA, to automatically find interest tags for non-famous users in social network such as Twitter. Instead of extracting tags from text posts, tags of non-famous users are inferred from interest topics of famous users. With the proposed model, the formulation of social relationship between non-famous users and famous user is simulated and interest tags of famous users are exploited to supervise the training of the model and to make use of latent relation among famous users. Furthermore, the influence of popularity of famous user and popular tags are considered, and tags of non-famous users are ranked based on random walk model. Experiments were conducted on Twitter real datasets. Comparison with state-of-the-art methods shows that our method is more superior in terms of both ranking and quality of the tagging results.

Mobile Geographic Web Search Personalization with Language Model

2013 ◽  
Vol 303-306 ◽  
pp. 1420-1425
Author(s):  
Qiang Pu ◽  
Ahmed Lbath ◽  
Da Qing He
Keyword(s):  
Query Expansion ◽  
Web Search ◽  
Mixed Model ◽  
Topic Model ◽  
Language Model ◽  
Mobile Users ◽  
Information Need ◽  
User Interest ◽  
Modeling Framework ◽  
Model Based

Mobile personalized web search has been introduced for the purpose of distinguishing mobile user's personal different search interest. We first take the user's location information into account to do a geographic query expansion, then present an approach to personalizing web search for mobile users within language modeling framework. We estimate a user mixed model estimated according to both activated ontological topic model-based feedback and user interest model to re-rank the results from geographic query expansion. Experiments show that language model based re-ranking method is effective in presenting more relevant documents on the top retrieved results to mobile users. The main contribution of the improvements comes from the consideration of geographic information, ontological topic information and user interests together to find more relevant documents for satisfying their personal information need.

Deconstructing empathy

2002 ◽  
Vol 25 (1) ◽  
pp. 31-32 ◽  
Author(s):  
John N. Constantino
Keyword(s):  
Clinical Research ◽  
Social Relationship ◽  
Proposed Model

Under Preston & de Waal's proposed model, empathy might be regarded as everything that determines the quality of a social relationship. Although the authors provide a useful heuristic for understanding relationships, clinical research efforts with a somewhat narrower focus have provided some additional insights into this topic, which might lead to testable hypotheses regarding the neurobiology of empathy.

Extracting Influential Nodes in Social Networks on Local Weight Aspect

2016 ◽  
Vol 8 (2) ◽  
pp. 21-35
Author(s):  
Jun Jun Cheng ◽  
Yan Chao Zhang ◽  
Xin Zhou ◽  
Hui Cheng
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Time Complexity ◽  
Heuristic Algorithms ◽  
Transmission Model ◽  
User Interest ◽  
Local Weight ◽  
Weight Index ◽  
Influential Nodes

Studies have shown that influential nodes play an important role in all kinds of dynamic behavior in the complex network. Excavation or recognition of such nodes contributes to the development of application areas such as social network advertising and user interest recommendation. Although some heuristic algorithms such as degree, betweenness, closeness and k-shell (or k-core) can identify influential nodes at the same time, they are disadvantaged in terms of accuracy and time complexity. Based on this, the authors propose a novel local weight index to distinguish the node influence based on the theory of ties strength. This index emphasizes that the node influence is jointly decided by the quantity and quality of the neighbors, and its time complexity is much lower than closeness and betweenness. With the aid of SIR information transmission model, this paper verifies the validity of local weight index.

scholarly journals An Engagement Model Based on User Interest and QoS in Video Streaming Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaoying Tan ◽  
Yuchun Guo ◽  
Mehmet A. Orgun ◽  
Liyin Xue ◽  
Yishuai Chen
Keyword(s):  
Large Scale ◽  
Fog Computing ◽  
User Engagement ◽  
Mobile Video ◽  
User Interest ◽  
User Behaviors ◽  
Proposed Model ◽  
Large Scale Dataset ◽  
Qoe Model

With the surging demand on high-quality mobile video services and the unabated development of new network technology, including fog computing, there is a need for a generalized quality of user experience (QoE) model that could provide insight for various network optimization designs. A good QoE, especially when measured as engagement, is an important optimization goal for investors and advertisers. Therefore, many works have focused on understanding how the factors, especially quality of service (QoS) factors, impact user engagement. However, the divergence of user interest is usually ignored or deliberatively decoupled from QoS and/or other objective factors. With an increasing trend towards personalization applications, it is necessary as well as feasible to consider user interest to satisfy aesthetic and personal needs of users when optimizing user engagement. We first propose an Extraction-Inference (E-I) algorithm to estimate the user interest from easily obtained user behaviors. Based on our empirical analysis on a large-scale dataset, we then build a QoS and user Interest based Engagement (QI-E) regression model. Through experiments on our dataset, we demonstrate that the proposed model reaches an improvement in accuracy by 9.99% over the baseline model which only considers QoS factors. The proposed model has potential for designing QoE-oriented scheduling strategies in various network scenarios, especially in the fog computing context.

Design of a Novel Query System for Social Network

2019 ◽  
Vol 12 (2) ◽  
pp. 175-193
Author(s):  
Charu Virmani ◽  
Dimple Juneja ◽  
Anuradha Pillai
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Vital Role ◽  
Agglomerative Clustering ◽  
User Intention ◽  
User Query ◽  
Proposed Model ◽  
Hierarchical Agglomerative Clustering ◽  
Query System

User intention and nature of network plays a vital role towards the quality of response received as the result of any user query. Therefore, the need of system understanding the user's intent and network dynamism as well is highly apparent. The proposed query processing and analysing system (QPAS) for social networks is based on extracting user's intent from various social networks using existing NLP techniques. It fetches the information and further employs hybrid ensemble k-means hierarchical agglomerative clustering (HEKHAC) and modified Bitonic sort to improve the responses. The proposed approach offers an edge over other mechanisms as it not only retrieves more user-centric results as compared to traditional way of keyword-based searching but also in timely manner as well. It is an innovative approach to investigate the new aspects of social network. The proposed model offers a noteworthy revolution scoring up to precision and recall respectively.

scholarly journals A Text-Mining Analysis on the Review of the Non-Financial Reporting Directive: Bringing Value Creation for Stakeholders into Accounting

2021 ◽  
Vol 13 (2) ◽  
pp. 763
Author(s):  
Simona Fiandrino ◽  
Alberto Tonelli
Keyword(s):  
Text Mining ◽  
Financial Reporting ◽  
Value Creation ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Current Debate ◽  
The Core ◽  
Probabilistic Topic Model ◽  
Integrated Logic

The recent Review of the Non-Financial Reporting Directive (NFRD) aims to enhance adequate non-financial information (NFI) disclosure and improve accountability for stakeholders. This study focuses on this regulatory intervention and has a twofold objective: First, it aims to understand the main underlying issues at stake; second, it suggests areas of possible amendment considering the current debates on sustainability accounting and accounting for stakeholders. In keeping with these aims, the research analyzes the documents annexed to the contribution on the Review of the NFRD by conducting a text-mining analysis with latent Dirichlet allocation (LDA) probabilistic topic model (PTM). Our findings highlight four main topics at the core of the current debate: quality of NFI, standardization, materiality, and assurance. The research suggests ways of improving managerial policies to achieve more comparable, relevant, and reliable information by bringing value creation for stakeholders into accounting. It further addresses an integrated logic of accounting for stakeholders that contributes to sustainable development.

SCOPAS — SEMANTIC COMPUTATION OF PAGE SCORE

2013 ◽  
Vol 12 (06) ◽  
pp. 1309-1331
Author(s):  
K. S. KUPPUSAMY ◽  
G. AGHILA
Keyword(s):  
Information Needs ◽  
Web Search ◽  
Web Pages ◽  
Dimensional Approach ◽  
User Interest ◽  
Proposed Model ◽  
Micro Level ◽  
Semantic Computation ◽  
Novel Model ◽  
The Web

This paper presents a novel model for scoring web pages, entitled SCOPAS (Semantic COmputation of PAge Score). With the prolific growth in the number of users of World Wide Web and the heterogeneity of their information needs, it becomes mandatory to evaluate the relevance of a web page in terms of user specific requirements. SCOPAS is aimed at modeling the web pages to facilitate efficient evaluation by harnessing the inherent features of the page in terms of its content and structure. The proposed model further enriches the scoring procedure by fine-graining the evaluation to a micro level through segmentation of the page. A variable magnitude, multi-dimensional approach is proposed for evaluating each of the segments by incorporating the relevance of intra-segment level components. The user-interest is captured with the help of FOAF (Friend Of A Friend) Ontology to achieve personalized page scoring. The generic SCOPAS model is extended to SCOPAS-Rank, which explores utilization of the model in improving the web search engine's result ordering. A prototype implementation of the proposed SCOPAS-Rank model is developed and experiments were conducted on it. The results of the experiments validate the effectiveness of the proposed model.

scholarly journals Regularization of multilingual topic models

2015 ◽  
pp. 26-38
Author(s):  
М.А. Дударенко
Keyword(s):  
Topic Model ◽  
Information Source ◽  
Topic Models ◽  
Bilingual Dictionary ◽  
Bilingual Dictionaries ◽  
Probabilistic Topic Model ◽  
Word Translation ◽  
Cross Lingual ◽  
Cross Language

Предлагается многоязычная вероятностная тематическая модель, одновременно учитывающая двуязычный словарь и связи между документами параллельной или сравнимой коллекции. Для комбинирования этих двух видов информации применяется аддитивная регуляризация тематических моделей (ARTM). Предлагаются два способа использования двуязычного словаря: первый учитывает только сам факт связи между словами--переводами, во втором настраиваются вероятности переводов в каждой теме. Качество многоязычных моделей измеряется на задаче кросс-язычного поиска, когда запросом является документ на одном языке, а поиск производится среди документов другого языка. Показано, что комбинированный учет слов--переводов из двуязычного словаря и связанных документов улучшает качество кросс-язычного поиска по сравнению с моделями, использующими только один тип информации. Сравнение разных методов включения в модель двуязычных словарей показывает, что оценивание вероятностей переводов не только улучшает качество модели, но и позволяет находить тематический контекст для пар слово--перевод. A multilingual probabilistic topic model based on the additive regularization ARTM allowing to combine both a parallel or comparable corpus and a bilingual translation dictionary is proposed. Two approaches to include information from a bilingual dictionary are discussed: the first one takes into account only the fact of connection between word translations, whereas the second one learns the translation probabilities for each topic. To measure the quality of the proposed multilingual topic model, a cross-language search is performed. For each query document in one language, it is found its translation on another language. It is shown that the combined translation of words from a bilingual dictionary and the corresponding connected documents improves the cross-lingual search compared to the models using only one information source. The use of learning word translation probabilities for bilingual dictionaries improves the quality of the model and allows one to determine a context (a set of topics) for each pair of word translations, where these translations are appropriate.

Bi-Term Topic Model for SMS Classification

2017 ◽  
Vol 13 (2) ◽  
pp. 28-40
Author(s):  
Jialin Ma ◽  
Yongjun Zhang ◽  
Lin Zhang ◽  
Kun Yu ◽  
Jinlin Liu
Keyword(s):  
Text Classification ◽  
Short Message Service ◽  
Topic Model ◽  
Classification Algorithms ◽  
Short Message ◽  
Semantic Features ◽  
Spam Filtering ◽  
Proposed Model ◽  
Message Service

With the overflowing of Short Message Service (SMS) spam nowadays, many traditional text classification algorithms are used for SMS spam filtering. Nevertheless, because the content of SMS spam messages are miscellaneous and distinct from general text files, such as more shorter, usually including mass of abbreviations, symbols, variant words and distort or deform sentences, the traditional classifiers aren't fit for the task of SMS spam filtering. In this paper, the authors propose a Short Message Biterm Topic Model (SM-BTM) which can be used to automatically learn latent semantic features from SMS spam corpus for the task of SMS spam filtering. The SM-BTM is based on the probability of topic model theory and Biterm Topic Model (BTM). The experiments in this work show the proposed model SM-BTM can acquire higher quality of topic features than the original BTM, and is more suitable for identifying the miscellaneous SMS spam.

Extraverts Have Larger Social Network Layers

2011 ◽  
Vol 32 (3) ◽  
pp. 161-169 ◽  
Author(s):  
Thomas V. Pollet ◽  
Sam G. B. Roberts ◽  
Robin I. M. Dunbar
Keyword(s):  
Social Network ◽  
Social Relationships ◽  
Outer Layer ◽  
Network Size ◽  
Emotional Intensity ◽  
Emotional Closeness ◽  
Network Layer ◽  
Network Layers ◽  
Social Network Size

Previous studies showed that extraversion influences social network size. However, it is unclear how extraversion affects the size of different layers of the network, and how extraversion relates to the emotional intensity of social relationships. We examined the relationships between extraversion, network size, and emotional closeness for 117 individuals. The results demonstrated that extraverts had larger networks at every layer (support clique, sympathy group, outer layer). The results were robust and were not attributable to potential confounds such as sex, though they were modest in size (raw correlations between extraversion and size of network layer, .20 < r < .23). However, extraverts were not emotionally closer to individuals in their network, even after controlling for network size. These results highlight the importance of considering not just social network size in relation to personality, but also the quality of relationships with network members.

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