scholarly journals Learning Contextualized User Preferences for Co‐Adaptive Guidance in Mixed‐Initiative Topic Model Refinement

2021 ◽  
Vol 40 (3) ◽  
pp. 215-226
Author(s):  
F. Sperrle ◽  
H. Schäfer ◽  
D. Keim ◽  
M. El‐Assady
Keyword(s):  
Topic Model ◽  
User Preferences ◽  
Model Refinement ◽  
Adaptive Guidance ◽  
Mixed Initiative

scholarly journals Semantic Concept Spaces: Guided Topic Model Refinement using Word-Embedding Projections

2019 ◽  
pp. 1-1 ◽  
Author(s):  
Mennatallah El-Assady ◽  
Rebecca Kehlbeck ◽  
Christopher Collins ◽  
Daniel Keim ◽  
Oliver Deussen
Keyword(s):  
Topic Model ◽  
Word Embedding ◽  
Semantic Concept ◽  
Model Refinement

A Recommendation System for Scientific Papers through Bayesian Nonparametric Hybrid Filtering

2014 ◽  
pp. 20-41
Author(s):  
Abel Rodriguez ◽  
Radhakrishna Vuppala
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Topic Model ◽  
User Preferences ◽  
Point Estimation ◽  
Relational Model ◽  
Bayesian Nonparametric ◽  
Homogeneous Groups ◽  
Scientific Papers

Recommender systems have become an important area of research with numerous applications on e-commerce. This chapter introduces a joint statistical model for user preferences and item features that can serve as the basis for a recommendation about recently published scientific papers. The model is constructed using ideas from the literature on Bayesian nonparametric mixture modeling. More specifically, user preferences are modeled using an Infinite Relational Model (IRM) in which both users and items are independently partitioned into homogeneous groups, while item features are modeled using a topic model, which also partitions items into groups with homogenous features. Information is shared across both components of the model through a common partition of items. Hence, the model is a hybrid system that combines ideas from collaborative and content-based filtering. The chapter discusses three different computational strategies, including a Markov chain Monte Carlo algorithm for full posterior inference, an iterated conditional maximization algorithm, and a mean-field variational algorithm for point estimation and prediction in large datasets where Markov chain Monte Carlo approaches might not be practical. The model is illustrated through simulation studies and by analyzing data from CiteULike.

A domain knowledge incorporated text mining approach for capturing user needs on BIM applications

2019 ◽  
Vol 27 (2) ◽  
pp. 458-482 ◽  
Author(s):  
Shenghua Zhou ◽  
S. Thomas Ng ◽  
Sang Hoon Lee ◽  
Frank J. Xu ◽  
Yifan Yang
Keyword(s):  
Text Mining ◽  
Sentiment Analysis ◽  
Domain Knowledge ◽  
Topic Model ◽  
User Preferences ◽  
User Needs ◽  
Synthetic Approach ◽  
User Interest ◽  
Content Type ◽  
Aec Industry

Purpose In the architecture, engineering and construction (AEC) industry, technology developers have difficulties in fully understanding user needs due to the high domain knowledge threshold and the lack of effective and efficient methods to minimise information asymmetry between technology developers and AEC users. The paper aims to discuss this issue. Design/methodology/approach A synthetic approach combining domain knowledge and text mining techniques is proposed to help capture user needs, which is demonstrated using building information modelling (BIM) apps as a case. The synthetic approach includes the: collection and cleansing of BIM apps’ attribute data and users’ comments; incorporation of domain knowledge into the collected comments; performance of a sentiment analysis to distinguish positive and negative comments; exploration of the relationships between user sentiments and BIM apps’ attributes to unveil user preferences; and establishment of a topic model to identify problems frequently raised by users. Findings The results show that those BIM app categories with high user interest but low sentiments or supplies, such as “reality capture”, “interoperability” and “structural simulation and analysis”, should deserve greater efforts and attention from developers. BIM apps with continual updates and of small size are more preferred by users. Problems related to the “support for new Revit”, “import & export” and “external linkage” are most frequently complained by users. Originality/value The main contributions of this work include: the innovative application of text mining techniques to identify user needs to drive BIM apps development; and the development of a synthetic approach to orchestrating domain knowledge, text mining techniques (i.e. sentiment analysis and topic modelling) and statistical methods in order to help extract user needs for promoting the success of emerging technologies in the AEC industry.

scholarly journals A^3NCF: An Adaptive Aspect Attention Model for Rating Prediction

2018 ◽  
Author(s):  
Zhiyong Cheng ◽  
Ying Ding ◽  
Xiangnan He ◽  
Lei Zhu ◽  
Xuemeng Song ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Large Scale ◽  
Topic Model ◽  
State Of The Art ◽  
Representation Learning ◽  
Weight Vector ◽  
User Preferences ◽  
Attention Model ◽  
Item Characteristics ◽  
Rating Prediction

Current recommender systems consider the various aspects of items for making accurate recommendations. Different users place different importance to these aspects which can be thought of as a preference/attention weight vector. Most existing recommender systems assume that for an individual, this vector is the same for all items. However, this assumption is often invalid, especially when considering a user's interactions with items of diverse characteristics. To tackle this problem, in this paper, we develop a novel aspect-aware recommender model named A$^3$NCF, which can capture the varying aspect attentions that a user pays to different items. Specifically, we design a new topic model to extract user preferences and item characteristics from review texts. They are then used to 1) guide the representation learning of users and items, and 2) capture a user's special attention on each aspect of the targeted item with an attention network. Through extensive experiments on several large-scale datasets, we demonstrate that our model outperforms the state-of-the-art review-aware recommender systems in the rating prediction task.

A Study on Bestseller Short Text Semantics Analysis Using Topic Model

2018 ◽  
Vol 15 ◽  
pp. 101-112
Author(s):  
So-Hyun Park ◽  
Ae-Rin Song ◽  
Young-Ho Park ◽  
Sun-Young Ihm
Keyword(s):  
Topic Model ◽  
Short Text

Investigating user preferences in night bus lines operation using Conjoint Analysis in the city of Thessaloniki, Greece

2020 ◽  
Author(s):  
Dimitrios Nalmpantis ◽  
Dimitra Giannaka ◽  
Stavros Malliaris ◽  
Evangelos Genitsaris ◽  
Ioannis Karagiotas ◽  
...  
Keyword(s):  
Conjoint Analysis ◽  
User Preferences ◽  
The City

Improved Sampling Strategies for Protein Model Refinement based on Molecular Dynamics Simulation

2020 ◽  
Author(s):  
Lim Heo ◽  
Collin Arbour ◽  
Michael Feig
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Structure Prediction ◽  
Protein Structures ◽  
Conformational Space ◽  
Dynamics Simulation ◽  
Model Refinement ◽  
Protein Model ◽  
Lower Accuracy ◽  
Simulation Based

Protein structures provide valuable information for understanding biological processes. Protein structures can be determined by experimental methods such as X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, or cryogenic electron microscopy. As an alternative, in silico methods can be used to predict protein structures. Those methods utilize protein structure databases for structure prediction via template-based modeling or for training machine-learning models to generate predictions. Structure prediction for proteins distant from proteins with known structures often results in lower accuracy with respect to the true physiological structures. Physics-based protein model refinement methods can be applied to improve model accuracy in the predicted models. Refinement methods rely on conformational sampling around the predicted structures, and if structures closer to the native states are sampled, improvements in the model quality become possible. Molecular dynamics simulations have been especially successful for improving model qualities but although consistent refinement can be achieved, the improvements in model qualities are still moderate. To extend the refinement performance of a simulation-based protocol, we explored new schemes that focus on an optimized use of biasing functions and the application of increased simulation temperatures. In addition, we tested the use of alternative initial models so that the simulations can explore conformational space more broadly. Based on the insight of this analysis we are proposing a new refinement protocol that significantly outperformed previous state-of-the-art molecular dynamics simulation-based protocols in the benchmark tests described here. <br>

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