User context information prediction based on the mobile internet social pictures

2016 ◽  
Author(s):  
Yilei Wang ◽  
Huaigui Ren ◽  
Zhen Qin ◽  
Wentao Zheng ◽  
Linfang Yu ◽  
...  
Keyword(s):  
Mobile Internet ◽  
Context Information ◽  
User Context

An Electronic Commerce Collaborative Filtering Recommedation Algorithm Based on User Context

2014 ◽  
Vol 687-691 ◽  
pp. 1488-1491
Author(s):  
Peng Fang
Keyword(s):  
Electronic Commerce ◽  
Collaborative Filtering ◽  
Prediction Accuracy ◽  
Information Resources ◽  
Personalized Recommendation ◽  
Context Information ◽  
Recommendation Algorithm ◽  
User Context ◽  
Context Similarity ◽  
Internet Information

With the speedy development of Internet, information technology has provided an unmatched amount of information resources. To help people to find helpful information, electronic commerce personalized recommendation technique emerges. Collaborative filtering is one successful personalized recommendation technology, and is widely used in many fields. But traditional collaborative filtering recommendation algorithm has the problem of sparsity, which will influence the efficiency of prediction. User context information is rarely considered in the recommendation process, especially in the collaborative filtering. In this paper, a new electronic commerce collaborative filtering recommendation algorithm is given which applies the user context information. This method combines the rating similarity and the user context similarity in the electronic commerce recommendation process to improve the prediction accuracy by efficiently managing the problem of data sparsity.

scholarly journals Review Rating Prediction Based on User Context and Product Context

2018 ◽  
Vol 8 (10) ◽  
pp. 1849 ◽  
Author(s):  
Bingkun Wang ◽  
Shufeng Xiong ◽  
Yongfeng Huang ◽  
Xing Li
Keyword(s):  
Prediction Model ◽  
Language Processing ◽  
Product Information ◽  
Prediction Method ◽  
Training Data ◽  
Context Information ◽  
Research Focus ◽  
User Context ◽  
Online User Reviews ◽  
Rating Prediction

With the explosion of online user reviews, review rating prediction has become a research focus in natural language processing. Existing review rating prediction methods only use a single model to capture the sentiments of review texts, ignoring users who express the sentiment and products that are evaluated, both of which have great influences on review rating prediction. In order to solve the issue, we propose a review rating prediction method based on user context and product context by incorporating user information and product information into review texts. Our method firstly models the user context information of reviews, and then models the product context information of reviews. Finally, a review rating prediction method that is based on user context and product context is proposed. Our method consists of three main parts. The first part is a global review rating prediction model, which is shared by all users and all products, and it can be learned from training datasets of all users and all products. The second part is a user-specific review rating prediction model, which represents the user’s personalized sentiment information, and can be learned from training data of an individual user. The third part is a product-specific review rating prediction model, which uses training datasets of an individual product to learn parameter of the model. Experimental results on four datasets show that our proposed methods can significantly outperform the state-of-the-art baselines in review rating prediction.

scholarly journals InContexto: Multisensor Architecture to Obtain People Context from Smartphones

2012 ◽  
Vol 8 (4) ◽  
pp. 758789 ◽  
Author(s):  
Gonzalo Blázquez Gil ◽  
Antonio Berlanga ◽  
José M. Molina
Keyword(s):  
Mobile Phones ◽  
Embedded Sensors ◽  
Context Information ◽  
Distributed Architecture ◽  
Sensory Data ◽  
User Context ◽  
Android Os ◽  
Made In ◽  
The Way

The way users intectact with smartphones is changing after the improvements made in their embedded sensors. Increasingly, these devices are being employed as tools to observe individuals habits. Smartphones provide a great set of embedded sensors, such as accelerometer, digital compass, gyroscope, GPS, microphone, and camera. This paper aims to describe a distributed architecture, called inContexto, to recognize user context information using mobile phones. Moreover, it aims to infer physical actions performed by users such as walking, running, and still. Sensory data is collected by HTC magic application made in Android OS, and it was tested achieving about 97% of accuracy classifying five different actions (still, walking and running).

Automatic Stereotype Activation Is Context Dependent

2010 ◽  
Vol 41 (3) ◽  
pp. 131-136 ◽  
Author(s):  
Catharina Casper ◽  
Klaus Rothermund ◽  
Dirk Wentura
Keyword(s):  
Lexical Decision ◽  
Context Information ◽  
Decision Task ◽  
Social Categories ◽  
Priming Effects ◽  
Stereotype Activation ◽  
Social Stereotypes ◽  
Priming Paradigm ◽  
Automatic Activation ◽  
Mental Schemas

Processes involving an automatic activation of stereotypes in different contexts were investigated using a priming paradigm with the lexical decision task. The names of social categories were combined with background pictures of specific situations to yield a compound prime comprising category and context information. Significant category priming effects for stereotypic attributes (e.g., Bavarians – beer) emerged for fitting contexts (e.g., in combination with a picture of a marquee) but not for nonfitting contexts (e.g., in combination with a picture of a shop). Findings indicate that social stereotypes are organized as specific mental schemas that are triggered by a combination of category and context information.

Impact of Context Information on Metaphor Elaboration

2018 ◽  
Vol 65 (6) ◽  
pp. 370-384 ◽  
Author(s):  
Veronika Lerche ◽  
Ursula Christmann ◽  
Andreas Voss
Keyword(s):  
Diffusion Model ◽  
Processing Speed ◽  
A Priori ◽  
Model Fit ◽  
Verbal Task ◽  
Context Information ◽  
Speed Of Processing ◽  
Good Account ◽  
Dependent Variables ◽  
Decision Settings

Abstract. In experiments by Gibbs, Kushner, and Mills (1991) , sentences were supposedly either authored by poets or by a computer. Gibbs et al. (1991) concluded from their results that the assumed source of the text influences speed of processing, with a higher speed for metaphorical sentences in the Poet condition. However, the dependent variables used (e.g., mean RTs) do not allow clear conclusions regarding processing speed. It is also possible that participants had prior biases before the presentation of the stimuli. We conducted a conceptual replication and applied the diffusion model ( Ratcliff, 1978 ) to disentangle a possible effect on processing speed from a prior bias. Our results are in accordance with the interpretation by Gibbs et al. (1991) : The context information affected processing speed, not a priori decision settings. Additionally, analyses of model fit revealed that the diffusion model provided a good account of the data of this complex verbal task.

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