Mining User Interests and Change Patterns in Microblog

2013 ◽  
Vol 380-384 ◽  
pp. 1959-1962
Author(s):  
Dong Liu ◽  
Quan Yuan Wu
Keyword(s):  
Feature Vector ◽  
Hamming Distance ◽  
User Interest ◽  
User Interests ◽  
Series Of Experiments ◽  
Share Information

Nowadays, more and more people use microblogs to share information. Consequently, mining microblog users behavior features is very valuable. In the paper, we propose a user interest mining framework. After data pre-processing, VSM is used to generate the feature vector of the tweet sets. Furthermore, k-bit binaries called interest hash-value and continuous interest hash-value are generated by use of Simhash algorithm. The user interests and change patterns could be mined by analyzing the hamming distance sequences between adjacent two hash-values. Taking Sina microblog as background, a series of experiments are done to prove the effectiveness of the algorithms.

Argo

2011 ◽  
pp. 67-83
Author(s):  
Xin-Jing Wang ◽  
Mo Yu ◽  
Lei Zhang ◽  
Wei-Ying Ma
Keyword(s):  
Semantic Gap ◽  
Visual Features ◽  
User Interest ◽  
Low Level ◽  
User Interests ◽  
Supplement User ◽  
Target User ◽  
Series Of Experiments ◽  
High Level

In this chapter, we introduce the Argo system which provides intelligent advertising made possible from user generated photos. Based on the intuition that user-generated photos imply user interests which are the key for profitable targeted ads, Argo attempts to learn a user’s profile from his shared photos and suggests relevant ads accordingly. To learn a user interest, in an offline step, a hierarchical and efficient topic space is constructed based on the ODP ontology, which is used later on for bridging the vocabulary gap between ads and photos as well as reducing the effect of noisy photo tags. In the online stage, the process of Argo contains three steps: 1) understanding the content and semantics of a user’s photos and auto-tagging each photo to supplement user-submitted tags (such tags may not be available); 2) learning the user interest given a set of photos based on the learnt hierarchical topic space; and 3) representing ads in the topic space and matching their topic distributions with the target user interest; the top ranked ads are output as the suggested ads. Two key challenges are tackled during the process: 1) the semantic gap between the low-level image visual features and the high-level user semantics; and 2) the vocabulary impedance between photos and ads. We conducted a series of experiments based on real Flickr users and Amazon.com products (as candidate ads), which show the effectiveness of the proposed approach.

Discovery and classification of user interests on social media

2017 ◽  
Vol 45 (3) ◽  
pp. 130-138 ◽  
Author(s):  
Basit Shahzad ◽  
Ikramullah Lali ◽  
M. Saqib Nawaz ◽  
Waqar Aslam ◽  
Raza Mustafa ◽  
...  
Keyword(s):  
Information Filtering ◽  
Recommendation Systems ◽  
Personalized Recommendation ◽  
Support Vector ◽  
User Interest ◽  
Socioeconomic Impacts ◽  
Content Type ◽  
Positive Correlation ◽  
User Interests ◽  
Twitter Users

Purpose Twitter users’ generated data, known as tweets, are now not only used for communication and opinion sharing, but they are considered an important source of trendsetting, future prediction, recommendation systems and marketing. Using network features in tweet modeling and applying data mining and deep learning techniques on tweets is gaining more and more interest. Design/methodology/approach In this paper, user interests are discovered from Twitter Trends using a modeling approach that uses network-based text data (tweets). First, the popular trends are collected and stored in separate documents. These data are then pre-processed, followed by their labeling in respective categories. Data are then modeled and user interest for each Trending topic is calculated by considering positive tweets in that trend, average retweet and favorite count. Findings The proposed approach can be used to infer users’ topics of interest on Twitter and to categorize them. Support vector machine can be used for training and validation purposes. Positive tweets can be further analyzed to find user posting patterns. There is a positive correlation between tweets and Google data. Practical implications The results can be used in the development of information filtering and prediction systems, especially in personalized recommendation systems. Social implications Twitter microblogging platform offers content posting and sharing to billions of internet users worldwide. Therefore, this work has significant socioeconomic impacts. Originality/value This study guides on how Twitter network structure features can be exploited in discovering user interests using tweets. Further, positive correlation of Twitter Trends with Google Trends is reported, which validates the correctness of the authors’ approach.

Two Phase Recommendation Algorithm Based on Clustering User Interest and Trust

2014 ◽  
Vol 543-547 ◽  
pp. 1856-1859
Author(s):  
Xiang Cui ◽  
Gui Sheng Yin
Keyword(s):  
Social Networks ◽  
Electronic Commerce ◽  
Recommender Systems ◽  
Information Overload ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Two Phase ◽  
Rating Data ◽  
User Interests ◽  
The Future

Recommender systems have been proven to be valuable means for Web online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. We need a method to solve such as what items to buy, what music to listen, or what news to read. The diversification of user interests and untruthfulness of rating data are the important problems of recommendation. In this article, we propose to use two phase recommendation based on user interest and trust ratings that have been given by actors to items. In the paper, we deal with the uncertain user interests by clustering firstly. In the algorithm, we compute the between-class entropy of any two clusters and get the stable classes. Secondly, we construct trust based social networks, and work out the trust scoring, in the class. At last, we provide some evaluation of the algorithms and propose the more improve ideas in the future.

scholarly journals Country-Specific Interests towards Fall Detection from 2004–2021: An Open Access Dataset and Research Questions

Data ◽  
2021 ◽  
Vol 6 (8) ◽  
pp. 92
Author(s):  
Nirmalya Thakur ◽  
Chia Y. Han
Keyword(s):  
Public Health ◽  
Open Access ◽  
Data Science ◽  
Fall Detection ◽  
Time Range ◽  
User Interest ◽  
User Interests ◽  
The World ◽  
Research Questions ◽  
Google Search

Falls, which are increasing at an unprecedented rate in the global elderly population, are associated with a multitude of needs such as healthcare, medical, caregiver, and economic, and they are posing various forms of burden on different countries across the world, specifically in the low- and middle-income countries. For these respective countries to anticipate, respond, address, and remedy these diverse needs either by using their existing resources, or by developing new policies and initiatives, or by seeking support from other countries or international organizations dedicated to global public health, the timely identification of these needs and their associated trends is highly necessary. This paper addresses this challenge by presenting a study that uses the potential of the modern Internet of Everything lifestyle, where relevant Google Search data originating from different geographic regions can be interpreted to understand the underlining region-specific user interests towards a specific topic, which further demonstrates the public health need towards the same. The scientific contributions of this study are two-fold. First, it presents an open-access dataset that consists of the user interests towards fall detection for all the 193 countries of the world studied from 2004–2021. In the dataset, the user interest data is available for each month for all these countries in this time range. Second, based on the analysis of potential and emerging research directions in the interrelated fields of Big Data, Data Mining, Information Retrieval, Natural Language Processing, Data Science, and Pattern Recognition, in the context of fall detection research, this paper presents 22 research questions that may be studied, evaluated, and investigated by researchers using this dataset.

Evaluation of Literature Reading Value Based on User Interests Mining

2014 ◽  
Vol 513-517 ◽  
pp. 2068-2072
Author(s):  
Ya Hui Zhao ◽  
Zhen Guo Zhang ◽  
Rong Yi Cui
Keyword(s):  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Feature Space ◽  
Semantic Space ◽  
Latent Semantic Indexing ◽  
User Interest ◽  
User Interests ◽  
Literature Reading ◽  
Subjective Value

A literature evaluation method based on user interest is proposed by synthesizing self-values of literature and subjective values relative to query users in this paper. Firstly, in the process of mining user interests by hierarchical clustering, vector space model is employed for expressing the literature information that reflects users download behavior and the feature space is compressed by latent semantic indexing to reduce the space dimension. Secondly, subjective value of new literature relative to researchers is quantitatively evaluated in latent semantic space. Finally, the comprehensive evaluation model of literature reading value is constructed by the transformed E-measure index based on self-value and subjective value relative to query user. Experiments show that the proposed evaluation method by fully weigh subjective and objective factors of literature is more reasonable and effective compared with the traditional evaluation methods.

scholarly journals A Novel Contextual Information Recommendation Model and Its Application in e-Commerce Customer Satisfaction Management

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Feipeng Guo ◽  
Qibei Lu
Keyword(s):  
Customer Satisfaction ◽  
Collaborative Filtering ◽  
Distributed Cognition ◽  
Contextual Information ◽  
User Interest ◽  
User Interests ◽  
Good Ability ◽  
Extraction Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Information Recommendation

In the current supply chain environment, distributed cognition theory tells us that various types of context information in which a recommendation is provided are important for e-commerce customer satisfaction management. However, traditional recommendation model does not consider the distributed and differentiated impact of different contexts on user needs, and it also lacks adaptive capacity of contextual recommendation service. Thus, a contextual information recommendation model based on distributed cognition theory is proposed. Firstly, the model analyzes the differential impact of various sensitive contexts and specific examples on user interest and designs a user interest extraction algorithm based on distributed cognition theory. Then, the sensitive contexts extracted from user are introduced into the process of collaborative filtering recommendation. The model calculates similarity among user interests. Finally, a novel collaborative filtering algorithm integrating with context and user similarity is designed. The experimental results in e-commerce and benchmark dataset show that this model has a good ability to extract user interest and has higher recommendation accuracy compared with other methods.

scholarly journals A Multitask Learning Model with Multiperspective Attention and Its Application in Recommendation

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yingshuai Wang ◽  
Dezheng Zhang ◽  
Aziguli Wulamu
Keyword(s):  
User Satisfaction ◽  
Multitask Learning ◽  
Attention And Memory ◽  
User Interest ◽  
Training Models ◽  
User Interests ◽  
Interaction Mode ◽  
Public Data ◽  
Fusion Mode ◽  
Different Characteristics

Training models to predict click and order targets at the same time. For better user satisfaction and business effectiveness, multitask learning is one of the most important methods in e-commerce. Some existing researches model user representation based on historical behaviour sequence to capture user interests. It is often the case that user interests may change from their past routines. However, multi-perspective attention has broad horizon, which covers different characteristics of human reasoning, emotions, perception, attention, and memory. In this paper, we attempt to introduce the multi-perspective attention and sequence behaviour into multitask learning. Our proposed method offers better understanding of user interest and decision. To achieve more flexible parameter sharing and maintaining the special feature advantage of each task, we improve the attention mechanism at the view of expert interactive. To the best of our knowledge, we firstly propose the implicit interaction mode, the explicit hard interaction mode, the explicit soft interaction mode, and the data fusion mode in multitask learning. We do experiments on public data and lab medical data. The results show that our model consistently achieves remarkable improvements to the state-of-the-art method.

Topic recommendation system using personalized fuzzy logic interest set

2020 ◽  
pp. 1-11
Author(s):  
Wenqiang Zhu
Keyword(s):  
Fuzzy Logic ◽  
Recommendation System ◽  
Nearest Neighbor ◽  
User Behavior ◽  
Experimental Tests ◽  
User Interest ◽  
Fuzzy Logic Systems ◽  
Fine Grained ◽  
User Interests ◽  
Collaborative Recommendation

First, the recommendation system and its advantages are introduced in detail, and based on the characteristics of the intelligent topic logical interest set resource and user behavior in the existing intelligent topic logical interest set resource platform, a personalized fuzzy logic model of the intelligent topic logical interest set resource is established and adapted to it. The personalized fuzzy logic user personalized fuzzy logic interest model of personalized fuzzy logic is designed, and the user personalized fuzzy logic interest transfer method is designed to simulate the user learning process. Secondly, on the basis of the established model, according to the idea of collaborative filtering, the personalized fuzzy logic user’s personalized fuzzy logic interest value and the user’s rating of resources are respectively predicted, and the two prediction results are combined to recommend resources to the user. Finally, the ontology is applied to user interest description, and a method based on personalized fuzzy logic user rough interest vector and nearest neighbor concept aggregation is proposed to find fine-grained user interest and recommend interest resources. Experimental tests show that this method can better describe the composition and development of user interests, making the recommendation effect of interest resources for specific users more accurate and reliable. The problem of collaborative recommendation in personalized fuzzy logic systems is further studied, the basic principles and typical technologies of collaborative recommendation are analyzed, and the collaborative recommendation method based on users with similar interests and the collaborative recommendation method based on weighted association rules are proposed.

scholarly journals Deep Interest Evolution Network for Click-Through Rate Prediction

2019 ◽  
Vol 33 ◽  
pp. 5941-5948 ◽  
Author(s):  
Guorui Zhou ◽  
Na Mou ◽  
Ying Fan ◽  
Qi Pi ◽  
Weijie Bian ◽  
...  
Keyword(s):  
User Behavior ◽  
Target Item ◽  
State Of The Art ◽  
User Interest ◽  
User Interests ◽  
Sequential Structure ◽  
Changing Trend ◽  
Click Through Rate ◽  
Novel Model ◽  
Deep Interest

Click-through rate (CTR) prediction, whose goal is to estimate the probability of a user clicking on the item, has become one of the core tasks in the advertising system. For CTR prediction model, it is necessary to capture the latent user interest behind the user behavior data. Besides, considering the changing of the external environment and the internal cognition, user interest evolves over time dynamically. There are several CTR prediction methods for interest modeling, while most of them regard the representation of behavior as the interest directly, and lack specially modeling for latent interest behind the concrete behavior. Moreover, little work considers the changing trend of the interest. In this paper, we propose a novel model, named Deep Interest Evolution Network (DIEN), for CTR prediction. Specifically, we design interest extractor layer to capture temporal interests from history behavior sequence. At this layer, we introduce an auxiliary loss to supervise interest extracting at each step. As user interests are diverse, especially in the e-commerce system, we propose interest evolving layer to capture interest evolving process that is relative to the target item. At interest evolving layer, attention mechanism is embedded into the sequential structure novelly, and the effects of relative interests are strengthened during interest evolution. In the experiments on both public and industrial datasets, DIEN significantly outperforms the state-of-the-art solutions. Notably, DIEN has been deployed in the display advertisement system of Taobao, and obtained 20.7% improvement on CTR.

scholarly journals The museum as a dating venue: Couples in the Madhya Pradesh Tribal Museum in Bhopal, India

2018 ◽  
Vol 16 (1) ◽  
Author(s):  
Ina Ross
Keyword(s):  
Moral Education ◽  
Madhya Pradesh ◽  
Special Significance ◽  
User Interest ◽  
Social Image ◽  
User Interests ◽  
The Social ◽  
Dating Couples ◽  
Relationship Of ◽  
The Relationship

Using the example of the Madhya Pradesh tribal Museum in Bhopal, India, the following article deals with a romantic user interest in museums: as a meeting place for unmarried (mixed-sex) couples; in short, as a venue for dates. The article contextualizes this phenomenon by taking into account the relationship of the museum as an institution with its Indian visitors from a historical perspective, and by outlining the social context within which public intimacy is situated in present-day India. It interprets the utilisation of museums by dating couples as a process of appropriation which acquires special significance in view of the frequently cited inadequate entrenchment of museums in India. As requirements of the couples from the museum as a dating venue, the essay identifies the ambience and the discreet behaviour of personnel, a lack of surveillance by family or neighbours, the positive (since it contributes to education – including moral education) social image of the museum as an institution and, finally, economic reasons. However, what emerges as the central motive, as a prerequisite for the safety and sense of comfort of the couples, is the disciplining effect of the museum as an institution on other visitors. The discussion of the museum as a venue for dating is part of a research project about the user interests of visitors to the Madhya Pradesh Tribal Museum in Bhopal, India. In the study, about eight different categories of utilisation were worked out including the museum as a picnic spot and hang-out place, as arts and leisure centre, as a space of collective nostalgia and personal memories and as a backdrop for photos and selfies. 

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