scholarly journals DIM: Adaptively Combining User Interests Mined at Different Stages Based on Deformable Interest Model

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xiaoru Wang ◽  
Yueli Li ◽  
Zhihong Yu ◽  
Fu Li ◽  
Heng Zhang ◽  
...  
Keyword(s):  
Dirichlet Distribution ◽  
Multinomial Distribution ◽  
Situational Interest ◽  
Personalized Recommendation ◽  
User Interest ◽  
Degree Of Deformation ◽  
Tracking Model ◽  
Interest Model ◽  
Evolutional Process

User interest mining is widely used in the fields of personalized search and personalized recommendation. Traditional methods ignore the formation of user interest which is a process that evolves over time. This leads to the inability to accurately describe the distribution of user interest. In this paper, we propose the interest tracking model (ITM). To add the timing, ITM uses Dirichlet distribution and multinomial distribution to describe the evolutional process of interest topics and frequent patterns, which well adapts to the evolution of user interest hidden in short texts between different time slices. In addition, it is well known that user interest is composed of long-term interest and situational interest including short-term interest and social hot topics. State-of-the-art methods simply regard the users’ long-term interest as the users’ final interest, which makes those unable to completely describe the user interest distribution. To solve this problem, we propose the deformable interest model (DIM) which designs an objective function to combine users’ long-term interest and situational interest and more comprehensively and accurately mine user interest. Furthermore, we present the degree of deformation which measures the subinterest's degree of influence on final interest and propose in DIM the influence real-time update mechanism. The mechanism adaptively updates the degree of deformation through the linear iteration and reduces the degree of dependence of the interest model on training sets. We present results via a dataset consisting of Flickr users and their uploaded information in three months, a dataset consisting of Twitter users and their tweets in three months, and a dataset consisting of Instagram users and their uploaded information in three months, showing that the perplexity is reduced to 0.378, the average accuracy is increased to 94%, and the average NMI is increased to 0.20, which prove better interest prediction.

An Improved User Interest Model for Microblog Personalized Recommendation

2014 ◽  
Vol 571-572 ◽  
pp. 1157-1162
Author(s):  
Wei Long Ye ◽  
Bei Zhan Wang ◽  
Kang Chen ◽  
Kai Jie Guo
Keyword(s):  
Social Networks ◽  
Web 2.0 ◽  
Experimental Results ◽  
Personalized Recommendation ◽  
User Interest ◽  
Combined Model ◽  
User Data ◽  
Interest Model ◽  
User Interest Model ◽  
User Recommendation

As a representative application of Web 2.0, microblog is now becoming one of the most popular social networks. There has been an increasing number of research about user interest in social networks. Based on these related works, an improved user interest model for microblog user recommendation is presented in this paper. By fetching user data, generating datasets, building user interest models and combining these models, a recommended user list is generated to help people find users they interested. Experimental results show the effectiveness of the combined model.

A PERSONALIZED IMAGE RETRIEVAL BASED ON USER INTEREST MODEL

2010 ◽  
Vol 24 (03) ◽  
pp. 401-419 ◽  
Author(s):  
JING ZHANG ◽  
LI ZHUO ◽  
LANSUN SHEN ◽  
LIN HE
Keyword(s):  
Image Retrieval ◽  
User Satisfaction ◽  
Clustering Algorithm ◽  
Visual Features ◽  
Semantic Features ◽  
User Interest ◽  
Short Term ◽  
Interest Model ◽  
User Interest Model

In order to narrow the semantic gap, user interest model plays an important role in personalized image retrieval. A novel personalized image retrieval approach based on user interest model is proposed in this study. User interest model is developed on the basis of short-tem and long-term interests. (1) Short-term interests are represented by collecting visual and semantic features. Visual features are collected by MARS relevance feedback. Semantic features are constructed by building a mapping from image low-level visual features to high-level semantic features on the basis of SVM. (2) Long-term interests are inferred by inference engine from the collected short-term interests. Long-term visual features are collected by the nonlinear gradual forgetting interest inference algorithm and semantic features are obtained by clustering algorithm. After applying to image retrieval, experimental results show that the average recall/precision is significantly improved and a better user satisfaction rate is achieved as well. Furthermore, it demonstrates our model can be efficiently adapted to user interests and matches personalized image retrieval.

scholarly journals A novel user-interest model based on mixed measure

2017 ◽  
Vol 887 ◽  
pp. 012061
Author(s):  
Junkai Yi ◽  
Yacong Zhang ◽  
Mingyong Yin ◽  
Xianghui Zhao
Keyword(s):  
User Interest ◽  
Model Based ◽  
Mixed Measure ◽  
Interest Model ◽  
User Interest Model

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.

A Webpage Ranking Algorithm Based on Collaborative Recommendation

2013 ◽  
Vol 765-767 ◽  
pp. 998-1002
Author(s):  
Shao Xuan Zhang ◽  
Tian Liu
Keyword(s):  
Sorting Algorithm ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Search Results ◽  
Collaborative Recommendation ◽  
Interest Model ◽  
Sorting Efficiency ◽  
User Interest Model ◽  
Relevant Calculation ◽  
Personalized Ranking

In view of the present personalized ranking of search results user interest model construction difficult, relevant calculation imprecise problems, proposes a combination of user interest model and collaborative recommendation algorithm for personalized ranking method. The method from the user search history, including the submit query, click the relevant webpage information to train users interest model, then using collaborative recommendation algorithm to obtain with common interests and neighbor users, on the basis of these neighbors on the webpage and webpage recommendation level associated with the users to sort the search results. Experimental results show that: the algorithm the average minimum precision than general sorting algorithm was increased by about 0.1, with an increase in the number of neighbors of the user, minimum accuracy increased. Compared with other ranking algorithms, using collaborative recommendation algorithm is helpful for improving webpage with the user interest relevance precision, thereby improving the sorting efficiency, help to improve the search experience of the user.

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