A User Preference Based Automatic Potential Group Generation Method for Social Media Sharing and Recommendation

Jia, Dawen; Zeng, Cheng; Peng, Zhiyong; Peng, Chew Fong; Yang, Ziqiang; Lu, Zhou

doi:10.3724/sp.j.1016.2012.02381

Cited by 6 publications

(2 citation statements)

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“…People's potential demand and relatively stable taste are usually illustrated by exploiting measurable relations between people and locations from historical visits, and people's preference similarities are therefore captured from travel trajectories and characteristics [1][2][3][4][5][6]. Studies on travel habits and patterns are mainly used to predict trajectory [7][8][9][10][11][12][13][14] which is normally conducted via modeling GPS's trajectories [15][16] or analyzing LBSN-based check-in data [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Does Daily Travel Pattern Disclose People’s Preference?

Gan¹,

Gao²,

Han³

2018

Proceedings of the Annual Hawaii International Conference on System Sciences

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Existing studies normally focus on extracting temporal or periodical patterns of people's daily travel for location based services. However, people's characteristics and preference are actually paid much more attention by business. Therefore, how to capture characteristics from their daily travel patterns, is an interesting question. In order to address the research question, we first develop two basic measures in terms of repetitiveness of travel and then two advanced measures, to capture people's activity of daily travel, and the colorfulness of lifestyle, respectively. Incorporating historical trajectories, with real-time positions from a location-based social network (LBSN), i.e. Foursquare, we conduct statistical analysis for people's travel patterns in US cities. Finally, we illustrate people's profiles of travel patterns and lifestyles. Results show that people's preference can be inferred from the developed activity and colorfulness measures. Those findings demonstrate that proposed measures are supposed to be effectively adopted for researchers on travel pattern analysis and preference analysis, and further give suggestions to individuals for location-based decision making.

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Section: Introductionmentioning

confidence: 99%

Does Daily Travel Pattern Disclose People’s Preference?

Gan¹,

Gao²,

Han³

2018

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…With the rapid growth of information on the Internet, the problem of information overload [1] is outstanding increasingly. Collaborative filtering systems [2] can solve this problem through making recommendations for users and find wide application in e-commerce websites such as eBay, Netflix, and Amazon.…”

Section: Introductionmentioning

confidence: 99%

An ensemble method for detecting shilling attacks based on ordered item sequences

Zhang

Chen

2015

Security Comm Networks

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Collaborative filtering systems are vulnerable to shilling attacks in which malicious users bias the systems' recommendation output by inserting fake profiles. While many approaches have been proposed to detect shilling attacks, they suffer from low precision. To solve this problem, an ensemble method for detecting shilling attacks based on ordered item sequences is proposed. Firstly, by analyzing the differences of rating patterns between genuine and attack profiles, we construct ordered popular item sequences and ordered novelty item sequences, and based on which, the popular and novelty item rating series are constructed for each user profile. Secondly, we propose six features to characterize the attack profiles. Particularly, we extract two features based on the popular and novelty item rating series. We partition the item set according to the ordered item sequences and combine them with mutual information to extract another four features. Finally, we propose an ensemble framework to detect shilling attacks. In particular, we create base training sets with great diversities using bootstrap resampling technique. Based on these base training sets, we train decision tree algorithm to generate diverse base classifiers. The simple majority voting strategy is used to combine the predictive results of these base classifiers. Experimental results indicate that ensemble method for detecting shilling attacks based on ordered item sequences can significantly improve the precision while maintaining a high recall. Copyright © 2015 John Wiley & Sons, Ltd.

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