PS-LSTM:Popularity Analysis And Social Network For Point-Of-Interest Recommendation In Previously Unvisited Locations

2021 ◽  
Author(s):  
Chongyu Zhong ◽  
Jinghua Zhu ◽  
Heran Xi
Keyword(s):  
Social Network ◽  
Point Of Interest

scholarly journals Exploring IoT Location Information to Perform Point of Interest Recommendation Engine: Traveling to a New Geographical Region

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 992 ◽  
Author(s):  
Xu Yang ◽  
Billy Zimba ◽  
Tingting Qiao ◽  
Keyan Gao ◽  
Xiaoya Chen
Keyword(s):  
Social Network ◽  
Recommendation System ◽  
Real Data ◽  
User Preferences ◽  
Data Sets ◽  
Wireless Internet ◽  
User Reviews ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Geographical Regions

With the development of wireless Internet and the popularity of location sensors in mobile phones, the coupling degree between social networks and location sensor information is increasing. Many studies in the Location-Based Social Network (LBSN) domain have begun to use social media and location sensing information to implement personalized Points-of-interests (POI) recommendations. However, this approach may fall short when a user moves to a new district or city where they have little or no activity history and social network friend information. Thus, a need to reconsider how we model the factors influencing a user’s preferences in new geographical regions in order to make personalized and relevant recommendation. A POI in LBSNs is semantically enriched with annotations such as place categories, tags, tips or user reviews which implies knowledge about the nature of the place as well as a visiting person’s interests. This provides us with opportunities to better understand the patterns in users’ interests and activities by exploiting the annotations which will continue to be useful even when a user moves to unfamiliar places. In this research, we proposed a location-aware POI recommendation system that models user preferences mainly based on user reviews, which shows the nature of activities that a user finds interesting. Using this information from users’ location history, we predict user ratings by harnessing the information present in review text as well as consider social influence from similar user set formed based on matching category preferences and similar reviews. We use real data sets partitioned by city provided by Yelp, to compare the accuracy of our proposed method against some baseline POI recommendation algorithms. Experimental results show that our algorithm achieves a better accuracy.

scholarly journals P3OI-MELSH: Privacy Protection Target Point of Interest Recommendation Algorithm Based on Multi-Exploring Locality Sensitive Hashing

2021 ◽  
Vol 15 ◽  
Author(s):  
Desheng Liu ◽  
Linna Shan ◽  
Lei Wang ◽  
Shoulin Yin ◽  
Hui Wang ◽  
...  
Keyword(s):  
Social Network ◽  
Privacy Protection ◽  
Rapid Development ◽  
Locality Sensitive Hashing ◽  
Target Point ◽  
Interest Points ◽  
Recommendation Algorithm ◽  
Original Algorithm ◽  
Point Of Interest ◽  
Poi Recommendation

With the rapid development of social network, intelligent terminal and automatic positioning technology, location-based social network (LBSN) service has become an important and valuable application. Point of interest (POI) recommendation is an important content in LBSN, which aims to recommend new locations of interest for users. It can not only alleviate the information overload problem faced by users in the era of big data, improve user experience, but also help merchants quickly find target users and achieve accurate marketing. Most of the works are based on users' check-in history and social network data to model users' personalized preferences for interest points, and recommend interest points through collaborative filtering and other recommendation technologies. However, in the check-in history, the multi-source heterogeneous information (including the position, category, popularity, social, reviews) describes user activity from different aspects which hides people's life style and personal preference. However, the above methods do not fully consider these factors' combined action. Considering the data privacy, it is difficult for individuals to share data with others with similar preferences. In this paper, we propose a privacy protection point of interest recommendation algorithm based on multi-exploring locality sensitive hashing (LSH). This algorithm studies the POI recommendation problem under distributed system. This paper introduces a multi-exploring method to improve the LSH algorithm. On the one hand, it reduces the number of hash tables to decrease the memory overhead; On the other hand, the retrieval range on each hash table is increased to reduce the time retrieval overhead. Meanwhile, the retrieval quality is similar to the original algorithm. The proposed method uses modified LSH and homomorphic encryption technology to assist POI recommendation which can ensure the accuracy, privacy and efficiency of the recommendation algorithm, and it verifies feasibility through experiments on real data sets. In terms of root mean square error (RMSE), mean absolute error (MAE) and running time, the proposed method has a competitive advantage.

Predicting POI Visits in a Heterogeneous Location-Based Social Network

2016 ◽  
Vol 20 (6) ◽  
pp. 882-892 ◽  
Author(s):  
Zih-Syuan Wang ◽  
◽  
Jing-Fu Juang ◽  
Wei-Guang Teng
Keyword(s):  
Social Network ◽  
Prediction Model ◽  
Good Prediction ◽  
Heterogeneous Information ◽  
Practical Applications ◽  
Point Of Interest ◽  
Topological Features ◽  
Meta Path ◽  
Location Based Social Network ◽  
Location Based Social Networks

A point of interest (POI) is a specific location that people may find useful or interesting, such as restaurants, stores, attractions, and hotels. With the recent proliferation of location-based social networks (LBSN), numerous users gather to interact and share information on various POIs. POI recommendations have become a crucial issue because it not only helps users to learn about new places but also gives LBSN providers chances to post POI advertisements. As we utilize a heterogeneous information network to represent an LBSN in this work, POI recommendations are remodeled as a link prediction problem, which is significant in the field of social network analysis. Moreover, we propose to utilize the meta-path-based approach to extract implicit but potentially useful relationships between a user and a POI. Then, the extracted topological features are used to construct a prediction model with appropriate data classification techniques. In our experiments, the Yelp dataset is utilized as our testbed for performance evaluation purposes. The results show that our prediction model is of good prediction quality in practical applications.

The Advantages of Cryotechniques: Application To Bioluminescent Cells

1990 ◽  
Vol 48 (1) ◽  
pp. 486-487
Author(s):  
Gisèle Nicolas ◽  
Jean-Marie Bassot ◽  
Marie-Thérèse Nicolas
Keyword(s):  
Endoplasmic Reticulum ◽  
Striated Muscle ◽  
Light Emission ◽  
Stable State ◽  
Freeze Substitution ◽  
Initial Step ◽  
Point Of Interest ◽  
Cell Components ◽  
Excellent Preservation ◽  
External Water

The use of fast-freeze fixation (FFF) followed by freeze-substitution (FS) brings substantial advantages which are due to the extreme rapidity of this fixation compared to the conventional one. The initial step, FFF, physically immobilizes most molecules and therefore arrests the biological reactions in a matter of milliseconds. The second step, FS, slowly removes the water content still in solid state and, at the same time, chemically fixes the other cell components in absence of external water. This procedure results in an excellent preservation of the ultrastructure, avoids osmotic artifacts,maintains in situ most soluble substances and keeps up a number of cell activities including antigenicities. Another point of interest is that the rapidity of the initial immobilization enables the capture of unstable structures which, otherwise, would slip towards a more stable state. When combined with electrophysiology, this technique arrests the ultrastructural modifications at a well defined state, allowing a precise timing of the events.We studied the epithelium of the elytra of the scale-worm, Harmothoe lunulata which has excitable, conductible and bioluminescent properties. The intracellular sites of the light emission are paracrystals of endoplasmic reticulum (PER), named photosomes (Fig.1). They are able to flash only when they are coupled with plasma membrane infoldings by dyadic or triadic junctions (Fig.2) basically similar to those of the striated muscle fibers. We have studied them before, during and after stimulation. FFF-FS showed that these complexes are labile structures able to diffentiate and dedifferentiate within milliseconds. Moreover, a transient network of endoplasmic reticulum was captured which we have named intermediate endoplasmic reticulum (IER) surrounding the PER (Fig.1). Numerous gap junctions are found in the membranous infoldings of the junctional complexes (Fig.3). When cryofractured, they cleave unusually (Fig.4-5). It is tempting to suggest that they play an important role in the conduction of the excitation.

Sign in / Sign up

Export Citation Format

Share Document