Personalized Recommendation Combining User Interest and Social Circle

Qian, Xueming; He, Feng; Zhao, Guoshuai; Mei, Tao

doi:10.1109/tkde.2013.168

Cited by 321 publications

(134 citation statements)

References 51 publications

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“…For accuracy then the top ranked famous routes optimized according to social similar users travel history for personalized travel sequence recommendation [1]. Xueming Qian explains personalized recommendation which considers two factors, user personal information and users' social group [13]. Subramaniya Swamy described a system which helps to user in finding tourist locations that users want to visit.…”

Section: H Huang Describes Collaborative Filtering To Mine Gps Trajementioning

confidence: 99%

Survey on Recommendation of Personalized Travel Sequence

Aswale¹,

Dharmadhikari²

2017

International Journal of Advanced Research in Computer and Comm

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Now a day, traveling recommendation is important for user who is the plan for traveling. There are many existing techniques which are used for travel recommendation. In this paper explain a personalized travel sequence recommendation system using travelogues and users contributed photos with metadata of this photo by comparing existing different technique. It recommends personalized users travel interest and recommend a sequence of travel interest instead of an individual point of interest. The existing system cannot complete the requirement i.e. personalized and sequential recommendation together. To solve the problem of providing personalized and sequential travel package recommendation, a topical package model is created using social media data in which automatically mine user travel interest with another attribute like time, cost, and season of traveling. The proposed system uses the travelogues and photos of social media which map each user and routes description to the topical package area to induce user topical package model and route topical package model. To suggest personalized POI sequence, first famous routes are stratified as per the similarity between user package and route package. Then high stratified routes are more optimized by using social similar users travel records for more accuracy.

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Section: H Huang Describes Collaborative Filtering To Mine Gps Trajementioning

confidence: 99%

Survey on Recommendation of Personalized Travel Sequence

Aswale¹,

Dharmadhikari²

2017

International Journal of Advanced Research in Computer and Comm

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“…To facilitate the user's exploration and decision making, POI recommendation has been introduced by location-based services such as Yelp and Foursquare. The category topics are usually determined by the naive category information from recommended systems in TM [1], [5]. For example, the original category information of social media websites, such as Foursquare [5], ODP [4], and Yelp [5], serve as topics.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Personalized POI Recommendation using PCA-SVM based Filtering and Classification

Sharma¹,

Suryavanshi²

2017

IJCA

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The rapid growth of cities has developed an increasing number of points of interest (POIs), e.g., restaurants, stores, hotels, etc; to enrich people's life, providing us with more choices of life experiences than before. People are willing to explore the city and neighborhood in their daily life and decide "where they should go" according to their personal interest and various choices of POIs. The Existing Methodology implemented for the Filtering of POI Recommendation is efficient but contains less Precision and Recall, hence a new and efficient technique for the POI Recommendation using Principle Component Analysis with Support Vector Machine Learning is proposed which provides more efficient results in comparison.

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“…Accompanied by the successful recommendations in big data mining, various fields have become to introduce and apply the recommendation, i.e., QoS analysis systems, users and user-user trust networks in social network-based services, and proteins and protein-protein interactomes in bioinformatics [5][6][7]. Among these new application fields, we also apply the recommendation in the social relationship data.…”

Section: Introductionmentioning

confidence: 99%

Framework for Social Relationship Recommendation

Chen

Zhang

2017

Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

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Abstract-Find out the potential favorite data is the essence of recommendation techniques, which facilitates the recommendation techniques become a vital issue in the big data researches and applications. The traditional recommendation research and application centers on the commercial items and resources. We apply the recommendation to social relationships in social networks. We design a framework which can analyze and recommend various types of relationships. The framework has four modules and three work modes. Furthermore, we design a hybrid recommendation approach in the framework for the balancing between accuracy and efficiency. The framework can recommend the social relationships with high accuracy and low cost.

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Personalized Recommendation Combining User Interest and Social Circle

Cited by 321 publications

References 51 publications

Survey on Recommendation of Personalized Travel Sequence

Survey on Recommendation of Personalized Travel Sequence

An Efficient Personalized POI Recommendation using PCA-SVM based Filtering and Classification

Framework for Social Relationship Recommendation

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