Point-of-Interest Recommendation in Location-Based Social Networks with Personalized Geo-Social Influence

Abstract

Point-of-interest recommendation in location-based social networks with personalized geo-social influence project report on web mining The rapid urban expansion has greatly extended the physical boundary of our living area, along with a large number of POIs (points of interest) being developed. A POI is a specific location (e.g., hotel, restaurant, theater, mall) that a user may find useful or interesting.

Point-of-interest recommendation in location-based social networks with personalized geo-social influence project report on web mining When exploring the city and neighborhood, the increasing number of POIs could enrich people’s daily life, providing them with more choices of life experience than before, while at the same time also brings the problem of “curse of choices”, resulting in the difficulty for a user to make a satisfied decision on “where to go” in an efficient way. Point-of-interest recommendation in location-based social networks with personalized geo-social influence project report on web mining Personalized POI recommendation is a task proposed on purpose of helping users filter out uninteresting POIs and reduce time in decision making, which could also benefit virtual marketing.

Conclusion

Point-of-interest recommendation in location-based social networks with personalized geo-social influence project report on web mining In this dissertation, we study personalized POI recommendation on LBSNs. We investigate various LBSN-related properties to design the POI recommender systems, including geo-temporal patterns, geo-social correlations, and geo-content indications. We evaluate the performance of proposed models on a real-world LBSN dataset and summarize key observations from the experimental results.

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