“Location-based recommendation”的意思、由来-开放百科全书

Location-based recommendation is a recommender system that incorporates location information, such as that from a mobile device, into algorithms to attempt to provide more-relevant recommendations to users. This could include recommendations for restaurants, museums, or other points of interest or events near the user's location.

These services take advantage of the increasing use of smartphones that store and provide the location information of their users alongside location-based social networks (LBSN), like Foursquare, Gowalla, Swarm, and Yelp. In addition to geosocial networking services, traditional online social networks such as Facebook and Twitter are using the location information of their users to show and recommend upcoming events, posts, and local trends.

In addition to its value for users, this information is valuable for third-party companies to advertise products, hotels, places, and to forecast service demand such as the number of taxis needed in a part of a city.

Background

Recommender systems are information filtering systems which attempt to predict the rating or preference that a user would give, based on ratings that similar users gave and ratings that the user gave on previous occasions. These systems have become increasingly popular and are used for movies, music, news, books, research articles, search queries, social tags, and products in general.

Recommending new places

The main objective of recommending new places is to provide a suggestion to a user to visit unvisited places like restaurants, museums, national parks or other points of interest. This type of recommendation is quite valuable, especially for those who are traveling to a new city and want the best experience during their trip. Location-based social networks or third-party advertising companies are willing to provide a recommendation not only based on previous check-ins and preferences but also using social links to suggest a not-visited point-of-interest. The implicit goal of this type of recommendation is to lift the user's burden of searching for an interesting place.

One of the first studies in this area was conducted in 2011. The idea behind this work was to leverage social influence and location influence and provide recommendations. The authors provide three types of scores:^[1]

The aggregate of these three scores is defined as:

Where the three terms correspond to recommender systems based on user preference, social influence and geographical influence, respectively. The two weighting parameters

and

denote the relative importance of social influence and geographical influence compared to user preference.

Recommending the next place

Providing a sequence of recommendations becomes increasingly complex, and must take into account each location, the time, weather, reachability, etc., before providing any suggestion. These are generally known as context-aware recommendations, and tend to provide places that other people (possibly the user's friends) visited following an initial visit to the location of the user's first recommendation.

Recommending events and neighborhoods

There are a vast number of temporary events being held in different locations. Detecting and recommending events that would be interesting to a user is a task which requires considerable profiling, both of the user's history of event preferences and those of the user's social circle.

Social events

Researchers at a 2010 Institute of Electrical and Electronics Engineers (IEEE) conference discussed the need of a reliable fine-grained dataset of previous user-attendance in order to provide social-event detection.^[4] User residence area and attended events were estimated from user mobile data. Six different strategies were designed and tested for event recommendation:

Neighborhoods

In their paper, the authors of interactive tool Hoodsquare explain how they use 5-month Foursquare check-in data to group similar areas together to redraw the neighborhood boundaries of a city.^[5] Their analysis reveals different characteristics of a city which could be exploited for different tasks such as attractive neighborhood recommendations for tourists and city residents. Similarly, the Livehood project^[6] was conducted in order to define new local regions, named Livehoods, to partition a city in a way that could reflect the character of life in those areas.

References

1. ^{{Cite book|last=Ye|first=Mao|last2=Yin|first2=Peifeng|last3=Lee|first3=Wang-Chien|last4=Lee|first4=Dik-Lun|date=2011-01-01|title=Exploiting Geographical Influence for Collaborative Point-of-interest Recommendation|journal=Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval|series=SIGIR '11|location=New York, NY, USA|publisher=ACM|pages=325–334|doi=10.1145/2009916.2009962|isbn=9781450307574}}
2. ^{{Cite journal|last=Noulas|first=Anastasios|last2=Scellato|first2=Salvatore|last3=Mascolo|first3=Cecilia|last4=Pontil|first4=Massimiliano|date=2011-01-01|title=An empirical study of geographic user activity patterns in foursquare|url=http://www.citeulike.org/user/amirreza/article/9770331|pages=570–573}}
3. ^{{Cite journal|last=Brockmann|first=D.|last2=Hufnagel|first2=L.|last3=Geisel|first3=T.|date=2006-01-26|title=The scaling laws of human travel|journal=Nature|volume=439|issue=7075|pages=462–465|doi=10.1038/nature04292|issn=1476-4687|pmid=16437114|arxiv=cond-mat/0605511|bibcode=2006Natur.439..462B}}
4. ^{{Cite book|last=Quercia|first=D.|last2=Lathia|first2=N.|last3=Calabrese|first3=F.|last4=Lorenzo|first4=G. Di|last5=Crowcroft|first5=J.|date=2010-12-01|title=Recommending Social Events from Mobile Phone Location Data|journal=2010 IEEE International Conference on Data Mining|pages=971–976|doi=10.1109/ICDM.2010.152|isbn=978-1-4244-9131-5|citeseerx=10.1.1.469.1876}}
5. ^{{Cite book|last=Zhang|first=A. X.|last2=Noulas|first2=A.|last3=Scellato|first3=S.|last4=Mascolo|first4=C.|date=2013-09-01|title=Hoodsquare: Modeling and Recommending Neighborhoods in Location-Based Social Networks|journal=2013 International Conference on Social Computing|pages=69–74|doi=10.1109/SocialCom.2013.17|arxiv=1308.3657|isbn=978-0-7695-5137-1}}
6. ^{{Cite book|url=http://citeseerx.ist.psu.edu/viewdoc/summary;jsessionid=F503DE3CE38C23F37695D7C377E63647?doi=10.1.1.365.7792|title=The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City. ICWSM'12|last=Cranshaw|first=Justin|last2=Schwartz|first2=Raz|last3=Hong|first3=Jason I.|last4=Sadeh|first4=Norman}}