Location Recommendation — for Out-of-Town Users in Location-Based Social Network Yina Meng

Outline Background User-Based CF Model UPS-CF Model Evaluation Conclusion Outline First, I will introduce two locations-based social network applications And then I will tell you something about the strategy for most previous research of location recommendation and their problem. And based on these problem. This paper propose a new recommendation model to address the problem.

Background Location-based social networks(LBSNs) Foursquare Provide personalized recommendations of places to go to near a user’s current location. Gowalla Allow users to check into locations that they visited using their mobile devices. Check- ins could be pushed via notifications to phone, and by linking accounts, to Twitter and Facebook. Users would occasionally receive a virtual "Item" as a bonus upon checking in, and these items could be swapped or dropped at other spots. Users became "Founders" of a spot by dropping an item there. To encourage mobile users to explore new locations, the location recommendation service is an essential function to LBSNs. Background So what’s the locations-based social network. There are two examples. The first one is Foursquare, it is a kind of application which can provide location recommendation based on users’ current location. The second one is called Gowalla, it is a kind of application which can let people check in at locations. And users can receive items as bonus for checking in and they can swap or drop these items on other places. So in location-based social network, people who have similar visiting history can be considered as a relationship. Maybe we can use the behavior of people who have similar visiting history to do location recommendation for a user. A social network is individuals are connected by friendship. A location-based social network is individuals are connected by their locations. So similar visiting histories among people will be considered as a relationship. So we can use the location behavior of other user who have the similar visiting history with a specific user to make location recommendation for the user. A social network is a social structure made up of individuals connected by one or more specific types of interdependency, such as friendship, common interests, and shared knowledge. A location-based social network consists of the new social structure made up of individuals connected by the interdependency derived from their locations in the physical world as well as their location-tagged media content, such as photos, video, and texts. For example,

User Based CF User Based Collaborative Filtering (User-Based CF) A method of making item recommendations (filtering) about the interests of a user by collecting preferences information from many users (collaborating). Recommend locations for a target user in accordance with location visiting behaviors of “similar users”. A user’s preference over a specific location Cosine similarity weight w between user u and v User Based CF Most previous research did this thing. There is a basic location recommendation model called user based collaborative filtering. User-based CF is Which can be adopted for location recommendations by treating locations as items. (i.e., other users with similar visiting histories in terms of commonly visited locations) A ranking score as the probability of a user u visiting location l can be considered as a user’s implicit preference over a specific location. Let U be the user set and L be the location set in an LBSN. So when u,l pair is determined, we will use the top-m similar users of target user by computing similarity weight to predict the preference of the target user. There are many ways of calculating the similarity weight, For simplicity, cosine- based similarity is used in this paper. Cu,l represents check-in activity. cu,l = 1 represents that u has a check-in at l and cu,l = 0 otherwise. Same for Cv,l. The range of the weight is from 0 to 1 Then we choose top-m similar users to compute the preference here. So U’ is the top-m users set of the target user. In other words, we will use top-m similar users’ behavior to make the prediction so we can make a recommendation. So if the similar user has checked in this location, his or her weight will be count. U: user set; L: location set; cu,l: check-in activity(0 or 1);

User Based CF Performance Precision of user-based CF User Based CF Performance Reasons for degradation The recommended locations derived from candidate locations previously visited by the top similar users of the target user are likely to be close to her home region and thus too far away from her current region Some of the most similar users may not have visited locations near the target user’s current region This paper perform experiments using Foursquare and Gowalla’s dataset. We aim to support location recommendation for both in-town and out-of-town scenarios. So we make recommendations to users located at different distances from their home regions to see its performance. The evaluation method is the same as that of the following so I will explain it later. So just see the figure. The user-based CF method performs well when the locations are close to home regions of users, but the precision degrades when the locations are 20-40 km away from their home regions. The top m similar users are close to this target user’s home, the locations which have high preference score are near to the home and far away from user’s current location so the user don’t want to go to that location. most of similar users may not have visited locations near the target user’s current region, so check-in activity in that formula will always be zero

Incorporate a proximity constraint to filter locations far away from the user’s current region Extend the base of similar users from which the candidate locations are derived As prior studies have shown that friends tend to exhibit similar behaviors (and we assume that includes far-away places) Address the problem To address these problems, this paper propose two ideas which are first

UPS-CF Framework U: User preference P: Proximity S: Social-based Proximity constraint dp filter candidate locations which are farther than dp from the target user’s current location; A user’s preference over a specific location UPS-CF Framework Weight w between users u and v combining the roles of a similar user and a friend Control parameter α (where 0 <= α <= 1) balance the weight for the role of a similar user and the weight for the role of a friend So we get the UPS-CF model U represents user preference, P represents proximity and S represents social-based relationship for example friends. First, we add proximity constraint to filter locations which are farther than dp from the target user’s current location We calculate user’s preference in the same way as before. The difference is weight. In this method, the Weight w between users u and v combines the roles of a similar user and a friend using Control parameter α to balance the two kinds of weight, α is the weight for the role of a similar user and 1-α is the weight for the role of a friend so a large α means the role of similar users are important while a small α means friends are important If fu,v = 1 represents that u is friends with v and if fu,v = 0, they are not friends Cosine similarity weight w between user u and v and the friendship between user u and v is denoted as fu,v

Evaluation Two baseline algorithms Most Visited(MV) Closest Locations(CL) Four variants of collaborative filtering method User-Based CF(U) User and Proximity-Based CF(UP) User and Social-Based CF(US) User, Proximity and Social-Based CF(UPS) Evaluation we compare two baseline algorithms and four variants of the collaborative filtering method

Evaluation Four-fold cross validation Parameter Tuning (Training) Set dp = 100km Tune α to obtain its optimal settings for in-town and out-of-town scenarios. Precision@5 and precision@10 Evaluation We set dp to be 100 km so a reasonable distance for a user to travel When users are in town, similar users contribute more to effective recommendations while social friends play a more important role when users are out of town. Given the check-ins in the collected Foursquare and Gowalla datasets, the general idea is to mark o↵ some data points in the datasets (e.g., a user u has visited a lo- cation l). Using this as the ground truth, we evaluate how well the algorithms are able to recover the mark-o↵ l in their recommendations for u. This allows us to see not only how the algorithms compare overall but also for varying distance from the users’ home regions. Notice that α is the weight for the role of a similar user and 1 α is the weight for the role of a friend One possible expla-nation for a small ↵ in out-of-town scenarios is that users may travel to various places to visit friends, which makes friends more important in this scenario Precision@5 and precision@10 In-town recommendation —— similar users Out-of-town recommendation —— social friends

Evaluation Evaluation Process (Testing) Randomly remove a check-in record that a user u has visited a location l. Randomly select a query point q (current standing location of u) that is distance dql away from l. For each algorithm, we recommend N locations for u to visit and track if l was recommended. After repeating the process, we calculate precision@N for each algorithm The user location pairs (u, l) are divided into in town (0-20 km) and out of town (200- 1000 km) in accordance with the distance of a location l from the home region of user u

Effectiveness of algorithms for in-town users(0-20 km from the home region) Effectiveness of algorithms for out-of-town users (200-1000 km from the home region) Evaluation UP performs better than U and UPS performs better than US  proximity constraint US performs better than U and UPS performs better than UP  social connections For the out-of-town scenario, we see that the precision of UPS and UP is strengthened (in comparison to the in-town scenario) while the precision of US and U degrades in both datasets, which shows that filtering farther away locations is very important in this scenario. US and U do not per- form well because some of the recommended locations may be too far away. In addition, we see that UPS outperforms UP and US outperforms U, which means that social friends are important for recommendation CL performs average for out-of-town traveling, but it does not take advantage of similar users and social friends like UPS. MV performs poorly throughout.

Conclusion UPS-CF outperforms all other comparing algorithms The effectiveness of UPS-CF does not degrade for out-of-town users For in-town users, similar users are important while social friends become more important for out-of-town users Conclusion

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