Friends and Locations Recommendation with the use of LBSN

1. Friends and Locations Recommendation with the use of LBSN By EKUNDAYO OLUFEMI ADEOLA 27.02.2014

2. Outline • Understanding Users • Users routine activities minning • Semantic location history • User similarities calculation • Travel Recommendations • Location minning • Detecting travel sequence • Itinerary Recommendation

3. TERMINOLOGIES • LBSN (Location Base Social Networks) • POI (Points of Interest)Specific point on earth.

4. TERMINOLOGIES… • Stay Point Time threshold (tout – tin >∂time). It is also dependent on distance. • User Trajectory  P1->P2->P3->…->Pn PS: Where tin and tout are the time the user got to the region and the time the user leave the region respectively.

5. Law of Geography Everything is related to everything else, but near things are more related than distant things. Here we consider: • Users • Location

6. Relationship Users & Locations 3 kinds of graphs are shown here; User - Location graph (Left hand side) User - User graph (Top right hand) Location - Location graph (Lower right hand)

7. Understanding Users • One day Activity; User’s location history in one day. • Routine Activities;A representation of all the similar 1-day activities. R1 R2 House (20:00 – 7:30) Work (8:00 – 17:30) House (20:00 – 7:30) Work (8:00 – 19:30) Gym (18:00 – 19:30) where R1 and R2 are Routine activities 1 and 2 respectively.

8. Understanding Users… • StayPoint; Already explained in slide 4. • Reference Point; Significant places eg. School, Shopping mall etc.

9. Routine activities mining • Discovering activities patterns from multiple 1-day activities. • Find groups of similar 1-day activities • Represent 1-day activities within the same group with a routine activity model. Similarity can br measured by; where X and Y reference places vectors and D is total extracted reference places of the user, and xi and yiare the time duration the user stayed at the ith reference place during the corresponding time span in two different days.

10. Modeling human location history in semantic spaces • Stay Point Representation; Identifying the exact POI a user has visited. Stay region Using IT-IDF (Term Frequency Inverse Document Frequency) a feature vector could be created for stay region, and with this information the semantic meaning of a region could be gotten. If we regard POIs as word and treat stay region as documents.

11. Modeling human location history in semantic spaces… • Feature Vector; The feature of a stay region r in a collection of regions R is fr = <w1,w2 ...,wK>. where wiis the weight of POI category i in the region r, niis the nos of similar reference places egresturants, N is the total no of reference places, and R is the total nos of stay regions created by all users.

12. Feature Vector… Example; Suppose that a building S1 contains two restaurants and one museum, and another building S2 only has four restaurants. The total number of stay regions created by all the users is 100, in which 50 have restaurants and two contain museums. So, the feature vectors of s1 and s2 are f1 and f2 respectively: PS: Although we still cannot identify the exact POI category visited by an individual, this feature vector determines the interests of a user to some extent by extracting the semantic meaning of a region accessed by the individual.

13. Building a Semantic Location History Using IT-IDF the semantic meaning of a region could be gotten. Regards POIs as word and treat stay region as documents.

14. User Similarities Calculations • Popularity of different locations • Hierarchical property of geographic spaces • Sequential property of users’ movements; Loc A -> LocB -> LocC ->…-> LocN. • Here we consider Travel Match

15. User Similarities Calculations (contd) • Travel Match • The locations in a travel match do not have to be consecutive in the user’s original location history • What we need to detect for the calculating of user similarity are the maximum travel • matches

16. User Similarities Calculations (contd) • Similarity Score N = Total number of users in the database, n = number of users visiting location c. fw(l) is employed to assign a bigger weight to the similarity of sequences occurring at a lower layer, where l = depth of a layer in the hierarchy.

17. Hierarchical User clustering Representative user is determined according to the similarity score between each pairof users. E.g. the individual with the minimal distance to others users in the clusters can be selected as the representative user of the cluster.

18. Travel Recommendations • Interesting Places Popular Travel Sequence  Itinerary Planning  Activities Recommendation.

19. Mining Interesting Locations 1) Formulate a shared hierarchical framework F, using the stay points detected from user's GPS logs, which are then clustered into hierarchy geospatial region. 2) Build location graphs on each layer based on shared framework F and user’s location histories

20. Factors considered • Location Interest  Uses HIT (Hypertext Induced Topic Search) to give Authority score and Hub score to places. Location interest (cij) is represented by; where Iij= authority score of cluster cijconditioned by its ascendant nodes on level l, where 1 ≤ l < i.

21. Factors considered… • Travel Experience; In our system, a user’s (e.g., uk) travel experience is represented by a set of hub scores: Wheredenoted uk’s hub score conditioned by region cij.

22. Detecting travel sequences Example: Demonstrates the calculation of the popularity score for a 2-length sequence (i.e., a sequence containing two locations),A→C. First we consider the authority score of location A(IA), weighted by the Probability of people leaving by the sequence (Out AC) = 5/7. Secondly authority score of location C(Ic) Then the hub score of users (UAC) who have taken this sequence.

23. Following this method, the popularity score of sequence C → D is calculated as follows: Thus, the popularity score of sequence A→C→D equals: PS: This calculation will continue until we get the K-length most travel sequence.

24. Itinerary Recommendation • Query Verification; Check the feasibility of query according to spatial and temporal constraints. • Trip Candidate Selection; Searches a location-interest graph for candidate itineraries satisfying a user’s query • Trip Candidate Ranking; It ranks candidate itineraries according to three factor namely;

25. Itinerary Recommendation… • Elapsed Time ratio (ETR): Ratio between the time length of a recommended itinerary and that given by a user. • Stay Time Ratio (STR): Ratio between time a user could spend in a location and that for traveling between locations. • Interest Density Ratio (IDR): Sum of the interest values of the locations contained in an itinerary. Itineraries is ranked by the euclideandistance in these three dimensions: NB: Popular travel sequence could also be considered here.

26. Itinerary Recommendation

27. Location-Activity Recommendation Location – feature Matrix; It uses IT-IDF to assign a feature to a location (e.g. Restaurants, movies theatre). Activities – Activities: It models the correlation between two differentsactivities that can be performed at a location.

28. Questions and Comments • THANK YOU.