Table 4 Sample records from historical weather data.
Related Figures (17)
Table 1. Representation of visits in terms of (u, /, f). Table 2. Temporal and weather context concepts. €10z API OZ GE:00 Fe [AismPat: buetoeyz,] Aq pepeotMor Table 3. Representation of visits in terms of (u, /, t, w). Figure 2. Relationship between users and locations based on visits. Definition 8: (User Interest) From matrix Myz, the user u,’s travel interest can be derived as an array Ry = </p0,1pis- - +» pn >, Where rp; is u,’s implicit rating (visits made by u,) in a location i. S(R,) is the subset of R,, Wrp; € S(Rp), “pi F 0, that is, the set of locations that has been preferred (visited) by u,. The average rating in R, is denoted as Re. For example, R, = <5, 2, 0, 4, 3, O>, Ro = <1, 0, 3, 2, 0, 1> then S(R;) = <5, 2, 4, 3>, and S(R2) = <1, 3,2, 1>. We use similarity, sim(u,, u,), between users u, and ug as a weight to calculate the rank score for each location /;. That is, the more similar u, and u, are, the more weight r,; will carry in the prediction of /;. Instead of using the absolute values of ratings, we use the deviations from the average rating of the corresponding user. One problem with using the weighted sum is that it does not take into account the fact that different users may use the rating scale differently. Therefore, we use an adjusted weighted sum here. In Equation (2), multiplier & serves as a normalizing factor and usually selected as in Equation (3) and average rating of user up, R,, from locations in his traveling history, is defined according to Equation (4). Note that Equations (2-4) illustrate a well-known method widely used in many recommender systems (Adomavicius and Tuzhilin 2005). After comnutine the user’s nreferences for each location /: in 7’. we order the locations In the second step, we use the user—location matrix My, that represents the users’ preference and Myy that represents the similarities among users to personalize the rec- ommendations for active user u, for the target city. From Myy, we retrieve similarities between uw, and top N most similar users U’ € U, who have visited the target city, and use Equation (2) to predict preferences of u, for each location /; from L’, that is based on collaborative filtering. In collaborative filtering, the user is recommended items that people with similar tastes and preferences liked in the past. Figure 3. Spatial distribution of photos in cities of China: (a) Hangzhou, (b) Shanghai, (c) Beijing, (d) Chengdu, (e) Hong Kong, and (f) Guangzhou mapped on Google Earth. Table 6. Dataset summary. Figure 4. Convex hull polygon are drawn to show the boundaries of locations identified by cluster- ing geotagged photos in different cities: (a) Hangzhou, (b) Shanghai, (c) Beijing, (d) Chengdu, (e) Hong Kong, and (f) Guangzhou. Table 7. Summary of extracted tourist locations in different cities. Figure 5. Performance comparison of proposed and baseline methods with different number of test locations per user in terms of precision (P). 6.3.3.1. Precision (P). Precision can be defined as the fraction of correct predictions in total number of predictions made. Figure 5 depicts the performance of our proposed per- sonalized context-aware recommendation and other baseline methods in terms of precision in prediction. Popularity-based ranking and classic rank give better prediction results when compared with collaborative filtering method. The reasons are many users do not have sin- gle preferences but visit locations of many types and those locations that are popular and significant when they come to visit a new city. For this reason, evaluation method actu- ally expects us to recommend these. We find that tourists comply more with the genera travel preference and are therefore easier to predict by the popularity- or significance- based baseline. This is inherent to the evaluation of recommendations with a train and test set. For users with more number of locations visited in test city, the performance o collaborative filtering improves. These results indicate that collaborative filtering method is effective in recommending places in the case of main tourist tours, whereas it has prob- lems in the case of very short and targeted visit. Moreover, when we exploit context to filter the tourist locations for personalized recommendations, it outperforms all baseline recommendations methods. It shows that we can increase the recommendation accuracy or Figure 6. Benefit ratio of proposed personalized context-aware recommendation method over baseline methods. 6.3.3.3. Mean average precision. Mean average precision (MAP@n) is a widely used evaluation metric to measure the ranking effectiveness that is mean over the precision values after each correct recommendation in the top-n. To calculate MAP@50, we rec- ommend 50 locations considering each visit made by each test user in test city as a query and the location visited as one relevant item. We get average precision (AP) for each query AP = 1/r, where r is the position of relevant item in ranked list. We obtain the MAP using Equation (6): Figure 7. Ranking ability of different methods in terms of MAP@S50 across different number of test locations per user. Table 8. Performance of personalized ranking with different context parameters.
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