توسعه الگوريتم توصيه گر مكان احتمالاتي بر اساس دسته‌بندي مكان در شبكه‌هاي اجتماعي مكان‌مبنا مبتني بر تأثير زماني، تأثير مكاني و تأثير روابط اجتماعي

Devloping a Recommander Location Algorithm Utilizing Temporal Influence, Geographical Influence and Social Influence

يكي از سرويس‌هايي كه در بين كاربران تلفن‌هاي همراه هوشمند با استقبال زيادي روبه‌رو شده است، سرويس‌هاي مبتني بر شبكه‌هاي اجتماعي است. توصيه مكان يكي از سرويس‌هاي محبوب براي شبكه‌هاي اجتماعي مكان‌مبناست. اين سرويس بر اساس پيشينه رفتار كاربر و اطلاعات مكان مانند دسته‌بندي مكان، مكان‌هاي بازديد نشده را به كاربران پيشنهاد مي‌دهد. روش‌هاي موجود كه از اطلاعات ثبت موقعيت كاربر و اطلاعات مكان، استفاده مي‌كنند، تنها تأثير مكاني و تأثير زماني را در توصيه مكان در نظر گرفته‌اند. با توجه به اينكه روابط اجتماعي كاربر مي‌تواند بر عملكرد بهتر توصيه مكان تأثير بگذارد، اين تأثير نيز مي‌تواند نقش مهمي در توصيه مكان ايفا كند. در اين پژوهش، الگوريتم توصيه مكان PCLRTGS با در نظر گرفتن تأثير مكاني، تأثير زماني و تأثير روابط اجتماعي توسعه داده‌شده است. در اين الگوريتم، تأثير مكاني با استفاده از تابع توزيع مكاني مدل‌سازي شده است. همچنين براي مدل‌سازي تأثير زماني، منحني زماني هر كاربر در هر دسته ايجادشده و شباهت اين منحني با منحني‌هاي زماني كاربران ديگر در آن دسته به‌دست‌آمده و اين شباهت براي مدل‌سازي تأثير زماني بكار گرفته‌شده است. تأثير روابط اجتماعي نيز با استفاده از سه معيار شباهت ارتباطات اجتماعي كاربر و دوستانش، شباهت فعاليت‌هاي ثبت موقعيت كاربر و دوستانش و شباهت زماني رفتار ثبت موقعيت بين كاربر و دوستانش، مدل‌سازي شده است. الگوريتم توسعه داده‌شده با استفاده از دو معيار دقت و جامعيت ارزيابي‌شده و با دو الگوريتم PCLR و sPCLR در پنج گروه با تعداد توصيه‌هاي مختلف مقايسه شده است. نتايج نشان مي‌دهد كه الگوريتم پيشنهادي PCLRTGS ازنظر دقت و جامعيت، حدود 15-10 درصد عملكرد بهتري نسبت به دو الگوريتم PCLR و sPCLR دارد.

Social network-based services are among the services that have been welcomed by smart phone users. Location recommendation is a popular service in social network. This service suggested the unvisited places to the users based on the is based on the users’ visiting histories and location related information such as location categories. The existing methods which utilize check-in data and category information, only consider temporal and spatial information. Since the influence of user’s social relations can play an important role in location recommendation since it can improve the algorithm performance. In this paper, a PCLRSGT method is developed that consider temporal, spatial and social components. The spatial component models a user’s probability of checking in to a location. The spatial model obtains user’s home location by using his check-in dataset. It calculates the distance from the location to the user’s home. The spatial PDF filters out those locations that are far away from a user’s home and are not in the users’ interest. These locations should not be recommended to the user. The temporal component employs similar users’ check-in probabilities to model a user’s probability of checking in to a location. It constructs users’ temporal curves to represent a user’s periodic check-in behavior. A User Temporal Curve U for category j is defined as a sequence of probability values. The probability value is denoted as that means probability of checking into category j in hour m (1≤m≤24).The probability sequence for user u into category is denoted as . Since the distances between users temporal curves are used to find users’ similarity, in this paper the distances are measured by curve coupling method. Temporal similarity is used to predict user’s probability of checking in to a location. The periodic behavior of a certain user is predicted by a weighted summation of the periodic behaviors of his similar users. If two users are more similar in terms of temporal similarity, they influence each other's periodic check-in behavior more. The social component models a user’s probability of checking in to a location by considering similarity between user and his friends in terms of social connection, periodic check-in behavior and check-in activities into locations. The social influence weight between two friends is concluded based on all three similarities between a user and his friends in terms of social connection, periodic check-in behavior and check-in activities into locations. Therefore, the social influence weight between two friends is calculated by combining the three above factors. The social influence weight between two friends is used to predict user’s probability of checking in to a location. The dataset employed in this paper was collected from Gowalla. Gowalla was one of the popular location based social network launched in 2007 and closed in 2012. .The data set contains 1000 users and 15905 check-in records. A check-in record indicates a user has visited a location at a given time. It contains the user ID, location ID, and time stamp of the check-in. To evaluate the performance of the recommendation algorithm, the dataset was divided into training and testing datasets. So, one of the check-in records of each user was randomly moved to the testing dataset. The rest of the dataset formed the training dataset. As the result, the testing dataset contained 1000 check-in records, and the training dataset contained 14905 check-in records. In this paper, Precision and Recall were used to evaluate the performance of the location recommendation algorithm, which are widely accepted as the performance measurement for recommender systems.