بهبود دقت سيستمهای پیشنهاددهنده با تخمین اعتماد آگاه از زمان، مکان و زمینه بر اساس خوشه بندی و توزیع بتا
محورهای موضوعی : مهندسی برق و کامپیوتر
سمانه شیبانی
1
(گروه مهندسي كامپيوتر، دانشگاه آزاد اسلامی واحد مشهد)
حسن شاکری
2
(گروه مهندسي كامپيوتر، دانشگاه آزاد اسلامی واحد مشهد)
رضا شیبانی
3
(گروه مهندسی کامپیوتر دانشگاه آزاد اسلامی واحد مشهد)
کلید واژه: اعتماد, پيشنهاد آگاه از زمينه, توزيع بتا, خوشهبندي, سيستمهاي پيشنهاددهنده,
چکیده مقاله :
در دهههاي اخير رويكرد محاسبه و اعمال اعتماد بين كاربران در طراحي سيستمهاي پيشنهاددهنده مورد توجه محققان قرار گرفته است. با وجود اين، اغلب سيستمهاي پيشنهاددهنده مبتني بر اعتماد فقط از يك فاكتور براي تخمين مقدار اعتماد استفاده ميكنند. در اين مقاله يك رويكرد چندفاكتوري براي تخمين اعتماد بين كاربران سيستمهاي پيشنهاددهنده ارائه ميشود. در طرح پيشنهادي، ابتدا كاربران سيستم براساس شباهت مبتني بر اطلاعات دموگرافيك و تاريخچه ارزشيابيها خوشهبندي ميشوند. براي تخمين ارزشيابي كاربر فعال به يك آيتم خاص، مقدار اعتماد بين او و ساير كاربران همخوشهاش با درنظرگرفتن فاكتورهاي زمان، مكان، و زمينه ارزشيابي محاسبه ميشود. براي اين منظور، ما الگوريتمي مبتني بر توزيع بتا معرفي ميكنيم. يك معيار مبتني بر درخت جديد براي محاسبه شباهت معنايي بين زمينهها مورد استفاده قرار ميگيرد. در نهايت، ارزشيابي كاربر فعال با استفاده از ميانگينگيري وزني تخمين زده ميشود كه مقادير اعتماد به عنوان وزن در ميانگينگيري منظور ميشوند. طرح پيشنهادي بر روي سه مجموعهداده مطرح اجرا شده و ارزيابي و مقايسه نشان ميدهد كه اين طرح نتايج بهتري از نظر ملاكهاي دقت و كارآمدي نسبت به روشهاي موجود ارائه ميكند.
Calculation and applying trust among users has become popular in designing recommender systems in recent years. However, most of the trust-based recommender systems use only one factor for estimating the value of trust. In this paper, a multi-factor approach for estimating trust among users of recommender systems is introduced. In the proposed scheme, first, users of the system are clustered based on their similarities in demographics information and history of ratings. To predict the rating of the active user into a specific item, the value of trust between him and the other users in his cluster is calculated considering the factors i.e. time, location, and context of their rating. To this end, we propose an algorithm based on beta distribution. A novel tree-based measure for computing the semantic similarity between the contexts is utilized. Finally, the rating of the active user is predicted using weighted averaging where trust values are considered as weights. The proposed scheme was performed on three datasets, and the obtained results indicated that it outperforms existing methods in terms of accuracy and other efficiency metrics.
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