سيستم توصيه گر زمينه آگاه سفر با بهرهگيری از اطلاعات عکسهای برچسبدار جغرافيايی
محورهای موضوعی : عمومى
رضوان محمدرضایی لرکی
1
(گروه مهندسي كامپيوتر، واحد تهران مركزي، دانشگاه آزاد اسلامي، تهران)
رضا روانمهر
2
(گروه مهندسي كامپيوتر، واحد تهران مركزي، دانشگاه آزاد اسلامي، تهران )
میلاد امرالهی
3
(گروه مهندسي كامپيوتر، واحد تهران مركزي، دانشگاه آزاد اسلامي، تهران )
کلید واژه: سيستم توصيه‌گر زمینه آگاه, سیستم توصیهگر سفر, زمينه, الگوریتم رقابت استعماری, خوشه¬بندی C-Means فازی,
چکیده مقاله :
سيستم‌هاي توصیهگر بر اساس اطلاعات کاربران و آیتم‌ها، سلایق و علاقه‌مندی‌های مورد نظر کاربران را پیش‌بینی کرده و بر اساس آنها آیتم‌های مناسب را به کاربران پیشنهاد می‌دهند. اکثر رويکردهاي موجود براي سيستم‌هاي توصيه‌گر بر روی پیشنهاد دادن مرتبط‌ترین آیتم‌ها تمرکز می‌کنند و اطلاعات زمينه‌اي مانند زمان یا مکان را در نظر نمی‌گیرند. در اين مقاله يک روش برای سیستم توصيه‌گر زمينه آگاه سفر با بهرهگيري از اطلاعات عکس‌هاي برچسب دار جغرافيايي ارایه شده است که از دقت بالايي برخوردار است. این روش نسبت به مقالات مشابه، تعداد زمينه‌هاي بيشتری مانند (وضعيت آب و هوا، وضعيت روحي کاربر، ميزان ترافيک و ...) را در نظر می‌گیرد. با این کار باعث نزديک شدن کاربران يک خوشه و در نتیجه افزايش دقت می‌شود. روش پیشنهادی به جای استفاده از الگوریتم خوشه‌بندي کلاسيک از ادغام دو الگوريتم رقابت استعماري و خوشه‌بندي C-Means فازی استفاده می‌کند. ارزيابي روش ارايه شده بر روی مجموعه داده فليکر انجام شده و نتايج ارزيابي حاکي از آن است که روش پيشنهادي قادر به فراهم کردن توصيه‌هاي موثر و دقیق مطابق با علاقه‌مندی‌های کاربر و موقعيت فعلي بازديد او مي‌باشد. رویکرد پيشنهادي در معیارهای دقت، متوسط مطلق دقت و میانگین قدر مطلق خطا به ترتیب به 0.69 و 0.53 و 0.31 دست یافته است.
Recommender systems are the systems that help users find and select their target items. Most of the available events for recommender systems are focused on recommending the most relevant items to the users and do not include any context information such as time, location . This paper is presented by the use of geographically tagged photo information which is highly accurate. The distinction point between this thesis and other similar articles is that this paper includes more context (weather conditions, users’ mental status, traffic level, etc.) than similar articles which include only time and location as context. This has brought the users close to each other in a cluster and has led to an increase in the accuracy. The proposed method merges the Colonial Competitive Algorithm and fuzzy clustering for a better and stronger processing against using merely the classic clustering and this has increased the accuracy of the recommendations. Flickr dataset is used to evaluate the presented method. Results of the evaluation indicate that the proposed method can provide location recommendations proportionate to the users’ preferences and their current visiting location.
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