یک سامانه پیشنهاددهنده اجتماعی مبتنی بر تجزیه ماتریس با در‌نظر‌گرفتن پویایی علایق کاربران

در سال‌های اخیر، استفاده از سامانه‌‌های پیشنهاددهنده در شبکه‌های اجتماعی رشد قابل توجهی داشته است. در این سامانه‌‌ها، رفتار و علایق کاربران در طول زمان تغییر می‌کند و تطبیق سامانه‌های پیشنهاددهنده با این پویایی علایق و نیازهای کاربران به‌منظور ارائه پیشنهادات دقیق‌تر به کاربران ضروری است. علی‌رغم اهمیت این موضوع، اغلب سامانه‌‌های پیشنهاددهنده، رفتار پویای کاربر را در نظر نمی‌گیرند. در این مقاله، یک سامانه پیشنهاددهنده اجتماعی با در‌نظر‌گرفتن پویایی علایق کاربران ارائه می‌شود که از روش تجزیه ماتریس استفاده می‌کند. در مدل پیشنهادی با در‌نظر‌گرفتن این‌که هر کاربر الگوی تغییر علایق خاص خود را دارد، فرض می‌شود که علایق فعلی کاربر به علایق او در دوره زمانی قبلی بستگی دارد، و یک ماتریس انتقال علایق برای هر کاربر به‌منظور مدل‌کردن پویایی علایق کاربر بین دو دوره متوالی آموزش داده می‌شود و با ترکیب امتیازاتِ کاربران و اعتماد بین آن‌ها بر اساس روش تجزیه ماتریس، امتیازاتِ کاربران به اقلام پیش‌بینی می‌شود. ارزیابی‌ها بر روی مجموعه داده epinions نشان می‌دهند که مدل پیشنهادی نسبت به روش‌های مقایسه‌شده، منجر به بهبود بیشتر دقت در پیش‌بینی امتیازات می‌‌شود. همچنین تحلیل پیچیدگی زمانی مدل پیشنهادی بیان‌گر مقیاس‌پذیر‌بودن این مدل است.

A social recommender system based on matrix factorization considering dynamics of user preferences

With the expansion of social networks, the use of recommender systems in these networks has attracted considerable attention. Recommender systems have become an important tool for alleviating the information that overload problem of users by providing personalized recommendations to a user who might like based on past preferences or observed behavior about one or various items. In these systems, the users rsquo; behavior is dynamic and their preferences change over time for different reasons. The adaptability of recommender systems to capture the evolving user preferences, which are changing constantly, is essential. Recent studies point out that the modeling and capturing the dynamics of user preferences lead to significant improvements in recommendation accuracy. In spite of the importance of this issue, only a few approaches recently proposed that take into account the dynamic behavior of the users in making recommendations. Most of these approaches are based on the matrix factorization scheme. However, most of them assume that the preference dynamics are homogeneous for all users, whereas the changes in user preferences may be individual and the time change pattern for each user differs. In addition, because the amount of numerical ratings dramatically reduced in a specific time period, the sparsity problem in these approaches is more intense. Exploiting social information such as the trust relations between users besides the users rsquo; rating data can help to alleviate the sparsity problem. Although social information is also very sparse, especially in a time period, it is complementary to rating information. Some works use tensor factorization to capture user preference dynamics. Despite the success of these works, the processing and solving the tensor decomposition is hard and usually leads to very high computing costs in practice, especially when the tensor is large and sparse.In this paper, considering that user preferences change individually over time, and based on the intuition that social influence can affect the users rsquo; preferences in a recommender system, a social recommender system is proposed. In this system, the users rsquo; rating information and social trust information are jointly factorized based on a matrix factorization scheme. Based on this scheme, each users and items is characterized by a sets of features indicating latent factors of the users and items in the system. In addition, it is assumed that user preferences change smoothly, and the user preferences in the current time period depend on his/her preferences in the previous time period. Therefore, the user dynamics are modeled into this framework by learning a transition matrix of user preferences between two consecutive time periods for each individual user. The complexity analysis implies that this system can be scaled to large datasets with millions of users and items. Moreover, the experimental results on a dataset from a popular product review website, Epinions, show that the proposed system performs better than competitive methods in terms of MAE and RMSE.