Signal and Data Processing

fa بهره ‌برداری از ژانرهای فیلم و داده‌های کاربران به منظور بهبود سامانه های توصیه فیلم Using movie genres and Demographic Information to improve movie recommendation systems مقالات پردازش داده‌های رقمی Paper پژوهشي Research <div style="text-align: justify;">سامانه‌های توصیه فیلم ابزارهای کارآمدی هستند که به کاربران کمک می‌کنند فیلم‌های مورد علاقه خود را با بررسی علایق قبلی کاربران پیدا کنند. این سامانه‌ها بر اساس امتیاز کاربران به فیلم‌های گذشته و استفاده از آنها برای پیش‌بینی علایق آنها در آینده ایجاد شده‌اند؛ با این حال، امتیازدهی نامناسبی که کاربران ارائه می‌دهند، منجر به ایجاد مشکلی به نام پراکندگی داده ‌می‌شود. این مشکل موجب کاهش کارایی سامانه‌های توصیه فیلم می‌شود. از سوی دیگر، سایر داده‌های موجود مانند ژانر فیلم‌ها و اطلاعات جمعیت‌شناختی کاربران، نقش حیاتی در کمک به روش‌های توصیه‌کننده برای تولید بهتر توصیه‌ها دارند. این مقاله یک روش توصیه فیلم را با استفاده از ژانرهای فیلم و اطلاعات جمعیت‌شناختی کاربران پیشنهاد می‌کند. همچنین ما مدلی کارآمد جهت ارزیابی پروفایل امتیازدهی کاربر و تعیین کمینه امتیاز مورد نیاز برای تولید یک پیش‌بینی دقیق را پیشنهاد می‌کنیم؛ سپس، امتیازات مجازی مناسب با پروفایل‌هایی که امتیازات نامناسبی دارند ترکیب می‌شوند. این امتیازدهی مجازی با استفاده از شباهت مقادیر بین کاربران به‌دست‌آمده از ژانرهای فیلم و اطلاعات جمعیت‌شناختی کاربران محاسبه می‌شوند؛ علاوه بر این، یک معیار مفید برای تعیین میزان قابل‌اعتماد بودن یک بخش معرفی شده‌است که قابلیت اطمینان امتیازدهی مجازی را تضمین می‌کند؛ درنهایت، امتیازهای ناشناخته برای کاربر هدف براساس پروفایل‌های امتیازدهی توسعه‌یافته پیش‌بینی می‌شوند. آزمایش‌های انجام‌شده بر روی دو مجموعه‌داده توصیه فیلم معروف نشان‌می‌دهد که رویکرد پیشنهادی کارآمدتر از سایر توصیه‌کننده‌های مقایسه شده‌است.  </div> <div style="text-align: justify;">Movie recommendation systems are efficient tools to help users find their relevant movies by investigating the previous interests of users. These systems are established on considering the ratings of users provided for movies in the past and using them to predict their interests in the future. However, users mainly provide insufficient ratings leading to make a problem called data sparsity. This problem makes reducing the effectiveness of movie recommendation systems. On the other hand, other available data such as genres of movies and demographic information of users play a vital role in assisting recommenders in order to better produce recommendations. This paper proposes a movie recommendation method utilizing the movies’ genres and users’ demographic information. In particular, we propose an effective model to evaluate the user’s rating profile and determine the minimum number of ratings required to produce an accurate prediction. Then, appropriate virtual ratings are incorporated into the profiles with insufficient ratings to expand them. These virtual ratings are calculated using similarity values between users obtained by genres of movies and demographic information of users. Furthermore, an effective measure is introduced to determine how much an item is reliable. This measure guarantees the virtual ratings’ reliability. Finally, unknown ratings for target user are predicted based on the expanded rating profiles. Experiments performed on two well-known movie recommendation datasets demonstrate that the proposed approach is more efficient than other compared recommenders. We propose a movie recommender system in this paper by employing the genres of movies and demographic information of users to address the above-mentioned challenges. To this end, first of all, a model is developed in order to determine whether the target user’s rating profile is appropriate to produce accurate recommendations or not. In other words, the developed model determines how many ratings are required for each user to generate an accurate prediction with a high probability. This criterion is used to demonstrate that a rating profile contains sufficient ratings for producing reliable recommendations or not. Then, the quality of rating profiles containing insufficient ratings is boosted using an effective profile expansion technique which incorporates some virtual ratings to these profiles. These virtual ratings are calculated using the similarity values between users which are computed according to the genres of movies and demographic information of users. Moreover, the reliability values of users and items are calculated using appropriate reliability measurements to guarantee that the incorporated virtual ratings are reliable. Experimental results on two movie recommendation datasets indicate the superiority of the proposed approach in respect to other models. In the following, we provide a list of the main contributions of this paper:</div> <ul> <li class="CxSpMiddle" style="margin-bottom: 8px; text-align: justify;">We develop a model in order to evaluate the users’ rating profiles and determine how many ratings are required for generating an accurate prediction.</li> <li class="CxSpMiddle" style="margin-bottom: 8px; text-align: justify;">We propose a powerful profile expansion technique which incorporates some virtual ratings to user-item ratings matrix for improving its quality.</li> <li class="CxSpMiddle" style="margin-bottom: 8px; text-align: justify;">Movies’ genres and users’ demographic information are used as additional data in the proposed movie recommender system.</li> <li class="CxSpMiddle" style="margin-bottom: 8px; text-align: justify;">The reliability measures of users and items are used in the proposed method to guarantee the reliability of calculated virtual ratings.</li> <li class="CxSpMiddle" style="margin-bottom: 8px; text-align: justify;">The proposed method generates a denser user-item ratings matrix than the original matrix which results in alleviating data sparsity problem significantly.    </li> </ul> <div style="text-align: justify;">The remaining parts of this paper are structured as follows: in section 2, related works are investigated, section 3 includes the details of the proposed method, section 4 refers to the discussion of experimental results, and section 5 provides some conclusions about the paper Movie recommendation systems are efficient tools to help users find their relevant movies by investigating the previous interests of users. These systems are established on considering the ratings of users provided for movies in the past and using them to predict their interests in the future. However, users mainly provide insufficient ratings leading to make a problem called data sparsity. This problem makes reducing the effectiveness of movie recommendation systems. On the other hand, other available data such as genres of movies and demographic information of users play a vital role in assisting recommenders in order to better produce recommendations. This paper proposes a movie recommendation method utilizing the movies’ genres and users’ demographic information. In particular, we propose an effective model to evaluate the user’s rating profile and determine the minimum number of ratings required to produce an accurate prediction. Then, appropriate virtual ratings are incorporated into the profiles with insufficient ratings to expand them. These virtual ratings are calculated using similarity values between users obtained by genres of movies and demographic information of users. Furthermore, an effective measure is introduced to determine how much an item is reliable. This measure guarantees the virtual ratings’ reliability. Finally, unknown ratings for target user are predicted based on the expanded rating profiles. Experiments performed on two well-known movie recommendation datasets demonstrate that the proposed approach is more efficient than other compared recommenders.</div> سامانه های توصیه, فیلم, شروع سرد, پراکندگی داده ها, اطلاعات جمعیت شناختی, ژانر Recommender systems, movies, cold start, data sparsity, demographic information, genre 89 106 http://jsdp.rcisp.ac.ir/browse.php?a_code=A-10-2358-1&slc_lang=fa&sid=1 samad mohammadi صمد محمدی mohamadi601@gmail.com 100319475328460012578 100319475328460012578 No Department of Computer Engineering, Islamic Azad University, Central Tehran Branch, Tehran, Iran گروه مهندسی کامپیوتر، دانشگاه آزاد اسلامی واحد تهران مرکزی، تهران، ایران vahe aghazarian واهه آغازاریان aghazarian@iauctb.ac.ir 100319475328460012579 100319475328460012579 Yes Department of Computer Engineering, Islamic Azad University, Central Tehran Branch, Tehran, Iran گروه مهندسی کامپیوتر، دانشگاه آزاد اسلامی واحد تهران مرکزی، تهران، ایران alireza hedayati علیرضا هدایتی samad601@gmail.com 100319475328460012580 100319475328460012580 No Department of Computer Engineering, Islamic Azad University, Central Tehran Branch, Tehran, Iran گروه مهندسی کامپیوتر، دانشگاه آزاد اسلامی واحد تهران مرکزی، تهران، ایران