Method of Forming Recommendations Using Temporal Constraints in a Situation of Cyclic Cold Start of the Recommender System

The problem of the formation of the recommended list of items in the situation of cyclic cold start of the recommendation system is considered. This problem occurs when building recommendations for occasional users. The interests of such consumers change significantly over time. These users are considered “cold” when accessing the recommendation system. A method for building recommendations in a cyclical cold start situation using temporal constraints is proposed. Temporal constraints are formed on the basis of the selection of repetitive pairs of actions for choosing the same objects at a given level of time granulation. Input data is represented by a set of user choice records. For each entry, a time stamp is indicated. The method includes the phases of the formation of temporal constraints, the addition of source data using these constraints, as well as the formation of recommendations using the collaborative filtering algorithm. The proposed method makes it possible, with the help of temporal constraints, to improve the accuracy of recommendations for “cold” users with periodic changes in their interests

Are Item Attributes a Good Alternative to Context Elicitation in Recommender Systems?

International audienceContext-aware recommendation became a major topic of interest within the recommender systems community as the context is crucial to provide the right items at the right moment. Many studies aim at developing complex models to include contextual factors in the recommendation process. Despite a real improvement on the recommendations quality, such contextual factors face users' privacy and data collection issues. We support the idea that context could be expressed in term of item attributes rather than contextual factors. To investigate that hypothesis, we designed an online experiment where 174 users were asked to describe the context in which they would listen the proposed songs for which we collected 12 musical attributes. We make available all the material collected during this study for research purposes and non-commercial use

User-Oriented Preference Toward a Recommender System

            في الوقت الحاضر، من الملائم لنا استخدام محرك بحث للحصول على المعلومات المطلوبة. لكن في بعض الأحيان يسيء فهم المعلومات بسبب التقارير الإعلامية المختلفة. نظام التوصية (RS) شائع الاستخدام في كل الأعمال لأنه يمكن أن يوفر معلومات للمستخدمين التي ستجذب المزيد من الإيرادات للشركات. ولكن أيضًا ، في بعض الأحيان ، يوصي النظام المستخدمين بالمعلومات غير الضرورية. لهذا السبب ، قدم هذا البحث بنية لنظام التوصية التي يمكن أن تستند إلى التفضيل الموجه للمستخدم. هذا النظام يسمى UOP-RS. لجعل UOP-RS بشكل كبير، ركزهذا البحث على معلومات السينما وتجميع قاعدة بيانات الأفلام من موقع IMDb الذي يوفر معلومات متعلقة بالأفلام والبرامج التلفزيونية ومقاطع الفيديو المنزلية وألعاب الفيديو والمحتوى المتدفق الذي يجمع أيضًا العديد من التقييمات والمراجعات من المستخدمين. حلل البحث أيضًا بيانات المستخدم الفردي لاستخراج ميزات المستخدم. بناءً على خصائص المستخدم ، وتقييمات / درجات الفيلم ، ونتائج الأفلام ، تم بناء نموذج UOP-RS. في تجربتنا ، تم استخدام 5000 مجموعة بيانات أفلام IMDb و 5 أفلام موصى بها للمستخدمين. تظهر النتائج أن النظام يمكنه إرجاع النتائج في 3.86 ثانية ولديه خطأ 14٪ على السلع الموصى بها عند تدريب البيانات على أنها K = 50. في نهاية هذه الورقة خلص إلى أن النظام يمكن أن يوصي بسرعة مستخدمي السلع التي يحتاجون إليها. سوف يمتد النظام المقترح للاتصال بنظام Chatbot بحيث يمكن للمستخدمين جعل الاستعلامات أسرع وأسهل من هواتفهم في المستقبل.            Nowadays, it is convenient for us to use a search engine to get our needed information. But sometimes it will misunderstand the information because of the different media reports. The Recommender System (RS) is popular to use for every business since it can provide information for users that will attract more revenues for companies. But also, sometimes the system will recommend unneeded information for users. Because of this, this paper provided an architecture of a recommender system that could base on user-oriented preference. This system is called UOP-RS. To make the UOP-RS significantly, this paper focused on movie theatre information and collect the movie database from the IMDb website that provides information related to movies, television programs, home videos, video games, and streaming content that also collects many ratings and reviews from users. This paper also analyzed individual user data to extract the user’s features. Based on user characteristics, movie ratings/scores, and movie results, a UOP-RS model was built. In our experiment, 5000 IMDb movie datasets were used and 5 recommended movies for users. The results show that the system could return results on 3.86 s and has a 14% error on recommended goods when training data as . At the end of this paper concluded that the system could quickly recommend users of the goods which they needed.  The proposed system will extend to connect with the Chatbot system that users can make queries faster and easier from their phones in the future