Recommender system based on collaborative filtering (CF) suffers from two basic problems known as cold start and sparse data. Appling metric similarity criteria through matrix factorization is one of the ways to reduce challenge of cold start. However, matrix factorization extract characteristics of user vectors & items, to reduce accuracy of recommendations. Therefore, SSVD two-level matrix design was designed to refine features of users and items through NHUSM similarity criteria, which used PSS and URP similarity criteria to increase accuracy to enhance the final recommendations to users. In addition to compare with common recommendation methods, SSVD is evaluated on two real data sets, IMDB &STS. Experimental results depict that proposed SSVD algorithm performs better than traditional methods of User-CF, Items-CF, and SVD recommendation in terms of precision, recall, F1-measure. Our detection emphasizes and accentuate the importance of cold start in recommender system and provide with insights on proposed solutions and limitations, which contributes to the development. تفاصيل المقالة

Recommender systems are a branch of retrieval systems and information matching, which through identifying the interests and requires of the user, help the users achieve the desired information or service through a massive selection of choices. In recent years, the recommender systems apply describing information in the terms of the user, such as location, time, and task, in order to produce relevant and even customized recommendations. Recently, some companies began to utilize the context information in their search engines. For instance, when choosing a song for the customer, it attempts to include the current mood of the listener in the context of the suggestions that the user makes. Employing context information, in view of the system's access and ability to collect information from the user interface, it offers more precise and user-friendly content that, in addition to obtaining user satisfaction, will also lead to the development and promotion of the field of work and the concept known as context-aware recommender system. In particular, this paper explores the dimensions of research, work areas, architectures, and tools employed and the ability to create a structure that researchers have based on in this area. تفاصيل المقالة