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Recommendation systems get expanding significance because of their applications in both the scholarly community and industry. With the development of additional data sources and methods of extracting new information other than the rating history of clients on items, hybrid recommendation algorithms, in which some methods have usually been combined to improve performance, have become pervasive. In this work, we first introduce a novel method to extract the implicit relationship between content features using a sort of well-known methods from the natural language processing domain, namely Word2Vec. In contrast to the typical use of Word2Vec, we utilize some features of items as words of sentences to produce neural feature embeddings, through which we can calculate the similarity between features. Next, we propose a novel content-based recommendation system that employs the relationship to determine vector representations for items by which the similarity between items can be computed (RELFsim). Our evaluation results demonstrate that it can predict the preference a user would have for a set of items as good as pure collaborative filtering. This content-based algorithm is also embedded in a pure item-based collaborative filtering algorithm to deal with the cold-start problem and enhance its accuracy. Our experiments on a benchmark movie dataset corroborate that the proposed approach improves the accuracy of the system.