  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.
