Reciprocal Recommender Systems (RRS) recommend users to other users in a personalised manner, in scenarios where both sides of the preference relation must be considered. Existing RRS approaches based on collaborative filtering or content-based filtering, have been used for enhancing user experience in online dating and other online services aimed at connecting users with each other. However, some of these services e.g. skill sharing platforms, are still pervaded by content published, shared and consumed by users, consequently there is a valuable source of item-to-user preferential information not captured by existing RRS models. We present a novel hybrid RRS framework that integrates user preferences towards content in reciprocal recommendation, and we instantiate and evaluate it using data from Cookpad, a recipe sharing social media platform. As part of our model, we also implement a novel content-based extension of Jaccard similarity measure that operates on word embeddings.