Recommender systems assist users with the search of products, providing suggestions for items to purchase by taking into consideration their unique requirements. Critique-based recommenders are useful in scenarios where users are likely to search for and buy products for the first time, as they guide users through a product space in pursuit of suitable products with a cyclical recommendation process. In the literature, the proposals made differ mainly in the source of data and in how this data is analyzed to extract knowledge for providing users with recommendations. However, to date, most of the proposals only use a source of data and none of them consider to additionally integrate data from the collaborative interactions between users. In this paper, we propose a new critique-based recommender, called History and Opinion Collaborative Recommender (HOCR), that integrates both the social data and the knowledge extracted from the online collaborative users’ interactions. The results demonstrate that this integration significantly increases the performance of the recommendation process, i.e., users take less time in achieving the desired item.