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Despite improvements in their capabilities, search engines still fail to provide users with only relevant results. One reason is that most search engines implement a “one size fits all” approach that ignores personal preferences when retrieving the results of a user's query. Recent studies (Smyth, 2010) have elaborated the importance of personalizing search results and have proposed integrating recommender system methods for enhancing results using contextual and extrinsic information that might indicate the user's actual needs. In this article, we review recommender system methods used for personalizing and improving search results and examine the effect of two such methods that are merged for this purpose. One method is based on collaborative users' knowledge; the second integrates information from the user's social network. We propose new methods for collaborative‐and social‐based search and demonstrate that each of these methods, when separately applied, produce more accurate search results than does a purely keyword‐based search engine (referred to as “standard search engine”), where the social search engine is more accurate than is the collaborative one. However, separately applied, these methods do not produce a sufficient number of results (low coverage). Nevertheless, merging these methods with those implemented by standard search engines overcomes the low‐coverage problem and produces personalized results for users that display significantly more accurate results while also providing sufficient coverage than do standard search engines. The improvement, however, is significant only for topics for which the diversity of terms used for queries among users is low. © 2011 Wiley Periodicals, Inc.