An ongoing challenge for researchers is the ability to provide relevant search results to users. This paper attempts to arrive at the ranking score of search results by using the user’s behavior pattern, which is identified from the user’s social networking profile.

Gou et al. present their case for considering user-specific parameters to arrive at the relevance score, in a simple way. Their framework arrives at the relevancy score based on two aspects: one is dependent on the document corpus and its characteristics, which mainly depend on the values of the popular term frequency (tf) and inverse document frequency (idf); the other aspect is dependent on the user’s behavior and his or her contacts on social networking sites such as Facebook, Flickr, and YouTube.

The paper presents a case study based on a simulated social network and dataset from YouTube. The authors use SNDocRank, tf-idf, and cosine similarity to evaluate the search result’s ranking. They claim that the proposed method ranks results better for a given user. Although the work and results are interesting, the authors fail to present two important additional issues. The first is the ability to analyze the social networking aspect of each user--this may not be always correct, as a user might have multiple social network identifications and interests--and the challenge of gathering specific information to enable social network analysis for each new user. The second issue is the computational/communication overhead that will also add latency in presenting the search results. A quick outline of these issues would have added a lot of value to the paper.