In this paper, we examine methods that can provide accurate results in a form of a recommender system within a social networking framework. The social networking site of choice is Twitter, due to its interesting social graph connections and content characteristics. We built a recommender system which recommends potential users to follow by analyzing their tweets using the CRM114 regex engine as a basis for content classification. The evaluation of the recommender system was based on a dataset generated from real Twitter users created in late 2009.
This paper studies people recommendations designed to help users find known, offline contacts and discover new friends on social networking sites. We evaluated four recommender algorithms in an enterprise social networking site using a personalized survey of 500 users and a field study of 3,000 users. We found all algorithms effective in expanding users' friend lists. Algorithms based on social network information were able to produce better-received recommendations and find more known contacts for users, while algorithms using similarity of user-created content were stronger in discovering new friends. We also collected qualitative feedback from our survey users and draw several meaningful design implications.
An internal combustion engine structure in which the cylinder head and main bearing caps are linked by a framework of members including pairs of tension members each angled obliquely to the direct line between head and caps. The members of the framework are arranged so as to bear a substantial proportion of the firing loads of the engine in tension or compression without bending, thus relieving other parts of the engine, especially its outer surfaces, from loads under which they would tend to bend and thus emit noise. The framework may also serve to strengthen parts of the engine wall to make it less susceptible to bending.