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The university’s libraries have always had too many resources to be found by readers so the value of many books has been reduced for missing prescription. Although there have already been a lot of book recommendation researches in university library, few studies have been carried out in respect of the fact how to recommend the new-come books. New books should be more valuable to be recommended, but often overlooked by readers in the recommendation systems because of low weight. To solve this problem, a new-come book recommendation algorithm has been presented to help readers find their interested new books in time and improve all the new books’ value in use. Through extracting the feature of new books and analyzing readers’ borrowing behavior, effort have been devoted to get the readers’ borrowing interest and the popular books recently, and calculate the similarity between books and readers through feature extraction so as to find out the new books which maybe popular and recommend them to all readers timely, and find right readers for other new books. With experiments carried out by using historical data of Southwest University Library, to some extent, it shows that the algorithm can find the new-come books which comply with the popular feature or may be interested by some readers, then get the personalized recommendation result effectively.

A New-come Book Recommendation Algorithm Based on Features in University‘s Library