Heat conduction process has recently found its application in personalized recommendation [T. Zhou et al., PNAS 107, 4511 (2010)], which is of high diversity but low accuracy. By decreasing the temperatures of smalldegree objects, we present an improved algorithm, called biased heat conduction (BHC), which could simultaneously enhance the accuracy and diversity. Extensive experimental analyses demonstrate that the accuracy on MovieLens, Netflix and Delicious datasets could be improved by 43.5%, 55.4% and 19.2% compared with the standard heat conduction algorithm, and the diversity is also increased or approximately unchanged. Further statistical analyses suggest that the present algorithm could simultaneously identify users' mainstream and special tastes, resulting in better performance than the standard heat conduction algorithm. This work provides a creditable way for highly efficient information filtering. PACS numbers: 89.20.Hh, 89.75.Hc, 05.70.Ln With the advent of the Internet [1] and wide application of Web 2.0 techniques, there sprout many web sites that enable large communities to aggregate and interact. For example, Twitter allows its 1.7 × 10 8 members to share interests and life experiences, Facebook has already exceeded 500 million members since July 16th, 2010, and their members are growing ever faster. This brings massive amount of accessible information, more than every individual's ability to process. Searching, filtering and recommending thus become indispensable in the Internet era, in which the personalized recommender systems have become an effective tool to address the information overload problem by predicting users' interests and habits based on their historical records. Personalized recommender systems have been used to recommend books and CDs at Amazon.com, movies at Netflix.com, and news at Versifi Technologies (formerly AdaptiveInfo.com) [2] . Motivated by the practical significance to e-commerce, recommender systems have caught increasing attention and become an essential issue [3, 4] . A personalized recommender system includes three parts: data collection, model analysis and recommender algorithm, where the algorithm is the core part. Thus far, various kinds of algorithms have been proposed, including collaborative filtering (CF) approaches [5] [6] [7] [8] [9] [10] , content-based analyses [11, 12] , tag-aware algorithms [13] [14] [15] , link prediction approaches [16] [17] [18] , hybrid algorithms [19, 20] , and so on. For a review of current progress, see Refs. [2, 21] and the * liujg004@ustc.edu.cn † zhutou@ustc.edu references therein. A recommender system could be described by a bipartite network [22, 23] , in which there are two kinds of nodes: users U and objects O. The users' historical records are represented by the edges connecting users and objects. Supposing there are m objects O = {o 1 , o 2 , · · · , o m } and n users U = {u 1 , u 2 , · · · , u n }, the system can be fully described by an adjacency matrix A = {a lα } m,n , where a lα = 1 if o α is collected by u l , and a lα = 0 otherwise. A reasonable assumption is that objects collected by users are what these users like and a recommendation algorithm aims at predicting users' personal opinions on the objects they have not yet collected [24] [25] [26] . In the standard heat conduction (HC) algorithm, we first construct a propagator matrix W h , where the element w αβ denotes the conduction rate from object o β to o α . Denote H as
HC REF imitates the process of heat conduction, which achieves high diversity but low accuracy.
Information filtering via biased heat conduction
Phys. Rev. E 87 (2011) 037101
Physics Computer Science Medicine