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| Literature Recommendation Using Evolution Patterns |
| Wei Meng1,2 |
1. School of Information Management, Wuhan University, Wuhan 430072, China;
2. Air Force Engineering University Library, Xi'an 710051, China |
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Abstract [Objective] To help users retrieve and read the literature of one topic from the shallower to the deeper. [Context] Literature recommendation service is one of the core businesses in digital library, and it plays an important role in literature searching and querying for the readers. [Methods] This paper introduces a user searching behaviour Common evolution pAtterns based Literature retrievaL method (CALL for short). First, it extracts the features of literature, readers and retrieval logs, then it clusters the literature into n stages, further uses longest common subsequence method to mine the frequent article name sequences that are greater than the thresholds of length and frequency, finally it outputs the frequent subsequences from the above stage as the recommendation results. [Results] The author conducts extensive experiments on real literature and retrieval log datasets, and results demonstrate the accuracy, efficiency and scalability of the methods. And it can enrich the performance of recommendation of digital library. [Conclusions] The proposed methods can greatly enhance the efficiency of the existing literature recommendation systems, and make the direction of literature recommendation be diversified.
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Received: 20 October 2013
Published: 19 May 2014
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