Abstract
Nowadays, scholar recommender systems often recommend academic papers based on users’ personalized retrieval demands. Typically, a recommender system analyzes the keywords typed by a user and then returns his or her preferred papers, in an efficient and economic manner. In practice, one paper often contains partial keywords that a user is interested in. Therefore, the recommender system needs to return the user a set of papers that collectively covers all the queried keywords. However, existing recommender systems only use the exact keyword matching technique for recommendation decisions, while neglecting the correlation relationships among different papers. As a consequence, it may output a set of papers from multiple disciplines that are different from the user’s real research field. In view of this shortcoming, we propose a keyword-driven and popularity-aware paper recommendation approach based on an undirected paper citation graph, named PRkeyword+pop. At last, we conduct large-scale experiments on the real-life Hep-Th dataset to further demonstrate the usefulness and feasibility of PRkeyword+pop. Experimental results prove the advantages of PRkeyword+pop in searching for a set of satisfactory papers compared with other competitive approaches.
Highlights
With the increasing maturity of recommender systems [1], users are apt to employ existing academic paper recommender websites (e.g., Google Scholar and Baidu Academic) to search for their interested papers based on a set of keywords typed by the users
To recommend a set of satisfactory papers, we propose PRkeyword+pop approach that assists users in searching for a set of satisfactory papers, i.e., these papers cover all queried keywords and have higher
We make the following contributions: (1) We propose a novel keyword-driven and popularityaware paper recommendation approach, which efficiently recommends a set of satisfactory papers
Summary
With the increasing maturity of recommender systems [1], users are apt to employ existing academic paper recommender websites (e.g., Google Scholar and Baidu Academic) to search for their interested papers based on a set of keywords typed by the users. Erefore, a paper recommender system needs to analyze the user’s search requirements to return a set of papers that collectively covers all the queried keywords. E first phase is entering keywords; users analyze their research requirements and enter all query keywords (e.g., k1, k2, k3, and k6) to a recommender system. E third phase is paper selection [4, 5]; the recommender system recommends candidate papers containing query keywords to users. To recommend a set of satisfactory papers, we propose PRkeyword+pop (keywords-driven and popularity-aware paper recommendation) approach that assists users in searching for a set of satisfactory papers, i.e., these papers cover all queried keywords and have higher
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