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As one of the most common user interactive behaviors in many social media services, mention plays a significant role in both user interaction and information cascading. While an increasing line of work has focused on analyzing the mention mechanism for information diffusion, the essential problem of mentionee recommendation from the perspective of common users, i.e., how to find mentionees (mentioned users) who are most likely to be notified by a mentioner (mentioning user) for knowing a post, has been seldom investigated. This paper aims to develop personalized recommendation techniques to automatically generate mentionees when a user intends to mention others in a post. After analyzing real-world social media datasets we observe that users' mention behaviors are influenced by not only the semantic but also the spatial context factors of their mentioning activities, which motivate the needs for spatial context-aware user mention behavior modeling. In light of these, we proposed a joint probabilistic model, named Spatial COntext-aware Mention behavior Model (SCOMM), to simulate the process of generating users' location-tagged mentioning activities. By exploiting the semantic and spatial context factors in a unified way, SCOMM was able to reveal users' preferences behind their mention behaviors and provide a knowledge model for accurate mentionee recommendations. Furthermore, we designed an Item-Attribute Pruning (IAP) algorithm to overcome the curse of dimensionality and facilitate online top-k query performance. Extensive experiments were conducted on two real-world datasets to evaluate the performance of our methods. The experimental results demonstrated the superiority of our approach by making more effective and efficient recommendations compared with other state-of-the-art methods., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

We recently designed and deployed a metasearch engine, Metta, that sends queries and retrieves search results from five leading biomedical databases: PubMed, EMBASE, CINAHL, PsycINFO and the Cochrane Central Register of Controlled Trials. Because many articles are indexed in more than one of these databases, it is desirable to deduplicate the retrieved article records. This is not a trivial problem because data fields contain a lot of missing and erroneous entries, and because certain types of information are recorded differently (and inconsistently) in the different databases. The present report describes our rule-based method for deduplicating article records across databases and includes an open-source script module that can be deployed freely. Metta was designed to satisfy the particular needs of people who are writing systematic reviews in evidence-based medicine. These users want the highest possible recall in retrieval, so it is important to err on the side of not deduplicating any records that refer to distinct articles, and it is important to perform deduplication online in real time. Our deduplication module is designed with these constraints in mind. Articles that share the same publication year are compared sequentially on parameters including PubMed ID number, digital object identifier, journal name, article title and author list, using text approximation techniques. In a review of Metta searches carried out by public users, we found that the deduplication module was more effective at identifying duplicates than EndNote without making any erroneous assignments.

Presents three algorithms for processing multidatabase system and their input and output cost. Difference between the three algorithms; Results of the simulation results; Input and output cost analysis.