Exploiting Synergy Between Ontologies and Recommender Systems

Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no initial information is available early on upon which to base recommendations. Semantic knowledge structures, such as ontologies, can provide valuable domain knowledge and user information. However, acquiring such knowledge and keeping it up to date is not a trivial task and user interests are particularly difficult to acquire and maintain. This paper investigates the synergy between a web-based research paper recommender system and an ontology containing information automatically extracted from departmental databases available on the web. The ontology is used to address the recommender systems cold-start problem. The recommender system addresses the ontology's interest-acquisition problem. An empirical evaluation of this approach is conducted and the performance of the integrated systems measured

RepSeq-A database of amino acid repeats present in lower eukaryotic pathogens

BACKGROUND Amino acid repeat-containing proteins have a broad range of functions and their identification is of relevance to many experimental biologists. In human-infective protozoan parasites (such as the Kinetoplastid and Plasmodium species), they are implicated in immune evasion and have been shown to influence virulence and pathogenicity. RepSeq http://repseq.gugbe.com is a new database of amino acid repeat-containing proteins found in lower eukaryotic pathogens. The RepSeq database is accessed via a web-based application which also provides links to related online tools and databases for further analyses. RESULTS The RepSeq algorithm typically identifies more than 98% of repeat-containing proteins and is capable of identifying both perfect and mismatch repeats. The proportion of proteins that contain repeat elements varies greatly between different families and even species (3 - 35% of the total protein content). The most common motif type is the Sequence Repeat Region (SRR) - a repeated motif containing multiple different amino acid types. Proteins containing Single Amino Acid Repeats (SAARs) and Di-Peptide Repeats (DPRs) typically account for 0.5 - 1.0% of the total protein number. Notable exceptions are P. falciparum and D. discoideum, in which 33.67% and 34.28% respectively of the predicted proteomes consist of repeat-containing proteins. These numbers are due to large insertions of low complexity single and multi-codon repeat regions. CONCLUSION The RepSeq database provides a repository for repeat-containing proteins found in parasitic protozoa. The database allows for both individual and cross-species proteome analyses and also allows users to upload sequences of interest for analysis by the RepSeq algorithm. Identification of repeat-containing proteins provides researchers with a defined subset of proteins which can be analysed by expression profiling and functional characterisation, thereby facilitating study of pathogenicity and virulence factors in the parasitic protozoa. While primarily designed for kinetoplastid work, the RepSeq algorithm and database retain full functionality when used to analyse other species

TailoredRE: A Personalized Cloud-based Traffic Redundancy Elimination for Smartphones

The exceptional rise in usages of mobile devices such as smartphones and tablets has contributed to a massive increase in wireless network trac both Cellular (3G/4G/LTE) and WiFi. The unprecedented growth in wireless network trac not only strain the battery of the mobile devices but also bogs down the last-hop wireless access links. Interestingly, a signicant part of this data trac exhibits high level of redundancy in them due to repeated access of popular contents in the web. Hence, a good amount of research both in academia and in industries has studied, analyzed and designed diverse systems that attempt to eliminate redundancy in the network trac. Several of the existing Trac Redundancy Elimination (TRE) solutions either does not improve last-hop wireless access links or involves inecient use of compute resources from resource-constrained mobile devices. In this research, we propose TailoredRE, a personalized cloud-based trac redundancy elimination system. The main objective of TailoredRE is to tailor TRE mechanism such that TRE is performed against selected applications rather than application agnostically, thus improving eciency by avoiding caching of unnecessary data chunks. In our system, we leverage the rich resources of the cloud to conduct TRE by ooading most of the operational cost from the smartphones or mobile devices to its clones (proxies) available in the cloud. We cluster the multiple individual user clones in the cloud based on the factors of connectedness among users such as usage of similar applications, common interests in specic web contents etc., to improve the eciency of caching in the cloud. This thesis encompasses motivation, system design along with detailed analysis of the results obtained through simulation and real implementation of TailoredRE system

Exploiting synergy between ontologies and recommender systems

Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no initial information is available early on upon which to base recommendations.Semantic knowledge structures, such as ontologies, can provide valuable domain knowledge and user information. However, acquiring such knowledge and keeping it up to date is not a trivial task and user interests are particularly difficult to acquire and maintain. This paper investigates the synergy between a web-based research paper recommender system and an ontology containing information automatically extracted from departmental databases available on the web. The ontology is used to address the recommender systems cold-start problem. The recommender system addresses the ontology's interest-acquisition problem. An empirical evaluation of this approach is conducted and the performance of the integrated systems measured