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Databases and Information Systems
Sie sind hier: Startseite Teaching Lehrangebot Wintersemester 2017/18 Recommender Systems and Spark SQL
 

Recommender Systems and Spark SQL

Organizers:

Prof. Dr. Georg Lausen
Anas Alzoghbi
Victor Anthony Arrascue Ayala
Matteo Cossu

Organizational Matters:

Introductory Meeting: Wednesday 25.10.2017, 15.00 (c.t.) Download the slides pdf download
Room: Buil. 51, SR 01-029
Language: German / English
HISinOne: We kindly ask prospective participants to apply via HISinOne for participating in this project


Prerequisites:

Specific pre-requisites will be listed for each project. Attendance in the lecture 'Data Analysis and Query Languages' in the summer term is higly recommended.

Project Content

Recommender Systems
The goal of a Recommender System (RS) is to provide useful suggestions for a user and therefore to support her in a decision-making process. These systems are playing an increasingly important role. Many on-line retailers, streaming services and web applications such as Amazon.com, Spotify, Youtube, Netflix, Booking.com, Last.fm, etc. aim to provide personalized content and integrate high quality recommendations in their services. There exists a vast amount of techniques for producing recommendations. The purpose of the RS projects is to make it possible for students to get insights into the details of a specific approach and to understand its fundamental problems. The student should tackle a specific problem combining creativity and competence.

Spark SQL
With the expansion of the Web 2.0, Semantic Web and Social Networks new challenges arise due to the rapidly growing size of such graph structures that require distributed storage and processing strategies. In recent years, Spark has become the most popular framework for distributed, parallel processing of large-scale data. The Spark projects focus on Spark SQL, the component which provides functions for manipulating large sets of distributed, structured data using an SQL subset, whose queries can be evaluated in a distributed fashion. The participants will develop and implement an application on top of Spark for large scale graph processing/analysis. Prior knowledge of Spark is desirable but not required.

Master Project

The master projects have been designed to overcome a specific challenge of recommending items to users. The technique used will then depend on the kind of problem.

For a detail list of the offered projects click here.

Curriculum

Master of Science: 3rd Semester (Teamproject / Masterproject)
ECTS: 16