Antje Marx | Tuesday August 14th, 2018
Recommender systems have proven effective to help the audience of streaming platforms finding relevant movies and serials hidden in huge amount of available content. The question is how can the audience be assisted in finding the most interesting movies matching the individual user preferences. Popular platforms, like Netflix, Pandora, and Amazon successfully provide recommender systems. Collaborative filtering based approach (item-based and user-based) are popular to provide recommendations.
Standard recommendation approaches have several weaknesses in niche market due to the user data sparsity, missing ratings, and highly specific items. Therefore, customized recommendation strategies are needed, adapting to the specific system features while catering users’ demand. And such customization can take place when traditional recommender algorithms tailored to the special items format and user experience in the corresponding niche markets, thereby add value to the specific system or platform.
This has been the motivation for the cooperation between EYZ Media and the DAI-Labor of the TU Berlin.
The collaboration between EYZ Media and DAI-Lab in year 2018 considers both traditional recommendation strategies (content-based approach and collaborative filtering approach) and modern event-based approach (introducing domain relevant trend in heterogeneous system resource like Twitter). Obeying the API and dataflow definition from EYZ Media, DAI-Labor provides the support on recommendation solution by building up elasticsearch service, collaborative filtering component and event-based recommender pipeline. Corresponding evaluation turned out from both experts’ subjective opinion and offline evaluation on specific metrics help us better understand how recommenders booster user activities in the niche market.
In the following up tech blog articles, detailed Tech explanation of the recommender system built together by realeyz and DAI-Labor will be introduced one by one.
The DAI-Labor (Distributed Artificial Intelligence Laboratory) at the TU Berlin conducts research and development projects in order to provide solutions for a new generation of artificial intelligence systems and services. The competence center Information Retrieval and Machine Learning (IRML) focuses on developing innovative recommender systems and information retrieval systems combining machine learning techniques, graph-based approaches and social media analysis.
EYZ Media operates the VOD platform realeyz, on the web at realeyz.de, as Prime Video Channels and as an app in the stores. Thanks to its focus on independent film, realeyz has built up a growing user base and is one of the top 3 VOD specialty providers in Germany. RealEYZ stands for intelligent, exciting, classic and innovative content in all formats and lengths, curated carefully and with passion. Every day a new film, all in its original version with a selection of 1,000+ titles – and the focus on indie movies appeals above all to young, metropolitan users. The use of many unique components created by EYZ’s own dev. Team such as the recommender system with its algorithms that enable individual and personal UX as well as realeyz’’s presence on different channels and technical environments sharpen the profile, strengthen customer loyalty and establish the realeyz brand as the essential hub for independent productions. RealEYZ is supported by the EU-CREATIVE program and is part of the EuroVoD network.
Supported by Investitonsbank Berlin
We´d shortly like to introduce you to the project team:
Andreas Lommatzsch works as a senior researcher at the Distributed Artificial Intelligence Lab (DAI-Labor) at the TU Berlin. His research focuses on distributed knowledge management and machine learning algorithms. His primary interests lie in the areas of recommendations based on data-streams and context-aware meta-recommender algorithms.
Jing Yuan is a Ph.D. student working at Distributed Artificial Intelligence Lab (DAI-Labor) in TU Berlin. Her research interest includes recommender system, information retrieval, and machine learning algorithms.
Phani Saripalli works as a Data Engineer at EYZ Media GmbH (operator of realeyz.de) and is coordinating the project on site. He is specialized in building data pipelines and data wrangling. He works with Redis, AWS, Airflow, Flask, Python and Postgres to ensure data is transformed from its raw form to something that is insightful.
Khalit Hartmann is a Bachelor of Computer Science (Informatik) student working at Distributed Artificial Intelligence Lab (DAI-Labor) in TU Berlin. His current fields of research include recommender systems based on natural language processing and machine learning algorithms.