AI for Socializing: Build Recommendation Engines Using PredictionIO

Sanam Malhotra | 10th April 2020

With thriving social interactions, demand for more personalized and user-centric experiences is witnessing a boom. As manual efforts turn redundant, artificial intelligence (AI) and machine learning (ML) become the new powerhouses of online recommendations across social media platforms. Apache’s PredictionIO platform is gaining momentum as a server for building and deploying recommendation systems for digital businesses. As an emerging provider of artificial intelligence services, Oodles AI shares the hands-on experience of building social recommendation engines using PredictionIO.

 

Understanding Apache’s PredictionIO and its Benefits

Apache PredictionIO is an open-source machine learning server specifically designed for developing and deploying predictive engines. The platform serves as an ML stack that enables businesses to evaluate and manage their machine learning infrastructure using these three components-

1) PredictionIO platform- For building, analyzing, and deploying predictive engines using ML algorithms

2) Event Server- For collecting and unifying data from multiple platforms through the ML analytics layer and push for model training

3) Template Gallery- For downloading engine templates used for different ML applications.

recommendation engines using predictionIOImage Source- Google

 

Apart from regular installation, PredictionIO also assists developers in the integration of the ML server with their native applications. For instance, data or queries sent through a web or mobile app to PredictionIO is used for model training and retrieving prediction results. In addition to this, Apache offers a score of other features, such as-

a) Quick and convenient deployment

With customizable templates, developers can efficiently build and deploy predictive engines as a web service. For better results, Apache facilitates the installation with data processing libraries such as Spark MLLib and OpenNLP.

b) Real-time responses

Apache’s robust ML infrastructure accelerates the response rate to dynamic application queries. The platform is scalable to tune multiple engine variants systematically.

c) Comprehensive data collection

Apache matches the big data analytics requirements for businesses by fetching data from multiple sources in batches or in real-time. Also, in-built evaluation measures assist developers to accelerate the ML modeling process significantly.

Also read- Real-time Applications of Machine Learning with Apache Kafka

 

Social Applications of Recommendation Engines Using PredictionIO

Predictions and recommendations generated through PredictionIO can be channelized toward customer-centric services like social media, eCommerce, and chatbot development services. Let’s discuss some other business-oriented applications of recommendation engines powered by PredictionIO-

a) Building Connections

Expanding one’s network is at the core of any social media platform. From Facebook and Twitter to LinkedIn and Reddit, users are enthusiastic about making new friends, building connections, and bonding over common interests.

User data from social engines can be directed toward PredictionIO to churn recommendations about social networking. Its underlying machine learning algorithms fetch user’s activity and demographic data to recommend new connections for better engagement.

b) Churning Updates

Fresh updates, news feeds, and latest trends keep the wheels of social media churning. The more diversified and enriched a news feed, the more user engagement it drives. However, traditional servers often fall short on bandwidth to support a user base in millions.

With machine learning capabilities, PredictionIO streamlines and accelerates the circulation of social media updates and notifications. The real-time server syncs well with queries and data to provide personalized notifications to users for maximum engagement.

c) Recommending Products and Services

Online advertisements are the most crucial source of revenue for social media platforms. It is equally essential to formulate effective targeting strategies that capture the needs and preferences of your target audience.

In light of dynamic customer demands, recommendation engines using PredictionIO assists data analysts to make accurate predictions including-

i) Products and services tracing user’s purchase history

ii) Offers and discounts

iii) Preferred online content including videos, movies, series, and music

iv) Events or exhibitions that users might be interested in.

eCommerce business as well can build and deploy recommendation engines using PredictionIO for optimum user experience and revenue.

Also read- How eCommerce can harness Deep Learning for Recommendation Systems

 

Building Recommendation Engines Using PredictionIO With Oodles AI

We, at Oodles AI, are a team of AI professionals and subject-matter experts who build, manage, and deploy diversified artificial intelligence solutions and technologies. Our hands-on experience with machine learning algorithms and frameworks including PredictionIO enabled us to develop a recommendation engine for socializing purposes.

recommendation engines using PredictionIO

For one of our clients running a socializing bot over Twitter, we built a recommendation engine as a web service. The chatbot provides user-to-user recommendations for bonding and match-making purposes for all Twitter users.

In addition to this, our AI capabilities extend to-

a) Predictive Analytics

b) Chatbot development and integration

c) Recommendation systems for eCommerce and entertainment businesses

d) Natural Language Processing

e) Computer vision services, and more.

Explore our diversified AI services by reaching out to our AI development team.

About Author

Sanam Malhotra

Sanam is a technical writer at Oodles who is currently covering Artificial Intelligence and its underlying disruptive technologies. Fascinated by the transformative potential of AI, Sanam explores how global businesses can harness AI-powered growth. Her writings aim at contributing the multidimensional values of AI, IoT, and machine learning to the digital landscape.

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