Building a food recommendation engine with Spark / MLlib and Play

Recommendation engines have become very popular in the last decade with the explosion of e-commerce, on demand music and movie services, dating sites, local reviews, news aggregation and advertising (behavioral targeting, intent targeting, …). Depending on your past actions (e.g., purchases, reviews left, pages visited, …) or your interests (e.g., Facebook likes, Twitter follows), the recommendation engine will present other products that might interest you using other users actions and user behaviors (page clicks, page views, time spent on page, clicks on images/reviews, …).

In this post, we’re going to implement a recommender for food using Apache Spark and MLlib. So for instance, if one is interested by some coffee products then we might recommend her some other coffee brands, coffee filters or some related products that some other users like too.

Segmenting Audience with KMeans and Voronoi Diagram using Spark and MLlib

Analyzing huge data set to extract meaningful properties can be a difficult task. Several methods have been developed for the last 50 years to find hidden information.

Clustering algorithms can be used to group similar news like in Google News, find areas with high crime concentration, find trends, .. and segment the data into groups. This segmentation can be used for instance by publisher to reach a specific target audience.

In this post, we will be using the k-means clustering algorithm implemented in Spark Machine Learning Library(MLLib) to segment the dataset by geolocation .

The k-mean clustering algorithm is an unsupervised algorithm meaning that you don’t need to provide a training example for it to work(unlike neural network, SVM, Naives Bayes classifiers, …). It partitions observations into clusters in which each observation belongs to the cluster with the nearest mean. The algorithm takes as input the observations, the number of clusters(denoted k) that we want to partition the observation into and the number of iterations. It gives as a result the centers of the clusters.

The algorithm works as follow:

1. Take k random observations out of the dataset. Set the k centers of the clusters to those points
2. For each observation, find the cluster center which is the closest and assign this observation to this cluster
3. For each cluster, compute the new center by taking the average of the features of the observations assigned to this dataset
4. Go back to 2 and repeat this for a given number of iterations

The centers of the clusters will converge and will minimize the cost function which is the sum of the square distance of each observation to their assigned cluster centers.

This minimum might be a local optimum and will depend on the observation that were randomly taken at the beginning of the algorithm.

Implementing a real-time data pipeline with Spark Streaming

Real-time analytics has become a very popular topic in recent years. Whether it is in finance (high frequency trading), adtech (real-time bidding), social networks (real-time activity), Internet of things (sensors sending real-time data), server/traffic monitoring, providing real-time reporting can bring tremendous value (e.g., detect potential attacks on network immediately, quickly adjust ad campaigns, …). Apache Storm is one of the most popular frameworks to aggregate data in real-time but there are also many others such as Apache S4, Apache Samza, Akka Streams, SQLStream and more recently Spark Streaming.

According to Kyle Moses, on his page on Spark Streaming, it can process about 400,000 records / node / second for simple aggregations on small records and significantly outperforms other popular streaming systems such as Apache Storm (40x) and Yahoo S4 (57x). This can be mainly explained because Apache Storm processes messages individually while Apache Spark groups messages in small batches. Moreover in case of failure, where in Storm messages can be replayed multiple times, Spark Streaming batches are only processed once which greatly simplifies the logic (e.g., to make sure some values are not counted multiple times).

At a higher level, we can see Spark Streaming as a layer on top of Spark where data streams (coming from various sources such as Kafka, ZeroMQ, Twitter, …) can be transformed and batched in a sequence of RDDs (Resilient Distributed DataSets) using a sliding window. These RDDs can then be manipulated using normal Spark operations.

In this post we consider an adnetwork where adservers log impressions in Apache Kafka (distributed publish-subscribe messaging system). These impressions are then aggregated by Spark Streaming into a datawarehouse (here MongoDB to simplify). Usually the data is further aggregated into a fast data access layer (direct lookup) and accessible through an API as depicted in the figure below.

Figure 1. Ad network architecture