Recommender systems eventually output a ranking list of items regardless of different modelling choices. So it is important to look at how to evaluate directly ranking quality instead of other proxy metrics like mean squared error, etc.

HR (Hit Ratio)

In recommender settings, the hit ratio is simply the fraction of users for which the correct answer is included in the recommendation list of length L.

As one can see, the larger L is, the higher hit ratio becomes, because there is a higher chance that the correct answer is included in the recommendation list. …

Cross Entropy

Cross entropy loss is commonly used in classification tasks both in traditional ML and deep learning.

Note: logit here is used to refer to the unnormalized output of a NN, as in Google ML glossary. However, admittedly, this term is overloaded, as discussed in this post.

In this figure, the raw unnormalized output from a Neural Network is converted into probability by a softmax function.

Introduction

RS is to match items to users. The starting point is a user-item matrix filled with values representing either explicit feedback (user provided ratings) or implicit feedback (count of clicks, number of visits, watch time, etc.). One can also pose this question as a matrix filling problem: given the known entries in user-item matrix, how to fill in the missing entries given all sort of constraints?

Introduction

Given the recent hyped progress in deep learning based approaches in AI research especially in NLP and CV, it is time to go back to the fundamentals. Many of the fundamental methods, such as neighbourhood based methods are surprisingly effective in many areas, even today. We take a brief look at nearest neighbour method and its applications in density estimation and recommender systems. User-based and item-based collaborative filtering are discussed. In the end, the cold start and data sparsity issue are also touched upon.

Nearest Neighbour in the context of density estimation

Let’s start with the problem of density estimation. In plain words, density estimation is the construction…

Immutability is highly encouraged in Scala applications. As a first class into Scala, it often starts with the difference between val and var. In the naive example when the variable is pointing to a primitive data type like Integer, it all seems obvious, but what if the variable is pointing to a more complex data type, such as a class with a mixture of var and val members?

Let’s dive in with a small example walk-through.

val is conveniently called “value”, implying its immutability, whereas var is called “variable”, implying its mutability. …

What are we really talking about when we talk about AI?

The term “Artificial Intelligence” is coined shortly after World War II during the 1956 Dartmouth Conference. John McCarthy persuaded other attendees (amongst Marvin Minsky, Claude Shannon, etc.) to accept AI as the name for this promising field.

(Anecodotes: Claude Shannon again? He is just everywhere. And Marvin Minsky? Isn’t him the villain who has discouraged Neural Network reseach and caused the AI winter in 1970s?)

Compared to Physics and other fields in science and engineering, AI is one of the newest field, claimed by Russel & Norvig in their…

Let’s start by outlining some main benefits of using ML pipeline, already.

• Unified API for both data transformations and modelling
• No extra preprocessing steps needed during model serving time
• Joint grid search possible including data preprocessing steps/parameters and model selection/parameters
• Compatible with big data framework such as Spark

The last point might not be immediately obvious for some. Imagine if one day your model needs to train on large amount of data. You might have no better choice but to use some distributed framework like Spark. Using ML pipeline will make migrating to Spark a breeze to work with. One…

Hashing maps objects into different bins. Unlike conventional hashing functions which minimize collision probability, locality sensitive hashing functions maximize it for similar objects. In other words, for a given distance measure, similar items are more likely to be mapped to the same bin with LSH. This way, we can find neighbors for a certain item in a smaller area of neighborhood (hence the name “locality sensitive” hashing), without comparing it against all items. This dramatically speeds up nearest neighbor search.

Here we use “objects” in a broad sense to represent any data (e.g., sets, arrays, documents, etc.). One example is…