Prediction.IO template for item similarity measurement
Scala Python
Switch branches/tags
Nothing to show
Clone or download
alexice Merge pull request #8 from shankarkamra/master
Updated files to make it compatible with 0.12
Latest commit 8cd24f9 Dec 13, 2017

README.md

SVD Item Similarity Engine Template

Template to calculate similarity between items based on their attributes. Attributes can be either numeric or categorical in the last case it will be encoded using one-hot encoder. Algorithm uses SVD in order to reduce data dimensionality. Cosine similarity is now implemented but can be easily extended to other similarity measures.

Overview

This template is a simple example of unsupervised learning algorithm implementation. Given items with their attributes of numeric and string types (like movie release year, duration, director, actors, etc.) we can measure similarity between items based on some metric (cosine similarity in this example).

Every item is represented as a set of attributes. Attributes can be of type String, Array of Strings and Int. Items are transformed into vectors with categorical variables encoded using one-hot encoder (http://en.wikipedia.org/wiki/One-hot) i.e. every String and Array of Strings attribute is transformed to number of binary attributes (facts about item) like "Is John Carpenter the director of the movie?". So item vector length is equal to number of possible values of all categorical variables plus the number of numeric variables.

Given such encoding of data numeric and categorical variables are not equivalently contribute into resulting vector (the problem is that there is no unified method to merge categorical and numeric attributes.). To fix this issue in some extent weights of numeric variables are used in this example.

Then vectors are normalized. So that their dot product produces cosine of the angle between vectors or in other words item-attribute matrix multiplied to itself transposed produces cosine similarity matrix (item to item). If we take into consideration that the number of attributes and items can be very big, then such matrix can be very big as well and it can be computationally difficult to calculate it, so dimensionality reduction should be used. In this example Singular value decomposition (https://spark.apache.org/docs/latest/mllib-dimensionality-reduction.html#singular-value-decomposition-svd) is used. So we need to save one matrix of size M x K, M is the number of items and K is the number of dimensions we're going to use.

Data is generated from Movielens database. Genre and year is real, actor list, director, producer and film duration in minutes are randomly generated for demo purposes.

Engine parameters

This example has four parameters to tune. The main is dimensions, this parameter should be less or equal to min(number of items, item vector dimension) and represents dimensionality of the space we are projecting our vectorized items. This parameter should be chosen so that our model could grasp the data (similarity between items) from one side and were not very excessive from the other side. After training algorithm outputs top singular values and the worst case estimation of captured variability of data. Worst case estimation is obtained with supposition that all the left singular values (which are not calculated) are equal to the smallest one, the closer to 100% this value is the better.

The two other parameters are yearWeight and durationWeight. They represent weight of numeric attributes year and duration respectively. These parameters should be selected experimentally so that model would represent better similarity between items. No unified recipe can be offered for selecting them, but some intuition can be used. As both year and duration are scaled to zero mean and unit standard deviation their weights represent their relative importance to any other attribute.

The last parameter is normalizeProjection (true / false). SVD with dimensions < rank of item-attribute matrix is the approximation of the original matrix, so all vectors will be projections of original vectors into space with reduced dimensionality (dimensions). In this case dot product of vectors will be less then the cosine of the angle between them. To fix this it is good to normalize vectors after projection is done. It is not very necessary if singular values decay rate is high, in such case not much of information is lost due to projection.

Output during train

SVD is a technique to reduce data dimensionality, the idea is to describe in the first place the most considerable peculiarities and then gradually less and less considerable. The importance of every dimension (dimensions parameter) is given by correspondent singular value, SVD algorithm works so that highest value is found first, then next highest and so on. The sum of squares of all singular values is equal to data variance, so the more dimensions you use and the higher their correspondent singular values the more info about data the model preserves.

After training is done algorithm outputs singular values and worst case estimation of grasped variability, the higher last tally is the better. It's calculated in very simple manner -- in worst case all singular values left are considered equal to smallest of calculated by algorithm (actually they should be a little bit smaller even for completely random data, but this is the best estimation we can have without additional assumptions), having this we can calculate how much of variability in such worst case has our model "described".

If worst case estimation of grasped variability is low, then it's wise to increase the number of dimensions. But from the other side the more dimensions you use the longer train and prediction will work.

It's good to consider last singular values in output too. If their decay rate is slow i.e. every next value is very close to previous meanwhile worst case estimation of grasped variability is low, then the algorithm can not find system in data and it can mean that attributes do not describe similarity of items (you'll give such situation with random data, so this hallmark is a sign of that algorithm can not distinguish your data from random).

Versions

v0.0.5

Usage

Event Data Requirements

By default, the template requires the following events to be collected:

  • item $set event, which sets the attributes of the item

Input Query

  • array of items
  • number of results

Output PredictedResult

  • List of items with similarity score

Import sample data

$ cd data
$ python import_eventserver.py --access_key <your_access_key>

The sample data for training is generated from four files:

  • sample_actors.txt
  • sample_directors.txt
  • sample_producers.txt
  • sample_movies.txt

Build, train, deploy

As for most prediction.io templates the workflow is standard:

$ pio build && pio train && pio deploy

Query

$ cd data
$ python send_query.py