# Recommender Systems for Banking and Finance: Introduction and Examples

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WIP Alert This is a work in progress. Current information is correct but more content may be added in the future.

## Recommender Systems

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Well then, aren't Recommender Systems just good old Machine Learning?

Technically yes, but the settings are very different; whereas users typically type stuff into forms and hit search buttons to view search results, recommendations are usually displayed without explicitly being requested by users and are highly context-dependent1

## The User-item matrix

User-item matrices are highly sparse.
Element $$A_{ui}$$ represents the rating
user $$u$$ gave item $$i$$.
Source: @connectwithghosh

## Types of Recommender systems

Types of recommender systems
Source: Wikipedia

## Memory-based vs model-based Recommender Systems

If you need to train your system, it's model-based.

TODO


## Main Techniques: Collaborative filtering

Collaborative means that these methods use inputs from similar users too

• Memory-based

• Item-based
• User-based
• Model-based

• Matrix factorization approaches
• Other regular machine learning algorithms

## Main Techniques: Content-Based Recommendations

• Basically information retrieval

## Use cases in Commercial Banking/Finance

• Recommend financial products to users

TODO add more


## Other issues in RS research

• Scalability

TODO

• Personalization vs Privacy concerns

TODO

• Explicit vs Implicit Feedback

TODO

• System Robustness / Defense against attacks

It's very likely that users may try to artificially increase their products' popularity (product push) or decrease their competitors' (product nuke attack) once it's clear a rating system is in place.2

• Diversity vs Accuracy

TODO

• Context-aware Recommendation

TODO

• Location
• Time
• Cold start

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## Example: Simple Collaborative Filter with Python's Surpriselib

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### References

1: For example, at the bottom of a product page on Amazon, you will likely be shown product recommendations related to the current product you're viewing.

2: There are many other forms of attacking a recommender system: Identifying Attack Models for Secure Recommendation

## Appendix: Matrix Factorization

The dot product between row $$B$$ of the User matrix and row $$X$$ of
the Item Matrix is an approximation of the rating user $$B$$ would give item $$X$$
Source: @connectwithghosh