Recommender Systems

Recommender Systems is also called Collaborative Filtering.

'Users' are typically the rows in the data utilised for the analysis, and 'Items' will be the columns. (from retail ecommerce context)

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The values, such as ratings columns, are occasionally split by frequency. The quantity of buyers or raters is referred to as the frequency. The ratings are normalised throughout this procedure.

applied often on e-commerce sites. To create personalised tactics to propose products with a high likelihood of being purchased, customers' purchasing behaviours are examined.

• What is the item most likely to be purchased?
• Can we identify and make suggestions/recommendations upfront?

It is necessary to respond to these two basic questions. Recommendations in turn assist the user develop confidence in themselves and brand loyalty.

Types of Recommendation Strategies

The most often used method is collaborative filtering, which relies on user similarity metrics.

Similarity Measures:

Cosine Based Similarity:

Correlation Based Similarity:

Euclidean distance

Manhattan distance, etc.

What to Recommend:

List the products that the individual is MOST LIKELY to buy from the list of products that customers with comparable buying patterns have already purchased.

Sorting the list of items can be based on:

• How many similar customers purchased it
• Rated by most
• Highest rated, etc.

Disadvantages:

Costly memory and lazy learning

Compute is relatively costly - n computations for similarities

Alternative Approaches

Making strategic decisions in comparison to suggestions with "Better accuracy and offline" vs. "Slightly lower accuracy and online"

Recommendations vs Association Rules

Challenge with Rating Matrix-Based Recommendation:

The number of vacant cells in rating matrices makes them large and sparse.

The sparse rating matrix is handled using the SVD algorithm.

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