Freddie Karlbom

Collaborative Filtering - IICF - Week 3

Item-Item Collaborative Filtering (IICF).

UUCF oftentimes have issues of sparsity for larger datasets. There are strategies trying to address this like dimensionality reduction, but remains a challenge, but this also lead to the idea to look at item to item filtering.

This can also help with computing performance, as UUCF computations have high complexity and need to be recomputed frequently (especially for new users in order to give them useful recommendations).

Collaborative Filtering - UUCF - Week 1

Collaborative filtering ignore the attributes of users and items and instead only look at the interactions between them. Course begins by looking at User to User collaborative filtering (UUCF).

Intro to RS Week 4 - Unified Mathematical Model

Unified Mathematical Model

General high-level computing formula for recommendations; $s(i; u, q, x)$

We have a function that computes a score for items that also relies on user, query and context. Based on this, we can then see the domain search and discovery as different cases of the same general problem of recommending the best items to a user, be it based on user profile, a search query, the context or a combination thereof.

From Scoring to Recommendation

Similar to the raw scoring function, we can define an ordering function for a set of items I as;

The most basic one would be, score each item in set using the scoring function. Order by score descending and truncate after n items for a list of recommendations.

Intro to RS Week 3 - Content-Based Recommenders

Content-Based Recommenders

Stable preferences measured by content attributes.

Basic idea; model people and items based on

• TF-IDF
• Cosine similarity
• Data Normalisation

Intro to RS Week 2 - Non-personalized and Stereotyped Recommendations

• Good for new users to avoid cold-start
• Efficient to compute
• Creates a ‘single source of truth’ (Reddit topic rankings for example)