Summary: A Serendipity-Oriented Greedy Algorithm for Recommendations

Full text available: proceedings | direct link.

Denis Kotkov, Jari Veijalainen, and Shuaiqiang Wang. 2017. A Serendipity- Oriented Greedy Algorithm for Recommendations. In Proceedings of the 13th International Conference on Web Information Systems and Technologies, Vol. 1. ScitePress, 32–40.

Matrix Factorization: initial values

The initial distribution of feature values affects the results of matrix factorization (SVD) algorithm (this implementation). In this post, let's have a look at performance of SVD algorithm with different distributions of initial values. To conduct experiments, I used Lenskit framework and MovieLens100K dataset. The experiments includes three distributions:

1. Fixed values (0.1) (Fixed)
2. Random values (Random)
3. Popularity distribution for item features and random for user features (POP)

Lenskit: Popularity baseline (Learning to Rank)

This post is dedicated to popularity baseline in Lenskit 2 framework. The framework lacks this baseline. I therefore provide an implementation and demonstrate the results of the baseline.
The implementation includes three classes:
PopItemScorer - items scorer, which provides actual scores for items
PopModel - model that contains popularity of each item
PopModelBuilder - builder calculates popularity for each items and puts them to the model.