Summary: Investigating Serendipity in Recommender Systems Based on Real User Feedback

Full text available: proceedings | direct link.

Denis Kotkov, Joseph A. Konstan, Qian Zhao, and Jari Veijalainen. 2018. Investigating Serendipity in Recommender Systems Based on Real User Feedback. In Proceedings of SAC 2018: Symposium on Applied Computing , Pau, France, April 9–13, 2018 (SAC 2018), 10 pages. DOI: 10.1145/3167132.3167276

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: Precision, Recall and NDCG

This post is dedicated to accuracy metrics in Lenskit framework.

Summary: Cross-Domain Recommendations with Overlapping Items

Full text available: proceedings | direct link.

D. Kotkov, S. Wang, and J. Veijalainen. Cross-domain recommendations with overlapping items. In Proceedings of the 12th International Conference on Web Information Systems and Technologies, pages 131-138, 2016.

Summary: Challenges of Serendipity in Recommender Systems

Full text available: proceedings | direct link.

D. Kotkov, J. Veijalainen, and S. Wang. Challenges of serendipity in recommender systems. In Proceedings of the 12th International Conference on Web Information Systems and Technologies, pages 251-256, 2016.

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.

Lenskit code example

Recently, I started using Lenskit framework. The framework is designed for recommendations. It contains a few useful recommendation algorithms, such as item-item collaborative filtering and matrix factorization. However, there is a lack of documentation and examples on the framework.
I needed to use SimpleEvaluator class and I could not find a relevant documentation on the the class or a good example how I can use it.