User modeling

Matrix factorization

* "Variational autoencoders for collaborative filtering": Liang et al. (2018)
* "queriable" variant that takes into account the ambiguity of people who have many interests (but still use a single Gaussian distribution to model them): Wu et al. (2019)

## Multiple prototypes/clusters

Wu et al. (2019) demonstrate that users who interact with more items often have diverse interests and modeling these interests properly help improve the relevance of recommendations.

### Content-based

From Wang et al. (2006): Chen et al. (2001) and Pretschener et al. (1999) propose "a profile model with hierarchical concept categories"

### Collaborative filtering

#### Discrete presentation

From Si and Jin (2003):

* "The aspect model (Hofmann & Puzicha, 1999) is a

probabilistic latent space model, which models individual
preferences as a convex combination of preference
factors. [...] The aspect model assumes that users and items are
independent from each other given the latent class
variable."

* "two-sided clustering model is proposed for

collaborative filtering in (Hofmann & Puzicha, 1999).
This model assumes that each user should belong to
exactly one group of users and the same is true for each
item"

* "In the personality diagnosis model (Pennock et al., 2000), the observed rating for the test user *yt* on an item *x* is assumed to be drawn from an independent normal distribution with the mean as the true rating"

Si and Jin (2003): "FMM extends
existing partitioning/clustering algorithms for
collaborative filtering by clustering both users
and items together simultaneously without
assuming that each user and item should only
belong to a single cluster"

## References

1. ↑ Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. Varia-
tional autoencoders for collaborative filtering. arXiv preprint arXiv:1802.05814, 2018.
2. ↑ 2.0 2.1 Wu, G., Bouadjenek, M. R., &
Sanner, S. (2019). One-Class Collaborative Filtering with the Queryable
Variational Autoencoder.
3. ↑ Wang, J., Li, Z., Yao, J., Sun, Z.,
Li, M., & Ma, W. Y. (2006, January). Adaptive user profile model and
 collaborative filtering for personalized news. In *Asia-Pacific Web Conference* (pp. 474-485). Springer, Berlin, Heidelberg.
4. ↑ C. C. Chen, M. Chen, and Y. Sun. PVA: A Self-Adaptive Personal View Agent. In Proceed-
ings of the Seventh ACM SIGKDD international conference on Knowledge discovery and data
mining, 2001
5. ↑ A. Pretschner, and S. Gauch. Ontology Based Personalized Search. In 11th IEEE Intl. Conf.
On Tools with Artificial Intelligence, 1999
6. ↑ 6.0 6.1 Si, L., & Jin, R. (2003). Flexible mixture model for collaborative filtering. In *Proceedings of the 20th International Conference on Machine Learning (ICML-03)* (pp. 704-711).
7. ↑ Hofmann, T., & Puzicha, J. (1999). Latent Class Models
for Collaborative Filtering. In the Proceedings of
International Joint Conference on Artificial
Intelligence.
8. ↑ Hofmann, T., & Puzicha, J. (1999). Latent Class Models
for Collaborative Filtering. In the Proceedings of
International Joint Conference on Artificial
Intelligence.
9. ↑ Hofmann, T., & Puzicha, J. (1999). Latent Class Models
for Collaborative Filtering. In the Proceedings of
International Joint Conference on Artificial
Intelligence.
10. ↑ Pennock, D. M., Horvitz, E., Lawrence, S., & Giles, C.
L. (2000). Collaborative Filtering by Personality
Diagosis: A Hybrid Memory- and Model-Based
Approach. In the Proceeding of the Sixteenth
Conference on Uncertainty in Artificial Intelligence.