Name
Title | ||
|---|---|---|
UKIRF: An Item Rejection Framework for Improving Negative Items Sampling in One-Class Collaborative Filtering |
Abstract | ||
|---|---|---|
Collaborative Filtering (CF) is one of the most successful techniques in recommender systems. Most CF scenarios depict positive-only implicit feedback, which means that negative feedback is unavailable. Therefore, One-Class Collaborative Filtering (OCCF)techniques have been tailored to tackling these scenarios. Nonetheless, several OCCF models still require negative observations during training, and thus, a popular approach is to consider randomly selected unknown relationships as negative. This work brings forward a novel and non-random approach for selecting negative items called Unknown Item Rejection Framework (UKIRF). More specifically, we instantiate UKIRF using similarity approaches, i.e., TF-IDF and Cosine, to reject items similar to those a user interacted with. We apply UKIRF to different OCCF models in different datasets and show that it improves the recall rates up to 24% when compared to random sampling. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-75765-6_44 | ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2021, PT II |
Keywords | DocType | Volume |
Collaborative recommendations systems, Implicit feedback, Negative sampling, Similarity metrics | Conference | 12713 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antônio David Viniski | 1 | 0 | 0.34 |
| Jean Paul Barddal | 2 | 140 | 16.77 |
| Alceu Britto | 3 | 94 | 18.30 |