Name
Abstract | ||
|---|---|---|
We describe a novel approach that integrates user-generated tags with standard Matrix Factorization to allow users to interactively control recommendations. The tag information is incorporated during the learning phase and relates to the automatically derived latent factors. Thus, the system can change an item’s score whenever the user adjusts a tag’s weight. We implemented a prototype and performed a user study showing that this seems to be a promising way for users to interactively manipulate the set of items recommended based on their user prole or in cold-start situations. |
Year | Venue | Field |
|---|---|---|
2015 | RecSys Posters | Data mining,Computer science,Interactive control,Matrix decomposition,Merge (version control) |
DocType | Citations | PageRank |
Conference | 3 | 0.41 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tim Donkers | 1 | 41 | 6.15 |
| Benedikt Loepp | 2 | 88 | 10.71 |
| Jürgen Ziegler 0001 | 3 | 22 | 3.99 |