Name
Title | ||
|---|---|---|
Collaborative Filtering Method for Handling Diverse and Repetitive User-Item Interactions. |
Abstract | ||
|---|---|---|
Most collaborative filtering models assume that the interaction of users with items take a single form, e.g., only ratings or clicks or views. In fact, in most real-life recommendation scenarios, users interact with items in diverse ways. This in turn, generates complex usage data that contains multiple and diverse types of user feedback. In addition, within such a complex data setting, each user-item pair may occur more than once, implying on repetitive preferential user behaviors. In this work we tackle the problem of building a Collaborative Filtering model that takes into account such complex datasets. We propose a novel factor model, CDMF, that is capable of incorporating arbitrary and diverse feedback types without any prior domain knowledge. Moreover, CDMF is inherently capable of considering user-item repetitions. We evaluate CDMF against stateof- the-art methods with highly favorable results.
|
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3209542.3209550 | HT |
Field | DocType | ISBN |
Recommender system,World Wide Web,Collaborative filtering,Domain knowledge,Computer science,Matrix decomposition,Complex data type,Human–computer interaction,Usage data | Conference | 978-1-4503-5427-1 |
Citations | PageRank | References |
1 | 0.35 | 27 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oren Sar Shalom | 1 | 20 | 7.74 |
| Haggai Roitman | 2 | 314 | 32.07 |
| Amihood Amir | 3 | 1985 | 191.89 |
| Alexandros Karatzoglou | 4 | 1522 | 68.76 |