Name
Abstract | ||
|---|---|---|
The information overload problem remains serious for both consumers and service/content providers, leading to heightened demands for personalized recommendations. For recommender systems, updating user models is one of the most important tasks to keep up with their changing preferences and trends. Especially since new consumers and items emerge every day, which are promptly rated or reviewed, updating lists of items and rankings is crucial. In this paper, we set the goal of real time recommendation, to present these items instantly. Unlike standard collaborative filtering algorithms, our offline approach focuses only innovative consumers for these predictions, and then uses as few consumers as possible while keeping the same precision. Since innovators exist in many communities, and their opinions will spread and then stimulate their followers to adopt the same behavior, our approach is based on the hypothesis that a set of innova- tive consumers is sufficient to represent the most representative opinions in each community. Following this hypothesis, we derive a scalable method to detect both communities and innovative consumers in each community from a web- scale data from a behavior log. Our evaluation shows that our proposed weighting method can accurately sample given logs, and be compatible only with previous algorithms for real time recommendations. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2783258.2783260 | ACM Knowledge Discovery and Data Mining |
Keywords | Field | DocType |
Personalization,real-time recommendations,Serendipitous Recommendations,Topic models,Nonparametric models | Recommender system,Data mining,Information overload,Weighting,Collaborative filtering,Computer science,Topic model,Scalability,Personalization | Conference |
Citations | PageRank | References |
4 | 0.42 | 25 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Noriaki Kawamae | 1 | 119 | 10.96 |