Name
Abstract | ||
|---|---|---|
Recommender systems filter resources for a given user by predicting the most pertinent resource given a specific context. This paper describes a new approach of generating suitable recommendations based on the active user's navigation stream. The underlying hypothesis is that the resources order in the stream results from the intrinsic logic of the user's behavior. The Sequence Based Recommender we propose is inspired from Language Modeling and integrates skipping techniques. It has been tested on a browsing dataset extracted from Intranet logs provided by a French bank. Results show that the use of exponential decay weighting schemes when taking into account non contiguous sequences to compute recommendations enhances the accuracy. Moreover, we propose a skipping variant that provides a high accuracy while being less complex. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/WIIAT.2008.280 | Web Intelligence |
Keywords | Field | DocType |
active user,recommender system,stream result,sequence-based collaborative filtering,account non contiguous sequence,intranet log,language modeling,navigation stream,resources order,french bank,high accuracy,pattern analysis,computational modeling,exponential decay,navigation,accuracy,natural languages,collaborative filtering,language model,history | Data mining,Weighting,Computer science,Artificial intelligence,Language model,Recommender system,Distance measurement,Collaborative filtering,Information retrieval,Markov model,Intranet,Natural language,Machine learning | Conference |
Citations | PageRank | References |
2 | 0.40 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Geoffray Bonnin | 1 | 58 | 6.29 |
| Armelle Brun | 2 | 138 | 21.49 |
| Anne Boyer | 3 | 19 | 5.24 |