Name
Title | ||
|---|---|---|
Item-based relevance modelling of recommendations for getting rid of long tail products. |
Abstract | ||
|---|---|---|
The liquidation of long tail items can be assisted by recommender systems.We propose a probabilistic item-based Relevance Model (IRM2).IRM2 outperforms state-of-the-art recommenders for long tail liquidation. Recommender systems are a growing research field due to its immense potential application for helping users to select products and services. Recommenders are useful in a broad range of domains such as films, music, books, restaurants, hotels, social networks, news, etc. Traditionally, recommenders tend to promote certain products or services of a company that are kind of popular among the communities of users. An important research concern is how to formulate recommender systems centred on those items that are not very popular: the long tail products. A special case of those items are the ones that are product of an overstocking by the vendor. Overstock, that is, the excess of inventory, is a source of revenue loss. In this paper, we propose that recommender systems can be used to liquidate long tail products maximising the business profit. First, we propose a formalisation for this task with the corresponding evaluation methodology and datasets. And, then, we design a specially tailored algorithm centred on getting rid of those unpopular products based on item relevance models. Comparison among existing proposals demonstrates that the advocated method is a significantly better algorithm for this task than other state-of-the-art techniques. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.knosys.2016.03.021 | Knowl.-Based Syst. |
Keywords | Field | DocType |
Recommender systems,Collaborative filtering,Relevance models,Long tail | Recommender system,Revenue,Data mining,Overstock,Collaborative filtering,Computer science,Vendor,Artificial intelligence,Long tail,Probabilistic logic,Machine learning,Special case | Journal |
Volume | Issue | ISSN |
103 | C | 0950-7051 |
Citations | PageRank | References |
7 | 0.42 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Valcarce | 1 | 54 | 8.51 |
| Javier Parapar | 2 | 188 | 25.91 |
| Alvaro Barreiro | 3 | 226 | 22.42 |