Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6862276 | Knowledge-Based Systems | 2016 | 11 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Daniel Valcarce, Javier Parapar, Álvaro Barreiro,