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.
['Daniel Valcarce', 'Javier Parapar', 'Álvaro Barreiro']
Item-based relevance modelling of recommendations for getting rid of long tail products