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- W2344203481 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." @default.
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- W2344203481 date "2016-07-01" @default.
- W2344203481 modified "2023-10-14" @default.
- W2344203481 title "Item-based relevance modelling of recommendations for getting rid of long tail products" @default.
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- W2344203481 doi "https://doi.org/10.1016/j.knosys.2016.03.021" @default.
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