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    一种由长尾分布约束的推荐方法

    A Long Tail Distribution Constrained Recommendation Method

    • 摘要: 由于在线商品销售的长尾效应,冷门商品的总销量非常巨大,因而对冷门商品的推荐十分重要,然而由于对冷门商品的评价数量少,导致现存的推荐算法对其推荐权重接近平均推荐权重,所以很难使用户关注冷门商品,影响了冷门商品的销售,因此合理地提高冷门商品的推荐权重十分重要.提出一种由长尾分布约束的推荐方法(long tail distribution constrained recommendation method, LTDCR),由用户行为的相似度确定用户间相似关系,并应用不信任关系约束用户相似关系的传播,通过长尾分布约束由用户间相似关系计算的推荐权重,并给出一种精确描述长尾分布的方法.在包含大量冷门商品的数据集的实验结果表明,LTDCR在训练集较小的情况下,有效地提高了对冷门商品的推荐效果.

       

      Abstract: The sales of on-line shopping follow the rule of long tail distribution, therefore the total sales of unpopular goods are very large. Recommendations for unpopular goods are as important as recommendations for popular goods. However, many existing recommendation methods only focus on the recommendations for popular goods, and assign an average weight of recommendation to unpopular goods which have small number of ratings, thus it is hard to bring unpopular goods to user's attention and the sales of unpopular goods are depressed. So it is very important to improve the weight of recommendation for unpopular goods. In this paper, a long tail distribution constrained recommendation (LTDCR) method is proposed for improving the weight of recommendation for unpopular goods appropriately. The weight of recommendation in LTDCR is calculated using similarity relationship among users, where the similarity relationship is determined by the similarity of users' behaviors and is propagated under the constraint of distrust relationship. In order to improve the weight of recommendation for unpopular goods, the weight of recommendation is constrained by the long tail distribution. An accurate description of long tail distribution is also given in this paper. The experimental results in dataset containing large number of unpopular goods show that LTDCR need fewer training set to improve the effectiveness of recommendations for unpopular goods.

       

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