A new neighbourhood formation approach for solving cold-start user problem in collaborative filtering

Rahul Kumar, P. Bala, Shubhadeep Mukherjee
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引用次数: 2

Abstract

Collaborative filtering (CF) is the most widely accepted recommendation technique. Despite its popularity, this approach faces some major challenges like that of a cold-start user problem where a user has rated a handful of items. Due to very few ratings available for the cold-start users, their similarities with rest of the users has been questioned in the past, none have focused on their approach for neighbour identification. Whilst the traditional CF approaches select only those similar users as neighbours who have rated the item under consideration, the neighbourhood comprises of weak neighbours of the cold-start users. To address this shortcoming, our proposed approach selects neighbours with highest similarity irrespective of their availability of ratings for that item. Moreover, for the selected similar neighbours with missing ratings, an item based regression is performed to partially populate the matrix. The efficacy of the proposed neighbourhood formation approach addressing cold-start user problem is validated on two publicly available MovieLens datasets. Our approach provides superior quality of recommendations evaluated on a range of prediction and classification accuracy metrics. The results are encouraging particularly for systems having higher percentage of cold-start users which indicates the effectiveness of our approach in practical settings of new internet portals.
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一种新的邻域形成方法用于解决协同过滤中的冷启动用户问题
协同过滤(CF)是目前应用最广泛的推荐技术。尽管这种方法很受欢迎,但它也面临着一些重大挑战,比如冷启动用户问题,即用户对少数几个项目进行了评级。由于冷启动用户的可用评级很少,他们与其他用户的相似性在过去受到质疑,没有人关注他们的邻居识别方法。虽然传统的CF方法只选择那些对所考虑的项目进行评级的相似用户作为邻居,但邻居由冷启动用户的弱邻居组成。为了解决这个缺点,我们提出的方法选择具有最高相似性的邻居,而不考虑其对该项目的可用性评级。此外,对于选择的具有缺失评级的相似邻居,执行基于项的回归来部分填充矩阵。在两个公开可用的MovieLens数据集上验证了所提出的邻域形成方法解决冷启动用户问题的有效性。我们的方法在一系列预测和分类准确度指标上提供了高质量的推荐。结果令人鼓舞,特别是对于具有较高百分比的冷启动用户的系统,这表明我们的方法在新互联网门户的实际设置中的有效性。
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来源期刊
International Journal of Applied Management Science
International Journal of Applied Management Science Business, Management and Accounting-Strategy and Management
CiteScore
1.20
自引率
0.00%
发文量
21
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