Ieee Projects 100% Working Code + Documentation+ Explaination – Best Price Typicality-based Collaborative Filtering Recommendation
Ieee Projects 100% Working Code + Documentation+ Explaination – Best Price Typicality-based Collaborative Filtering Recommendation
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协同过滤（CF）是推荐系统中一项重要且流行的技术。然而，目前的CF方法存在数据稀疏、推荐不准确、预测误差大等问题。本文借鉴认知心理学中的对象典型性思想，提出了一种基于典型性的协同过滤推荐方法TyCo。基于典型化的CF的一个显著特性是，它根据用户组中的用户典型化程度（而不是像传统CF那样，根据用户的关联项或项目的普通用户）找到用户的“邻居”。据我们所知，目前还没有通过结合对象典型性来研究CF推荐的工作。TyCo在推荐精度（就MAE而言）上优于许多CF推荐方法，在Movielens数据集上至少提高了6.35%，特别是在稀疏训练数据上（MAE提高了9.89%），并且比其他CF方法具有更低的时间成本。此外，它可以用更少的大误差预测获得更准确的预测。
Collaborative filtering (CF) is an important and popular technology for recommender systems. However, current CF methods suffer from such problems as data sparsity, recommendation inaccuracy, and big-error in predictions. In this paper, we borrow ideas of object typicality from cognitive psychology and propose a novel typicality-based collaborative filtering recommendation method named TyCo. A distinct feature of typicality-based CF is that it finds " neighbors " of users based on user typicality degrees in user groups (instead of the corated items of users, or common users of items, as in traditional CF). To the best of our knowledge, there has been no prior work on investigating CF recommendation by combining object typicality. TyCo outperforms many CF recommendation methods on recommendation accuracy (in terms of MAE) with an improvement of at least 6.35 percent in Movielens data set, especially with sparse training data (9.89 percent improvement on MAE) and has lower time cost than other CF methods.Further, it can obtain more accurate predictions with less number of big-error predictions.