Novel Significance Weighting Schemes for Collaborative Filtering: Generating Improved Recommendations in Sparse Environments

Abstract

Recommender systems apply machine learning and data mining techniques for filtering unseen information and can predict whether a user would like a given resource. To date, a number of recommender system algorithms have been proposed, where collaborative filtering is the most famous and adopted recommendation algorithm. Collaborative filtering recommender systems recommend items by identifying other similar users, in case of user-based collaborative filtering, or similar items, in case of item-based collaborative filtering. Significance weighting schemes assign different weights to neighbouring users/items found against an active user/item. Several significance weighting schemes have been proposed [1], [2], [3], [4]. In this paper, we claim that these proposed schemes are flawed by the fact that they can not be applied to general recommender system datasets. We provide the correct generalized significance weighting schemes using different novel heuristics, and by extensive experimental results on three different data sets, show how significance weighting schemes affect the performance of a recommender system. Furthermore, we claim that the conventional weighted sum prediction formula used in item-based [5] collaborative filtering is not correct for very sparse datasets. We provide the correct prediction formula and empirically evaluate it.

More information

Identifiers

Catalogue record

Export record