CBPF: leveraging context and content information for better recommendations
This work addresses the need for more personalized recommendations in context-aware systems, but it appears incremental as it builds on existing methods by combining content and context.
The authors tackled the problem of improving recommendations by integrating contextual information, modeling its influence on ratings using Pearson Correlation Coefficient, and demonstrated effectiveness across three datasets with varying sparsity levels.
Recommender systems help users to find their appropriate items among large volumes of information. Different types of recommender systems have been proposed. Among these, context-aware recommender systems aim at personalizing as much as possible the recommendations based on the context situation in which the user is. In this paper we present an approach integrating contextual information into the recommendation process by modeling either item-based or user-based influence of the context on ratings, using the Pearson Correlation Coefficient. The proposed solution aims at taking advantage of content and contextual information in the recommendation process. We evaluate and show effectiveness of our approach on three different contextual datasets and analyze the performances of the variants of our approach based on the characteristics of these datasets, especially the sparsity level of the input data and amount of available information.