Combining Privileged Information to Improve Context-Aware Recommender Systems
This work addresses a major bottleneck in context-aware recommender systems by providing an automatic method to incorporate contextual information, though it is incremental as it builds on existing LUPI-based clustering.
The paper tackled the challenge of automatically obtaining contextual information for context-aware recommender systems by using topic hierarchies constructed from bag-of-words, named entities, and domain terms, resulting in improved recommendation performance as demonstrated in evaluations across four systems.
A recommender system is an information filtering technology which can be used to predict preference ratings of items (products, services, movies, etc) and/or to output a ranking of items that are likely to be of interest to the user. Context-aware recommender systems (CARS) learn and predict the tastes and preferences of users by incorporating available contextual information in the recommendation process. One of the major challenges in context-aware recommender systems research is the lack of automatic methods to obtain contextual information for these systems. Considering this scenario, in this paper, we propose to use contextual information from topic hierarchies of the items (web pages) to improve the performance of context-aware recommender systems. The topic hierarchies are constructed by an extension of the LUPI-based Incremental Hierarchical Clustering method that considers three types of information: traditional bag-of-words (technical information), and the combination of named entities (privileged information I) with domain terms (privileged information II). We evaluated the contextual information in four context-aware recommender systems. Different weights were assigned to each type of information. The empirical results demonstrated that topic hierarchies with the combination of the two kinds of privileged information can provide better recommendations.