Context-aware user modeling strategies for journey plan recommendation

Popular journey planning systems, like Google Maps or Yahoo! Maps, usually ignore user’s preferences and context. This paper shows how we applied context-aware recommendation technologies in an existing journey planning mobile application to provide personalized and context-dependent recommendations to users. We describe two different strategies for context-aware user modeling in the journey planning domain. We present an extensive performance comparison of the proposed strategies by conducting a user-centric study in addition to a traditional offline evaluation methodPeer ReviewedPostprint (published version

Contextual modeling content-based approaches for new-item recommendation

The new-item cold-start problem is a well-known limitation of context-free and context-aware Collaborative Filtering (CF) prediction models. In such situations, only Content-based (CB) approaches can produce meaningful recommendations. In this paper, we propose three Context-Aware Content-Based (CACB) models that extend a linear CB prediction model with context-awareness by including additional parameters that represent the influence of context with respect to the users' interests and rating behaviour. The precision of the proposed models has been evaluated using a contextually-tagged rating data set for journey plans in the city of Barcelona (Spain), which has a high number of new items. We demonstrate that, in this data set, the most sophisticated CACB model, which exploits the contextual information at different granularities and also the distributional similarities between contextual conditions during user modeling, significantly outperforms a context-free CB model as well as a state-of-the-art context-aware approach.Peer ReviewedPostprint (published version

Contextual modeling content-based approaches for new-item recommendation

The new-item cold-start problem is a well-known limitation of context-free and context-aware Collaborative Filtering (CF) prediction models. In such situations, only Content-based (CB) approaches can produce meaningful recommendations. In this paper, we propose three Context-Aware Content-Based (CACB) models that extend a linear CB prediction model with context-awareness by including additional parameters that represent the influence of context with respect to the users' interests and rating behaviour. The precision of the proposed models has been evaluated using a contextually-tagged rating data set for journey plans in the city of Barcelona (Spain), which has a high number of new items. We demonstrate that, in this data set, the most sophisticated CACB model, which exploits the contextual information at different granularities and also the distributional similarities between contextual conditions during user modeling, significantly outperforms a context-free CB model as well as a state-of-the-art context-aware approach.Peer Reviewe