Augmenting service recommender systems by incorporating contextual opinions from user reviews

Context-aware recommender systems have been widely investigated in both academia and industry because they can make recommendations based on a user’s current context (e.g., location, time). However, most existing context-aware techniques only use contextual information at the item level when modeling users’ preferences, i.e., contextual information that correlates with users’ overall evaluations of items such as ratings. Few studies have attempted to detect more fine-grained contextual preferences at the level of item aspects (e.g., a hotel’s “location”, “food quality”, and “service”). In this study, we use contextual weighting strategies to derive users’ aspect-level context-dependent preferences from user-generated textual reviews. The inferred context-dependent preferences are then combined with users’ context-independent preferences that are also inferred from reviews to reflect their stable requirements over time. To automatically incorporate both types of user preferences into the recommendation process, we propose a linear-regression-based algorithm that uses a stochastic gradient descent learning procedure. We tested the proposed recommendation algorithm with two real-life service datasets (one with hotel review data and the other with restaurant review data) and compared its contribution with three previously suggested approaches: one that does not consider contextual information; one that uses contextual information to pre-filter rating data before applying the recommendation algorithm; and one that generates recommendations according to users’ aspect-level contextual preferences. The experiment results demonstrate that our approach outperforms the others in terms of recommendation accuracy.