Time and sequence awareness in similarity metrics for recommendation

Modeling the temporal context efficiently and effectively is essential to provide useful recommendations to users. In this work, we focus on improving neighborhood-based approaches where we integrate three different mechanisms to exploit temporal information. We first present an improved version of a similarity metric between users using a temporal decay function, then, we propose an adaptation of the Longest Common Subsequence algorithm to be used as a time-aware similarity metric, and we also redefine the neighborhood-based recommenders to be interpreted as ranking fusion techniques where the neighbor interaction sequence can be exploited by considering the last common interaction between the neighbor and the user. We demonstrate the effectiveness of these approaches by comparing them with other state-of-the-art recommender systems such as Matrix Factorization, Neural Networks, and Markov Chains under two realistic time-aware evaluation methodologies (per user and community-based). We use several evaluation metrics to measure both the quality of the recommendations - in terms of ranking relevance - and their temporal novelty or freshness. According to the obtained results, our proposals are highly competitive and obtain better results than the rest of the analyzed algorithms, producing improvements under the two evaluation dimensions tested consistently through three real-world datasets.