Attentive Item2Vec: Neural Attentive User Representations
This work addresses the issue of dynamically changing user interests in recommender systems, offering an incremental improvement over existing factorization methods.
The authors tackled the problem of static user representations in recommender systems by proposing Attentive Item2Vec, which uses a context-target attention mechanism to dynamically capture user interests based on potential recommendations, and demonstrated its effectiveness by outperforming baselines on several datasets.
Factorization methods for recommender systems tend to represent users as a single latent vector. However, user behavior and interests may change in the context of the recommendations that are presented to the user. For example, in the case of movie recommendations, it is usually true that earlier user data is less informative than more recent data. However, it is possible that a certain early movie may become suddenly more relevant in the presence of a popular sequel movie. This is just a single example of a variety of possible dynamically altering user interests in the presence of a potential new recommendation. In this work, we present Attentive Item2vec (AI2V) - a novel attentive version of Item2vec (I2V). AI2V employs a context-target attention mechanism in order to learn and capture different characteristics of user historical behavior (context) with respect to a potential recommended item (target). The attentive context-target mechanism enables a final neural attentive user representation. We demonstrate the effectiveness of AI2V on several datasets, where it is shown to outperform other baselines.