Attentive Long Short-Term Preference Modeling for Personalized Product Search

Abstract

E-commerce users may expect different products even for the same query, due to their diverse personal preferences. It is well-known that there are two types of preferences: long-term ones and short-term ones. The former refers to user' inherent purchasing bias and evolves slowly. By contrast, the latter reflects users' purchasing inclination in a relatively short period. They both affect users' current purchasing intentions. However, few research efforts have been dedicated to jointly model them for the personalized product search. To this end, we propose a novel Attentive Long Short-Term Preference model, dubbed as ALSTP, for personalized product search. Our model adopts the neural networks approach to learn and integrate the long- and short-term user preferences with the current query for the personalized product search. In particular, two attention networks are designed to distinguish which factors in the short-term as well as long-term user preferences are more relevant to the current query. This unique design enables our model to capture users' current search intentions more accurately. Our work is the first to apply attention mechanisms to integrate both long- and short-term user preferences with the given query for the personalized search. Extensive experiments over four Amazon product datasets show that our model significantly outperforms several state-of-the-art product search methods in terms of different evaluation metrics.

References in corpus (15)

• Efficient Estimation of Word Representations in Vector Space
• BPR: Bayesian Personalized Ranking from Implicit Feedback
• On the difficulty of training Recurrent Neural Networks
• Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
• On the Properties of Neural Machine Translation: Encoder-Decoder Approaches
• Convolutional Neural Network Architectures for Matching Natural Language Sentences
• A Deep Relevance Matching Model for Ad-hoc Retrieval
• Session-based Recommendations with Recurrent Neural Networks
• Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews
• MMALFM: Explainable Recommendation by Leveraging Reviews and Images
• Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN
• Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
• Inferring Networks of Substitutable and Complementary Products
• A Study of MatchPyramid Models on Ad-hoc Retrieval
• Ensemble Methods for Personalized E-Commerce Search Challenge at CIKM Cup 2016