Attentive Long ShortTerm Preference Modeling for Personalized Product Search Ecommerce 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: longterm ones and shortterm ones. The former refers to usersxe2x80x99 inherent purchasing bias and evolves slowly. By contrast, the latter reflects usersxe2x80x99 purchasing inclination in a relatively short period. They both affect usersxe2x80x99 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 ShortTerm Preference model, dubbed as ALSTP, for personalized product search. Our model adopts the neural networks approach to learn and integrate the long and shortterm user preferences with the current query for the personalized product search. In particular, two attention networks are designed to distinguish which factors in the shortterm as well as longterm user preferences are more relevant to the current query. This unique design enables our model to capture usersxe2x80x99 current search intentions more accurately. Our work is the first to apply attention mechanisms to integrate both long and shortterm user preferences with the given query for the personalized search. Extensive experiments over four Amazon product datasets show that our model significantly outperforms several stateoftheart product search methods in terms of different evaluation metrics.

A Novel Hybrid Deep Recommendation System to Differentiate Userxe2x80x99s Preference and Itemxe2x80x99s Attractiveness Abstract   With the fast development of online Ecommerce Websites and mobile applications, usersxe2x80x99 auxiliary information as well as productsxe2x80x99 textual information can be easily collected to form a vast amount of training data. Therefore, research efforts are urgently needed to make customized recommendations using such large but sparse data. Deep recommendation model is a natural choice for this research issue. However, most existing approaches try to investigate either userxe2x80x99s auxiliary information such as age and zipcode, or itemxe2x80x99s textual information such as product descriptions, reviews or comments. Therefore, it is desired to see whether userxe2x80x99s auxiliary information and itemxe2x80x99s textual information could be modeled simultaneously. This paper proposes a novel approach which is essentially a hybrid probabilistic matrix factorization model. Particularly, it has two sub components. One component tries to predict userxe2x80x99s rating scores by capturing userxe2x80x99s personal preferences extracted from auxiliary information. Another component tries to model itemxe2x80x99s textual attractiveness to different users via a proposed attention based convolutional neural network. We then propose a global objective function and optimize these two sub components under a unified framework. Extensive experiments are performed on five realworld datasets, i.e., ML100K, ML1M, ML10M, AIV and Amazon sub dataset. The promising experimental results have demonstrated the superiority of our proposed approach when compared with both baseline models and stateoftheart deep recommendation approaches, i.e., PMF, CDL, CTR, ConvMF, ConvMF and DAttn with respect to RMSE criterion.