Modeling Personalized Item Frequency Information for Next-basket Recommendation
Haoji Hu, Xiangnan He, Jinyang Gao, Zhi-Li Zhang
Abstract
Next-basket recommendation (NBR) is prevalent in e-commerce and retail industry. In this scenario, a user purchases a set of items (a basket) at a time. NBR performs sequential modeling and recommendation based on a sequence of baskets. NBR is in general more complex than the widely studied sequential (session-based) recommendation which recommends the next item based on a sequence of items. Recurrent neural network (RNN) has proved to be very effective for sequential modeling, and thus been adapted for NBR. However, we argue that existing RNNs cannot directly capture item frequency information in the recommendation scenario.
Topics & Concepts
Computer scienceRecurrent neural networkRecommender systemSet (abstract data type)Session (web analytics)Sequence (biology)Information retrievalArtificial neural networkArtificial intelligenceData miningWorld Wide WebBiologyProgramming languageGeneticsRecommender Systems and TechniquesImage Retrieval and Classification TechniquesAdvanced Bandit Algorithms Research