TA4REC: Recurrent Neural Networks with Time Attention Factors for Session-based Recommendations

• Main Authors: Sun, Yu, Zhao, Peize, Zhang, Honggang
• Format: Conference Proceeding
• Language: English
• Subjects: Logic gates; Logistics; Recommender systems; Recurrent neural networks; Training
• ISSN: 2161-4407
• DOI: 10.1109/IJCNN.2018.8489591

Recommender systems show increasingly importance with the development of E-commerce, news and multimedia applications. Traditional recommendation algorithms such as collaborative-filtering-based methods and graph-based methods mainly use items' original attributes and relationships between items and users, ignoring items' chronological order in browsing sessions. In recent years, RNN-based methods show their superiority when dealing with the sequential data, and some modified RNN models have been proposed. However, these RNN models only use the sequence order of items and neglect items' browsing time information. It is widely accepted that users tend to spend more time on their interested items, and these interested items are always closely related to users' current target. Based on the above view, items' browsing time is an important feature in recommendations. In this paper, we propose a modified RNN-based recommender system called TA4Rec, which can recommend the probable item that may be clicked in the next moment. Our main contribution is to introduce a method to calculate the time-attention factors from browsing items' duration time and add time-attention factors to the RNN-based model. We conduct experiments on RecSys Challenge 2015 dataset and the result shows that TA4REC model has gained obvious improvement on session-based recommendations than the classic session-based recommender method.