Abstract
A session-based recommendation system is designed to predict the user’s next click behavior based on an ongoing session. Existing session-based recommendation systems usually model a session into a sequence and extract sequence features through recurrent neural network. Although the performance is greatly improved, these procedures ignore the relationships between items that contain rich information. In order to obtain rich items embeddings, we propose a novel Recommendation Model based on Multi-channel Convolutional Neural Network for session-based recommendation, RMMCNN for brevity. Specifically, we capture items' internal features from three dimensions through multi-channel convolutional neural network firstly. Next, we merge the internal features with external features obtained by a GRU unit. Then, both internal features and external features are merged by an attention mechanism together as the input of the transformation function. Finally, the probability distribution is taken as the output after the softmax function. Experiments on various datasets show that our method's precision and recommendation performance are better than those of other state-of-the-art approaches.
Highlights
With the explosive growth of the information in the Internet era, recommendation systems have become an effective solution for users to deal with large amounts of information [1]
Traditional recommendation systems are mainly divided into recommendation systems based on collaborative filtering (CF), content-based recommendation systems (CB), and hybrid recommendation systems (HRS) [10]
To overcome the limitations mentioned above, we propose a Recommendation Model based on Mutichannel Convolutional Neural Network (RMMCNN). e main contributions are as follows: (i) We introduce a multichannel convolutional neural network to extract item information in the context of a session
Summary
With the explosive growth of the information in the Internet era, recommendation systems have become an effective solution for users to deal with large amounts of information [1]. The user behavior is modelled as a session. E session consists of the sequence of clicks performed by the user on items. Us, the session contains the time series of user behavior and information between users and items [8, 9]. CF-based recommendation systems build user preference models through the similarity of users or/and items. The CB recommendation systems state recommendations based on the content of item characteristics [11]. In order to combine the advantages of both, HRS emerge to extract information from item attributes [12], users’ social networks [13], and item comments [14]
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