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Next-item recommendation within a short session using the combined features of horizontal and vertical convolutional neural network

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Abstract

Session-based recommendation systems are designed to offer recommendations to users based on their current browsing session rather than relying on their entire historical behavior. In many real-world scenarios, user profiles and historical behaviors are not readily available, which makes it challenging to provide accurate recommendations. However, most recommender systems only consider the user’s long-term profile and static preferences while ignoring their dynamic preferences, resulting in unreliable recommendations. The existing traditional and deep learning-based methods generate recommendations based on session data, such as clicks are not able to capture sequential patterns, contextual information, and dynamic preferences altogether. To address these issues, a deep learning-based model, i.e., horizontal vertical convolutional neural network (HV-CNN) has been proposed, which uses the combination of horizontal and vertical convolutional features to recommend the next item for a given sequence of items in the current ongoing session. The session clicks present in the dataset have been embedded using Word2Vec embedding technique before providing it to the proposed HV-CNN model. Although predicting the next item within a session is challenging due to the limited contextual information available, the proposed model outperforms state-of-the-art methods on the publicly available 30Music dataset.

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Data Availability

Datasets used in this study are publicly available and accessible. Links for the datasets are provided in the footnote.

Notes

  1. http://recsys.deib.polimi.it/datasets/

  2. www.kaggle.com/chadgostopp/recsys-challenge-2015.

Abbreviations

SBR:

Session-based recommendation

RS:

Recommendation System

CB:

Content-based

CF:

Collaborative Filtering

ML:

Machine Learning

NN:

Neural network

DL:

Deep Learning

SBRS:

Session-based Recommendation System

MDP:

Markov Decision Process

POI:

Point of Interest

POP:

Popularity Predictor

MF:

Matrix Factorization

MC:

Markov Chain

FPM:

Frequent Pattern Mining

FSM:

Frequent Sequence Mining

KNN:

K-nearest Neighbour

IKNN:

Item K-nearest Neighbour

SKNN:

Session K-nearest Neighbour

V-SKNN:

Vector Session K-nearest Neighbour

S-SKNN:

Sequential Session K-nearest Neighbour

SF-SKNN:

Sequential Filter Session K-nearest Neighbour

CNN:

Convolutional Neural Network

HCNN:

Horizontal Convolutional NN

VCNN:

Vertical Convolutional NN

HV-CNN:

Horizontal Vertical Convolutional NN

GNN:

Graph Neural Network

RL:

Reinforcement Learning

RNN:

Recurrent Neural Network

GRU:

Gated Recurrent Unit

MGU:

Minimal Gated Unit

GNN:

Graph Neural Network

MRR:

Mean Reciprocal Rank

HR:

Hit Rate

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Patna, Patna, 800005, Bihar, India

    Chhotelal Kumar & Mukesh Kumar

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  1. Chhotelal Kumar
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  2. Mukesh Kumar
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Correspondence to Chhotelal Kumar.

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Appendix

Appendix

The comparison between the training loss and validation loss of the HCNN model is presented in Fig. 7a, whereas the training accuracy and validation accuracy of the proposed HCNN-based method with the filter size of \(2 \times 50\) against various values of the epoch is presented in Fig. 7b.

The comparison between training loss and validation loss is presented in Fig. 8a, whereas the training accuracy and validation accuracy of the proposed VCNN model against various values of the epoch is presented in Fig. 8b.

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Kumar, C., Kumar, M. Next-item recommendation within a short session using the combined features of horizontal and vertical convolutional neural network. Multimed Tools Appl 83, 38611–38634 (2024). https://doi.org/10.1007/s11042-023-17201-z

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