Empirical analysis of session-based recommendation algorithms

One of the most important concerns about recommender systems is the filter bubble phenomenon. While recommender systems try to personalize information, they tighten the filter bubble around the users and deprive them of a wide range of content. To overcome this problem, one can diversify the personalized recommendation list. A diversified list usually presents a broader content to the user. Session-based recommender systems are types of recommenders in which only the current session of the user is available, and therefore, they should recommend the next item given the items in the current session. While diversifying conventional recommender systems has been well assessed in the literature, it has gained less attention in session-based recommenders. Diversity and accuracy usually have a negative correlation, i.e., by improving one the other one will be declined. In this study, we propose diversity and accuracy enhancing approaches based on sequential rule mining and session-based k-nearest neighbor methods. Finally, we propose a performance balancing approach that improves both the diversity and accuracy of these session-based recommender systems. We demonstrate the performance of the proposed methods on four music recommender datasets. Keywords Session-based recommenders • Diversity • Session-based k-nearest neighbor • Sequential rule mining • Filter bubble phenomenonThis article is part of the topical collection "Advanced Theories and Algorithms for Next-generation Recommender Systems" guest edited by

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“…accuracy measures compared to more complex methods. These results are also supported by other studies [22][23][24][25][26][27][28].…”

Section: Session-based Recommender Systemssupporting

confidence: 91%

Personalizing Diversity Versus Accuracy in Session-Based Recommender Systems

Gharahighehi

Vens

2021

SN COMPUT. SCI.

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“…(6) LSTMAPI: the recurrent neural network is used directly for prediction [13]. (7) SR-DL: the objective function of two-session sorting is optimized by stochastic gradient descent [20].…”

Section: Baseline Algorithmmentioning

confidence: 99%

An English Teaching Resource Recommendation System Based on Network Behavior Analysis

Zhang

2021

Scientific Programming

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The sharing of English teaching resources has always been a concern. In order to further improve the value of different English teaching resources, this paper proposes a resource management system based on an improved collaborative recommendation algorithm. The proposed model can predict user behavior based on deep learning models of graph neural network (GNN) and recurrent neural network (RNN). The graph neural network can capture the hidden state of local user behavior and be used as a preprocessing step. Recurrent neural networks can capture time series information. Therefore, the model is constructed by combining GNN and RNN to obtain the advantages of both. In order to prove the effectiveness of the model, we used CNGrid’s real user behavior dataset in the experiment and finally compared the results with other methods. The different deep learning-based models achieved a precision of up to 88% and outperformed other traditional models. The experimental results show that this new deep learning model has good sharing value.

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“…Moreover, they are easy to suffer from the data sparsity issue. Recently, session-KNN (SKNN) was proposed for nextitem recommendation, which utilizes the similarity between sessions to calculate the score of the candidate items to be the next item [16,17]. Based on SKNN, Garg et al [18] introduced sequence and time aware neighborhood model for session-based recommendation, which additionally takes into account the readily available position information of items within sessions/sequences for more accurate recommendations.…”

Section: A Conventional Sequential Recommendationsmentioning

confidence: 99%

Learning Users’ Visual Preferences for Improving Recommendations

Wang

Yang

et al. 2022

IEEE Access

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Sequential recommender systems (SRSs) aim to predict the next item interest to a user by learning the users' dynamic preferences over items from the sequential user-item interactions. Most of existing SRSs make recommendations by only modeling a user's main preference towards the functions of items, while ignoring the user's auxiliary visual preference towards the appearances and styles of items. Although visual preference is less significant than the main preference, it may still play an important role in most of users' choice on items. On the one hand, a user often prefers to choose the item which matches her/his visual preference well from multiple items with the same function. For example, a lady may choose one clothes whose style suits her best from multiple clothes with the same function. On the other hand, some particular users (e.g., young girls) are usually very concerned about the appearances of some special items (e.g., clothes, jewelry). Therefore, the overlook of modeling users' visual preference may generate unsatisfied recommendations which can not match a user's various types of preferences and thus reduce the consumption experience. To address this gap, in this paper, we propose modeling users' visual preferences to improve the performance of sequential recommendations. Specifically, we devise a coupled Double-chain Preference learning Network (DPN) to jointly learn a user's main preference and visual preference as well as the interactions between them. In DPN, one chain is for modeling a user's main preference by taking the IDs of items as the input and the other chain is for modeling the user's visual preference by taking appearance images of items as the input. Finally, the two types of preferences are carefully integrated with an attention module for the next item prediction. Extensive experiments on two real-world transaction datasets show the superiority of our proposed DPN over the representative and state-of-the-art SRSs. INDEX TERMS visual preference, recommendations, deep neural networks I. INTRODUCTION

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Empirical analysis of session-based recommendation algorithms

Cited by 75 publications

References 51 publications

Personalizing Diversity Versus Accuracy in Session-Based Recommender Systems

Personalizing Diversity Versus Accuracy in Session-Based Recommender Systems

An English Teaching Resource Recommendation System Based on Network Behavior Analysis

Learning Users’ Visual Preferences for Improving Recommendations

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