A Graph-based Method for Session-based Recommendations (original) (raw)
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Session-Based Recommendations for e-Commerce with Graph-Based Data Modeling
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Conventional recommendation methods such as collaborative filtering cannot be applied when long-term user models are not available. In this paper, we propose two session-based recommendation methods for anonymous browsing in a generic e-commerce framework. We represent the data using a graph where items are connected to sessions and to each other based on the order of appearance or their co-occurrence. In the first approach, called Hierarchical Sequence Probability (HSP), recommendations are produced using the probabilities of items’ appearances on certain structures in the graph. Specifically, given a current item during a session, to create a list of recommended next items, we first compute the probabilities of all possible sequential triplets ending in each candidate’s next item, then of all candidate item pairs, and finally of the proposed item. In our second method, called Recurrent Item Co-occurrence (RIC), we generate the recommendation list based on a weighted score produced...
A Flexible Session-Based Recommender System for e-Commerce
Applied Sciences
Research into session-based recommendation systems (SBSR) has attracted a lot of attention, but each study focuses on a specific class of methods. This work examines and evaluates a large range of methods, from simpler statistical co-occurrence methods to embeddings and SotA deep learning methods. This paper analyzes theoretical and practical issues in developing and evaluating methods for SBSR in e-commerce applications, where user profiles and purchase data do not exist. The major tasks of SBRS are reviewed and studied, namely: prediction of next-item, next-basket and purchase intent. For physical retail shopping where no information about the current session exists, we treat the previous baskets purchased by the user as previous sessions drawn from a loyalty system. Mobile application scenarios such as push notifications and calling tune recommendations are also presented. Recommender models using graphs, embeddings and deep learning methods are studied and evaluated in all SBRS ...
2020
Boosting sales of e-commerce services is guaranteed once users find more matching items to their interests in a short time. Consequently, recommendation systems have become a crucial part of any successful e-commerce services. Although various recommendation techniques could be used in e-commerce, a considerable amount of attention has been drawn to session-based recommendation systems during the recent few years. This growing interest is due to the security concerns in collecting personalized user behavior data, especially after the recent general data protection regulations. In this work, we present a comprehensive evaluation of the state-of-the-art deep learning approaches used in the session-based recommendation. In session-based recommendation, a recommendation system counts on the sequence of events made by a user within the same session to predict and endorse other items that are more likely to correlate with his/her preferences. Our extensive experiments investigate baseline...
A Study of Deep Learning-Based Approaches for Session-Based Recommendation Systems
Recommending relevant items of interest for a user is the main purpose of the recommendation system. In the past, those systems achieve the recommended list based on long-term user profiles. However, personal data privacy is becoming a big challenge recently. Thus, the recommendation system needs to reduce the dependence on user profiles while preserving high accuracy on the recommendation. Session-based recommendation is a recently proposed approach for the recommendation system to overcome the issue of user profiles dependency. The relevance of the problem is quite high and has triggered interest among researchers in observing the activities of users. It increased several proposals for session-based recommendation algorithms that aim to predict the next actions. In this paper, we would like to compare the performance of such algorithms by using various datasets and evaluation metrics. A deep learning approach named GRU4REC (Hidasi et al. in Session-based recommendations with recurrent neural networks, 2015) and simpler methods are included in our comparison. Real-world datasets from three different domains are included in our experiment. Our experiments reveal that in some cases of numerous unpopular items dataset, GRU4REC's performance is lower than expected. However, its performance is significantly increased after applying our proposed sampling method. Therefore, our obtained results suggested that there is still room for improving deep learning session-based recommendation algorithms.
Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks
Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019
Predicting a user's preference in a short anonymous interaction session instead of long-term history is a challenging problem in the real-life session-based recommendation, e.g., e-commerce and media stream. Recent research of the session-based recommender system mainly focuses on sequential patterns by utilizing the attention mechanism, which is straightforward for the session's natural sequence sorted by time. However, the user's preference is much more complicated than a solely consecutive time pattern in the transition of item choices. In this paper, therefore, we study the item transition pattern by constructing a session graph and propose a novel model which collaboratively considers the sequence order and the latent order in the session graph for a session-based recommender system. We formulate the next item recommendation within the session as a graph classification problem. Specifically, we propose a weighted attention graph layer and a Readout function to learn embeddings of items and sessions for the next item recommendation. Extensive experiments have been conducted on two benchmark E-commerce datasets, Yoochoose and Diginetica, and the experimental results show that our model outperforms other state-of-the-art methods. CCS CONCEPTS • Information systems → Recommender systems.
Exploiting Cross-session Information for Session-based Recommendation with Graph Neural Networks
ACM Transactions on Information Systems, 2020
Different from the traditional recommender system, the session-based recommender system introduces the concept of the session , i.e., a sequence of interactions between a user and multiple items within a period, to preserve the user’s recent interest. The existing work on the session-based recommender system mainly relies on mining sequential patterns within individual sessions, which are not expressive enough to capture more complicated dependency relationships among items. In addition, it does not consider the cross-session information due to the anonymity of the session data, where the linkage between different sessions is prevented. In this article, we solve these problems with the graph neural networks technique. First, each session is represented as a graph rather than a linear sequence structure, based on which a novel F ull G raph N eural N etwork (FGNN) is proposed to learn complicated item dependency. To exploit and incorporate cross-session information in the individual s...
Empirical analysis of session-based recommendation algorithms
User Modeling and User-Adapted Interaction
Recommender systems are tools that support online users by pointing them to potential items of interest in situations of information overload. In recent years, the class of session-based recommendation algorithms received more attention in the research literature. These algorithms base their recommendations solely on the observed interactions with the user in an ongoing session and do not require the existence of long-term preference profiles. Most recently, a number of deep learning-based (“neural”) approaches to session-based recommendations have been proposed. However, previous research indicates that today’s complex neural recommendation methods are not always better than comparably simple algorithms in terms of prediction accuracy. With this work, our goal is to shed light on the state of the art in the area of session-based recommendation and on the progress that is made with neural approaches. For this purpose, we compare twelve algorithmic approaches, among them six recent n...
Rethinking Rule-Based Approaches in Session-Based Recommendation
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Existing approaches to session-based recommendation are focused on using advanced deep neural networks (DNNs). Recent studies have shown that some traditional methods can outperform some DNN-based models. However, in recent years, few studies have tried to build traditional models. In this paper, we investigate this issue and propose a concise rule-based method for session-based recommendation. Specifically, we make item adjacent and N-gram-based rules to extract frequenting, sequential and other patterns of the limited historical information to construct item correlation dictionaries. Then, we exploit these dictionaries at the inference stage by constructing candidate item generation and item set fusion rules to acquire candidate items. By this means, we can leverage short-term user-item interaction records to generate candidate items for anonymous sessions. Finally, we sort the candidate items to make a recommendation. Extensive experimental results on three real public datasets show that our method can significantly outperform existing traditional methods and even outperforms several representative DNN-based models. The model just costs a few seconds to train and inference, and occupies less memory space than that. Compared with the previous models, it has a huge lead in time and space.
DAGNN: Demand-aware Graph Neural Networks for Session-based Recommendation
ArXiv, 2021
Session-based recommendations have been widely adopted for various online video and E-commerce Websites. Most existing approaches are intuitively proposed to discover underlying interests or preferences out of the anonymous session data. This apparently ignores the fact these sequential behaviors usually reflect session user’s potential demand, i.e., a semantic level factor, and therefore how to estimate underlying demands from a session is challenging. To address aforementioned issue, this paper proposes a demand-aware graph neural networks (DAGNN). Particularly, a demand modeling component is designed to first extract session demand and the underlying multiple demands of each session is estimated using the global demand matrix. Then, the demand-aware graph neural network is designed to extract session demand graph to learn the demandaware item embedddings for the later recommendations. The mutual information loss is further designed to enhance the quality of the learnt embeddings....
Evaluation of session-based recommendation algorithms
User Modeling and User-Adapted Interaction
Recommender systems help users find relevant items of interest, for example on e-commerce or media streaming sites. Most academic research is concerned with approaches that personalize the recommendations according to long-term user profiles. In many real-world applications, however, such long-term profiles often do not exist and recommendations therefore have to be made solely based on the observed behavior of a user during an ongoing session. Given the high practical relevance of the problem, an increased interest in this problem can be observed in recent years, leading to a number of proposals for session-based recommendation algorithms that typically aim to predict the user's immediate next actions. In this work, we present the results of an in-depth performance comparison of a number of such algorithms, using a variety of datasets and evaluation measures. Our comparison includes the most recent approaches based on recurrent neural networks like gru4rec, factorized Markov model approaches such as fism or fossil, as well as simpler methods based, e.g., on nearest neighbor schemes. Our experiments reveal that algorithms of this latter class, despite their sometimes almost trivial nature, often perform equally well or significantly better than today's more complex approaches based on deep neural networks. Our results therefore suggest that there is substantial room for improvement regarding the development of more sophisticated session-based recommendation algorithms. 1