A Survey on Session-based Recommender Systems

Wang, Shoujin; Cao, Longbing; Wang, Yan; Sheng, Quan Z.; Orgun, Mehmet A.; Lian, Defu

doi:10.48550/arxiv.1902.04864

Cited by 29 publications

(17 citation statements)

References 209 publications

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“…The formulation of top-N session-based recomendation in this paper clozely follows that in NextItNet [28]. In SRS, the concept "session" is defined as a collection of items (referring to any objects e.g., videos, songs or queries) that happened at one time or in a certain period of time [10,24]. For instance, both a list of browsed webpages and a collection of watched videos consumed in a hour by a user can be regarded as a session.…”

Section: Top-n Session-based Recommendationmentioning

confidence: 99%

Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation

Yuan

Jiang

et al. 2020

Proceedings of the Web Conference 2020

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Long session-based recommender systems have attacted much attention recently. For each user, they may create hundreds of click behaviors in short time. To learn long session item dependencies, previous sequential recommendation models resort either to data augmentation or a left-to-right autoregressive training approach. While effective, an obvious drawback is that future user behaviors are always mising during training. In this paper, we claim that users' future action signals can be exploited to boost the recommendation quality. To model both past and future contexts, we investigate three ways of augmentation techniques from both data and model perspectives. Moreover, we carefully design two general neural network architectures: a pretrained two-way neural network model and a deep contextualized model trained on a text gap-filling task. Experiments on four real-word datasets show that our proposed two-way neural network models can achieve competitive or even much better results. Empirical evidence confirms that modeling both past and future context is a promising way to offer better recommendation accuracy.

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Section: Top-n Session-based Recommendationmentioning

confidence: 99%

Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation

Yuan

Jiang

et al. 2020

Proceedings of the Web Conference 2020

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“…It is positive that the above models based on general graph embedding that enhances the recommendation by employing side information have also made successful attempts in the industry. For instance, by taking the records of user's session-based online activities in Taobao as side information, Alibaba [307] constructed a weighted and directed network, which is comprised of user-item interactions in a continuous period [308] and can be employed in extracting hidden consumption habits of users to promote recommendation accuracy. In addition, SHINE [309] simultaneously takes the semantic networks, social networks, and profile networks of users as side information, using them to predict the sign of a sentiment link (i.e., user's attitudes towards an item) without analyzing textual information like user's comments on items.…”

Section: Recommendation Involving Side Informationmentioning

confidence: 99%

Recommender systems based on graph embedding techniques: A comprehensive review

Deng

2021

Preprint

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Recommender systems, a pivotal tool to alleviate the information overload problem, aim to predict user's preferred items from millions of candidates by analyzing observed user-item relations. As for tackling the sparsity and cold start problems encountered by recommender systems, uncovering hidden (indirect) user-item relations by employing side information and knowledge to enrich observed information for the recommendation has been proven promising recently; and its performance is largely determined by the scalability of recommendation models in the face of the high complexity and large scale of side information and knowledge. Making great strides towards efficiently utilizing complex and large-scale data, research into graph embedding techniques is a major topic. Equipping recommender systems with graph embedding techniques contributes to outperforming the conventional recommendation implementing directly based on graph topology analysis and has been widely studied these years. This article systematically retrospects graph embedding-based recommendation from embedding techniques for bipartite graphs, general graphs, and knowledge graphs, and proposes a general design pipeline of that. In addition, comparing several representative graph embedding-based recommendation models with the most common-used conventional recommendation models, on simulations, manifests that the conventional models overall outperform the graph embedding-based ones in predicting implicit user-item interactions, revealing the relative weakness of graph embedding-based recommendation in these tasks. To foster future research, this article proposes constructive suggestions on making a trade-off between graph embedding-based recommendation and the conventional recommendation in different tasks as well as some open questions.

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“…For sequential recommendation, Quadrana et al [6] proposed a categorization of the recommendation tasks and goals, and summarized existing solutions. Wang et al [7] illustrated the value and significance of the session-based recommender systems (SBRS), and proposed a hierarchical framework to categorize issues and methods, including some DL-based ones.…”

Section: Related Surveymentioning

confidence: 99%

Deep Learning-Based Sequential Recommender Systems: Concepts, Algorithms, and Evaluations

Fang

Guo

Zhang

et al. 2019

Lecture Notes in Computer Science

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In the field of sequential recommendation, deep learning methods have received a lot of attention in the past few years and surpassed traditional models such as Markov chain-based and factorization-based ones. However, DL-based methods also have some critical drawbacks, such as insufficient modeling of user representation and ignoring to distinguish the different types of interactions (i.e., user behavior) among users and items. In this view, this survey focuses on DL-based sequential recommender systems by taking the aforementioned issues into consideration. Specifically, we illustrate the concept of sequential recommendation, propose a categorization of existing algorithms in terms of three types of behavioral sequence, summarize the key factors affecting the performance of DL-based models, and conduct corresponding evaluations to demonstrate the effects of these factors. We conclude this survey by systematically outlining future directions and challenges in this field.Index Terms-sequential recommendation, session-based recommendation, sequential data, deep learning, influential factors ! • Hui Fang is with the 1. We searched on arXiv.org with keywords related to the sequential recommendation and DL techniques in March 2019.2. Fig. 3: A schematic diagram of the sequence recommendation. c i : behavior type, o i : behavior object. A behavior is represented by a 2-tuple. A behavior sequence (i.e., behavior trajectory) is a time ordered list of 2-tuples.The input behavior sequence {a 1 , a 2 , a 3 , ..., a t } is polymorphic, which can thus be divided into three types: experience-based, transaction-based and interaction-based behavior sequence, whose details are introduced as follows:

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A Survey on Session-based Recommender Systems

Cited by 29 publications

References 209 publications

Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation

Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation

Recommender systems based on graph embedding techniques: A comprehensive review

Deep Learning-Based Sequential Recommender Systems: Concepts, Algorithms, and Evaluations

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