A Survey on Session-based Recommender Systems

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

doi:10.1145/3465401

Cited by 309 publications

(133 citation statements)

References 96 publications

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“…In addition, the recent work on sequential recommendation (such as next-item, next-basket and next-song [ 4 , 35 , 36 ] recommendation) and interactive recommendation [ 52 ] also involves contextual information. They typically apply neural networks and the attention mechanism [ 53 ] to model contextual information related to the current object.…”

Section: Discussionmentioning

confidence: 99%

Personalized next-best action recommendation with multi-party interaction learning for automated decision-making

Cao

Zhu

2022

PLoS ONE

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Automated next-best action recommendation for each customer in a sequential, dynamic and interactive context has been widely needed in natural, social and business decision-making. Personalized next-best action recommendation must involve past, current and future customer demographics and circumstances (states) and behaviors, long-range sequential interactions between customers and decision-makers, multi-sequence interactions between states, behaviors and actions, and their reactions to their counterpart’s actions. No existing modeling theories and tools, including Markovian decision processes, user and behavior modeling, deep sequential modeling, and personalized sequential recommendation, can quantify such complex decision-making on a personal level. We take a data-driven approach to learn the next-best actions for personalized decision-making by a reinforced coupled recurrent neural network (CRN). CRN represents multiple coupled dynamic sequences of a customer’s historical and current states, responses to decision-makers’ actions, decision rewards to actions, and learns long-term multi-sequence interactions between parties (customer and decision-maker). Next-best actions are then recommended on each customer at a time point to change their state for an optimal decision-making objective. Our study demonstrates the potential of personalized deep learning of multi-sequence interactions and automated dynamic intervention for personalized decision-making in complex systems.

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Section: Discussionmentioning

confidence: 99%

Personalized next-best action recommendation with multi-party interaction learning for automated decision-making

Cao

Zhu

2022

PLoS ONE

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“…[22] shows a description of Netflix's recommenders and their usual evaluation scenario, but it does not report a detailed analysis of data processing steps. [12] presents a survey of the current state-of-the-art recommendation techniques, including intent-based ones. However, the authors solely describe these techniques and they do not perform an offline evaluation.…”

Section: Related Workmentioning

confidence: 99%

From Data Analysis to Intent-Based Recommendation: An Industrial Case Study in the Video Domain

et al. 2022

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This work presents a comprehensive study, from an industrial perspective, of the process between the collection of raw data, and the generation of next-item recommendation, in the domain of Video-on-Demand (VoD). Most research papers focus their efforts on analyzing recommender systems on already-processed datasets, but they do not face the same challenges that occur naturally in industry, e.g., processing raw interactions logs to create datasets for testing. This paper describes the whole process between data collection and recommendation, including cleaning, processing, feature engineering, session inferring, and all the challenges that a dataset provided by an industrial player in the domain posed. Then, a comparison on the new dataset of several intent-based recommendation techniques in the nextitem recommendation task follows, studying the impact of different factors like the session length, and the number of previous sessions available for a user. The results show that taking advantage of the sequential data available in the dataset benefits recommendation quality, since deep learning algorithms for sessionaware recommendation are consistently the most accurate recommenders. Lastly, a summary of the different challenges in the VoD domain is proposed, discussing on the best algorithmic solutions found, and proposing future research directions to be conducted based on the results obtained.

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“…Multi-session-based SBRSs incorporate other sessions to complement the information in the current session for next-item recommendations in it. Some multi-session-based SBRSs, e.g., session-aware recommender systems [5,21], incorporate the historical sessions of the current user. For example, both hierarchical RNN (HRNN) [14] and inter-and intra-session RNNs (II-RNN) [15] first employ a session-level RNN and an item-level RNN to encode a sequence of historical sessions of the current user and a sequence of items in the current session, respectively, and then combines the outputs from both RNNs to predict the next item in the current session.…”

Section: Related Workmentioning

confidence: 99%

“…recommendations [3,21]. Specifically, given a user's session context, e.g., a few selected items in an online transaction or a shopping basket [25], an SBRS aims to predict the next item in the same session that the user may prefer.…”

Section: Introductionmentioning

confidence: 99%

Next-item Recommendations in Short Sessions

Song

Wang

et al. 2021

Fifteenth ACM Conference on Recommender Systems

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The changing preferences of users towards items trigger the emergence of session-based recommender systems (SBRSs), which aim to model the dynamic preferences of users for next-item recommendations. However, most of the existing studies on SBRSs are based on long sessions only for recommendations, ignoring short sessions, though short sessions, in fact, account for a large proportion in most of the real-world datasets. As a result, the applicability of existing SBRSs solutions is greatly reduced. In a short session, quite limited contextual information is available, making the next-item recommendation very challenging. To this end, in this paper, inspired by the success of few-shot learning (FSL) in effectively learning a model with limited instances, we formulate the next-item recommendation as an FSL problem. Accordingly, following the basic idea of a representative approach for FSL, i.e., meta-learning, we devise an effective SBRS called INter-SEssion collaborative Recommender neTwork (INSERT) for next-item recommendations in short sessions. With the carefully devised local module and global module, INSERT is able to learn an optimal preference representation of the current user in a given short session. In particular, in the global module, a similar session retrieval network (SSRN) is designed to find out the sessions similar to the current short session from the historical sessions of both the current user and other users, respectively. The obtained similar sessions are then utilized to complement and optimize the preference representation learned from the current short session by the local module for more accurate next-item recommendations in this short session. Extensive experiments conducted on two real-world datasets demonstrate the superiority of our proposed INSERT over the state-of-the-art SBRSs when making next-item recommendations in short sessions.

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A Survey on Session-based Recommender Systems

Cited by 309 publications

References 96 publications

Personalized next-best action recommendation with multi-party interaction learning for automated decision-making

Personalized next-best action recommendation with multi-party interaction learning for automated decision-making

From Data Analysis to Intent-Based Recommendation: An Industrial Case Study in the Video Domain

Next-item Recommendations in Short Sessions

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