A Joint Neural Network for Session-Aware Recommendation

Guo, Yupu; Zhang, Duolong; Ling, Yanxiang; Chen, Honghui

doi:10.1109/access.2020.2984287

Cited by 14 publications

(8 citation statements)

References 33 publications

(44 reference statements)

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“…• Extend and Boost: We only report the combined effects of these extensions, and we denote the extended method by appending the postfix "_eb" (extend and boost) to the algorithm name, e.g., vsknn_eb. The reason is that the effectiveness of the individual extensions varied across algorithms, but combining the methods in almost all cases led to the highest performance improvements 9 . For the neural methods, gru4rec and narm, these extensions did not lead to positive effects in our initial experiments, which is why we do not report the results here.…”

Section: Resultsmentioning

confidence: 99%

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Session-aware Recommendation: A Surprising Quest for the State-of-the-art

Latifi,

Mauro,

Jannach

2020

Preprint

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Recommender systems are designed to help users in situations of information overload. In recent years, we observed increased interest in session-based recommendation scenarios, where the problem is to make item suggestions to users based only on interactions observed in an ongoing session. However, in cases where interactions from previous user sessions are available, the recommendations can be personalized according to the users' long-term preferences, a process called session-aware recommendation. Today, research in this area is scattered and many existing works only compare session-aware with session-based models. This makes it challenging to understand what represents the state-of-the-art. To close this research gap, we benchmarked recent session-aware algorithms against each other and against a number of session-based recommendation algorithms and trivial extensions thereof. Our comparison, to some surprise, revealed that (i) simple techniques based on nearest neighbors consistently outperform recent neural techniques and that (ii) session-aware models were mostly not better than approaches that do not use long-term preference information. Our work therefore not only points to potential methodological issues where new methods are compared to weak baselines, but also indicates that there remains a huge potential for more sophisticated session-aware recommendation algorithms.

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Section: Resultsmentioning

confidence: 99%

“…Since we are given user-IDs for the sessions, we are able to apply a user-wise data splitting approach. Specifically, like in[31,33,30,9,47],…”

mentioning

confidence: 99%

Session-aware Recommendation: A Surprising Quest for the State-of-the-art

Latifi,

Mauro,

Jannach

2020

Preprint

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“…We randomly collect 500,000 recommendation sessions (with 19,667,665 items) in temporal order, and leverage the first 80% sessions as the training/validation datasets and the later 20% sessions as the (offline) test dataset. For a new session, the initial state s 1 is N = 50 previously clicked or purchased items obtained from users' previous sessions [14]. The immediate reward r t of click/skip/leave behavior is empirically set as 1, 0, and -2, respectively.…”

Section: Experimental Settingsmentioning

confidence: 99%

Whole-Chain Recommendations

Zhao

Xia

Zou

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems. In practical recommendation sessions, users will sequentially access multiple scenarios, such as the entrance pages and the item detail pages, and each scenario has its specific characteristics. However, the majority of existing RL-based recommender systems focus on optimizing one strategy for all scenarios or separately optimizing each strategy, which could lead to sub-optimal overall performance. In this paper, we study the recommendation problem with multiple (consecutive) scenarios, i.e., whole-chain recommendations. We propose a multi-agent RL-based approach (DeepChain), which can capture the sequential correlation among different scenarios and jointly optimize multiple recommendation strategies. To be specific, all recommender agents (RAs) share the same memory of users' historical behaviors, and they work collaboratively to maximize the overall reward of a session. Note that optimizing multiple recommendation strategies jointly faces two challenges in the existing model-free RL model [22]-(i) it requires huge amounts of user behavior data, and (ii) the distribution of reward (users' feedback) are extremely unbalanced. In this paper, we introduce model-based RL techniques to reduce the training data requirement and execute more accurate strategy updates. The experimental results based on a real e-commerce platform demonstrate the effectiveness of the proposed framework.

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“…However, when the time series is long, the RNN model experiences the gradient exploding problem and the gradient vanishing problem. Aiming at the problems in the prediction of long time series, the Long-Short Term Memory (LSTM) model introduces the gating mechanism to make up for the deficiency of the RNN model in long time series processing to some extent [28,29].…”

Section: Introductionmentioning

confidence: 99%

A Nonlinear Autoregressive Model with Exogenous Variables for Traffic Flow Forecasting in Smaller Urban Regions

Guan³

2022

PROMTT

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Data-driven forecasting methods have the problems of complex calculations, poor portability and need a large amount of training data, which limits the application of data-driven methods in small cities. This paper proposes a traffic flow forecasting method using a Nonlinear AutoRegressive model with eXogenous variables (NARX model), which uses a dynamic neural network Focused Time-Delay Neural Network (FTDNN) with a Tapped Delay Line (TDL) structure as a nonlinear function. The TDL structure enables the FTDNN to have short-term memory capabilities. At the same time, before the data is input into the FTDNN, the use of trend decomposition or differential calculation on the traffic data sequence can make the NARX model maintain long-term predictive capabilities. Compared with common nonlinear models, the FTDNN has structural advantages. It uses a simple TDL structure without the memory mechanism and the gated structure, which can reduce the parameters of the model and reduce the scale of data. Through the four-day data of Guilin City, the traffic volume forecast for five minutes is verified, and the performance of the NARX model is better than that of the SARIMA model and the Holt-Winters model.

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A Joint Neural Network for Session-Aware Recommendation

Cited by 14 publications

References 33 publications

Session-aware Recommendation: A Surprising Quest for the State-of-the-art

Session-aware Recommendation: A Surprising Quest for the State-of-the-art

Whole-Chain Recommendations

A Nonlinear Autoregressive Model with Exogenous Variables for Traffic Flow Forecasting in Smaller Urban Regions

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