Modelling Contextual Information in Session-Aware Recommender Systems with Neural Networks

Twardowski, Bartłomiej

doi:10.1145/2959100.2959162

Cited by 85 publications

(62 citation statements)

References 6 publications

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“…[28], [30], [62]-[68] GNN [32] IB RNN [17], [73], [74], [76], [ Figure 10 shows the training and testing process of a sequential recommender system. In the training, the input includes raw data and label information, which are then fed into the data processing module, mainly including feature extraction and data augmentation.…”

Section: Discussionmentioning

confidence: 99%

“…For example, BPR-MF [16] optimizes a pairwise ranking objective function via stochastic gradient descent (SGD). Twardowski [17] proposed a MF-based sequential recommender system (a simplified version of Factorization Machines [18]), where only the interaction between a session and a candidate item is considered for prediction. FPMC [19] is a representative baseline for nextbasket recommendation, which integrates MF with firstorder MCs.…”

Section: Traditional Methodsmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Traditional Methodsmentioning

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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“…Contextual information was used in combination with RNNs, for example, in [70] or [71]. In [70], the authors consider not only the sequence of events when making predictions but also the type of the event, the time gaps between events, or the time of the day of an event, leading to what they call Contextual Recurrent Neural Networks for Recommendation (CRNN).…”

Section: ) Deep Learning For Session-based Recommendationmentioning

confidence: 99%

Contextual Hybrid Session-Based News Recommendation With Recurrent Neural Networks

2019

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Recommender systems help users deal with information overload by providing tailored item suggestions to them. The recommendation of news is often considered to be challenging, since the relevance of an article for a user can depend on a variety of factors, including the user's short-term reading interests, the reader's context, or the recency or popularity of an article. Previous work has shown that the use of Recurrent Neural Networks is promising for the next-in-session prediction task, but has certain limitations when only recorded item click sequences are used as input. In this work, we present a contextual hybrid, deep learning based approach for session-based news recommendation that is able to leverage a variety of information types. We evaluated our approach on two public datasets, using a temporal evaluation protocol that simulates the dynamics of a news portal in a realistic way. Our results confirm the benefits of considering additional types of information, including article popularity and recency, in the proposed way, resulting in significantly higher recommendation accuracy and catalog coverage than other session-based algorithms. Additional experiments show that the proposed parameterizable loss function used in our method also allows us to balance two usually conflicting quality factors, accuracy and novelty.

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“…It measures the system's ability to reject any nonrelevant document in the retrieved set. [36,37,50,55,71,74,75,77,78,80,82,90,92,100,[103][104][105][106][107][109][110][111]127,132,[135][136][137]160,161,164,165] Recall To measure the proportion of relevant documents that are retrieved…”

Section: Evaluation Techniquesmentioning

confidence: 99%

Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction

et al. 2017

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Intelligent data handling techniques are beneficial for users; to store, process, analyze and access the vast amount of information produced by electronic and automated devices. The leading approach is to use recommender systems (RS) to extract relevant information from the vast amount of knowledge. However, early recommender systems emerged without the cognizance to contextualize information regarding users' recommendations. Considering the historical methodological limitations, Context-Aware Recommender Systems (CARS) are now deployed, which leverage contextual information in addition to the classical two-dimensional search processes, providing better-personalized user recommendations. This paper presents a review of recent developmental processes as a fountainhead for the research of a context-aware recommender system. This work contributes by taking an integrated approach to the complete CARS developmental process, unlike other review papers, which only address a specific aspect of the CARS process. First, an in-depth review is presented pertaining to the state-of-the-art and classified literature, considering the domain of the application models, filters, extraction and evaluation approaches. Second, viewpoints are presented relating to the extraction of literature with analysis on the merit and demerit of each, and the evolving processes between them. Finally, the outstanding challenges and opportunities for future research directions are highlighted.

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Modelling Contextual Information in Session-Aware Recommender Systems with Neural Networks

Cited by 85 publications

References 6 publications

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

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

Contextual Hybrid Session-Based News Recommendation With Recurrent Neural Networks

Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction

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