When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation

Jannach, Dietmar; Ludewig, Malte

doi:10.1145/3109859.3109872

Cited by 388 publications

(269 citation statements)

References 32 publications

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“…In contrast, session-based KNN [10], [12], [13] compares the whole session with past sessions to recommend items, calculating similarities by Jaccard index or cosine similarity on binary vectors over the item space. It can be formulated as follows:…”

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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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: 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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“…It is worth mentioning that our analysis of interaction sessions di ers from session-based recommendation, which analyzes the user's behavior during an interaction session to identify relevant item(s) to suggest; e.g., see [13,16,19,20]. In fact, we mine interest co-occurrence by abstracting from the particular sequence of queries performed by the users.…”

Section: Analysis Of Interaction Sessionsmentioning

confidence: 99%

Extending a Tag-based Collaborative Recommender with Co-occurring Information Interests

Mauro¹,

Ardissono²

2019

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization

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Collaborative Filtering is largely applied to personalize item recommendation but its performance is a ected by the sparsity of rating data. In order to address this issue, recent systems have been developed to improve recommendation by extracting latent factors from the rating matrices, or by exploiting trust relations established among users in social networks.In this work, we are interested in evaluating whether other sources of preference information than ratings and social ties can be used to improve recommendation performance. Speci cally, we aim at testing whether the integration of frequently co-occurring interests in information search logs can improve recommendation performance in User-to-User Collaborative Filtering (U2UCF). For this purpose, we propose the Extended Category-based Collaborative Filtering (ECCF) recommender, which enriches category-based user pro les derived from the analysis of rating behavior with data categories that are frequently searched together by people in search sessions. We test our model using a big rating dataset and a log of a largely used search engine to extract the co-occurrence of interests. e experiments show that ECCF outperforms U2UCF and categorybased collaborative recommendation in accuracy, MRR, diversity of recommendations and user coverage. Moreover, it outperforms the SVD++ Matrix Factorization algorithm in accuracy and diversity of recommendation lists. CCS CONCEPTS•Information systems → Recommender systems; •Humancentered computing → Collaborative Filtering; KEYWORDSTag-based recommender systems; Collaborative Filtering; Categorybased user pro les; Preference co-occurrence in information search.

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“…Despite some advanced recommender systems have been proposed, neighborhood-based collaborative filtering remains one of the most common and effective recommender systems 16,17,18,19 and can be deployed by businesses companies, e.g., Amazons 20 . In addition, the recently proposed research 21 points out that neighborhood-based collaborative filtering outperforms than some advanced deep the collaborative filtering models, (e.g., NCF 22 ).…”

Section: Introductionmentioning

confidence: 99%

Data poisoning attacks on neighborhood‐based recommender systems

Chen

Xie

et al. 2020

Trans Emerging Tel Tech

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Nowadays, collaborative filtering recommender systems have been widely deployed in many commercial companies to make profit. Neighbourhood-based collaborative filtering is common and effective. To date, despite its effectiveness, there has been little effort to explore their robustness and the impact of data poisoning attacks on their performance. Can the neighbourhood-based recommender systems be easily fooled? To this end, we shed light on the robustness of neighbourhood-based recommender systems and propose a novel data poisoning attack framework encoding the purpose of attack and constraint against them. We firstly illustrate how to calculate the optimal data poisoning attack, namely UNAttack. We inject a few well-designed fake users into the recommender systems such that target items will be recommended to as many normal users as possible. Extensive experiments are conducted on three real-world datasets to validate the effectiveness and the transferability of our proposed method. Besides, some interesting phenomenons can be found. For example, 1) neighbourhood-based recommender systems with Euclidean Distance-based similarity have strong robustness. 2) the fake users can be transferred to attack the state-of-the-art collaborative filtering recommender systems such as Neural Collaborative Filtering and Bayesian Personalized Ranking Matrix Factorization.

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When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation

Cited by 388 publications

References 32 publications

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

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

Extending a Tag-based Collaborative Recommender with Co-occurring Information Interests

Data poisoning attacks on neighborhood‐based recommender systems

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