BPRH: Bayesian personalized ranking for heterogeneous implicit feedback

Qiu, Hao; Liu, Yun; Guo, Guibing; Sun, Zhu; Zhang, Jie; Nguyễn, Hải Thanh

doi:10.1016/j.ins.2018.04.027

Cited by 80 publications

(35 citation statements)

References 34 publications

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“…For example, Loni et al [37] proposed multi-channel BRP to adapt the sampling rule for different behavior types. Qiu et al [38] further proposed an adaptive sampling method for BPR by considering the cooccurrence of multiple behavior types. Guo et al [39] aimed to resolve the data sparsity problem by sampling unobserved items as positive items based on item-item similarity, which is calculated by multiple behavior types.…”

Section: Experience-based Sequential Recommendationmentioning

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: Experience-based Sequential Recommendationmentioning

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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“…Later, some researchers attempted to employ auxiliary behaviors (e.g., view, click ) in addition to target behavior for the performance-enhanced recommendation. For instance, Qiu et al designed TBPR/BPRH [13,14] to utilize purchase, view and like for trinity preference ranking. Ding et al proposed VALS [1] by fusing purchase with view through manually pre-defined weights.…”

Section: Related Workmentioning

confidence: 99%

“…Hence, the auxiliary module mainly takes advantage of auxiliary behaviors for a more fine-grained user preference inference. Existing studies either ignore the temporal dynamics of auxiliary behaviors [1,3,13,14], or pre-define a hard rule to order them [2,10]. Besides, all of them overlook the repeated behaviors.…”

Section: Different Modules Of Tdrbmentioning

confidence: 99%

“…The above examples highlight the presence of temporal dynamics and repeated behaviors in user-item interactions, which could potentially facilitate to model user preference, thus achieving better recommendations. They, however, are not well investigated by existing HIF based studies: (1) most methods [1,5,[11][12][13][14] directly ignore the temporal dynamics. They only model the influence of limited types of user behaviors (e.g., only view and purchase) via a weighted combination, which heavily restricts the generability of these methods; (2) although several approaches [2,10] notice the temporal dynamics, they simply pre-define a hard rule to order various user behaviors, thus failing to capture the real temporal dynamics.…”

Section: Introductionmentioning

confidence: 99%

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Modelling Temporal Dynamics and Repeated Behaviors for Recommendation

Zhou

Sun

Guo

et al. 2020

Advances in Knowledge Discovery and Data Mining

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Personalized recommendation has yield immense success in predicting user preference with heterogeneous implicit feedback (HIF), i.e., various user behaviors. However, existing studies consider less about the temporal dynamics and repeated patterns of HIF. They simply suppose:(1) a hard rule among user behaviors (e.g., add-to-cart must come before purchase and after view ); (2) merge repeated behaviors into one (e.g., view several times is considered as view once only), thus failing to unveil user preferences from their real behaviors. To ease these issues, we, therefore, propose a novel end-to-end neural framework -TDRB, which automatically models the Temporal Dynamics and Repeated Behaviors to assist in capturing user preference, thus achieving more accurate recommendations. Empirical studies on three real-world datasets demonstrate the superiority of our proposed TDRB against other state-of-the-arts.

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“…Personalized ranking is another kind of ranking research area, which ranks nodes from a specified node's view, such as Personalized PageRank [16] and Bayesian Personalized Ranking [17].…”

Section: Related Workmentioning

confidence: 99%

SignRank: A Novel Random Walking Based Ranking Algorithm in Signed Networks

Wei

Fang²,

et al. 2019

Wireless Communications and Mobile Computing

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Social networks have become an indispensable part of modern life. Signed networks, a class of social network with positive and negative edges, are becoming increasingly important. Many social networks have adopted the use of signed networks to model like (trust) or dislike (distrust) relationships. Consequently, how to rank nodes from positive and negative views has become an open issue of social network data mining. Traditional ranking algorithms usually separate the signed network into positive and negative graphs so as to rank positive and negative scores separately. However, much global information of signed network gets lost during the use of such methods, e.g., the influence of a friend’s enemy. In this paper, we propose a novel ranking algorithm that computes a positive score and a negative score for each node in a signed network. We introduce a random walking model for signed network which considers the walker has a negative or positive emotion. The steady state probability of the walker visiting a node with negative or positive emotion represents the positive score or negative score. In order to evaluate our algorithm, we use it to solve sign prediction problem, and the result shows that our algorithm has a higher prediction accuracy compared with some well-known ranking algorithms.

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BPRH: Bayesian personalized ranking for heterogeneous implicit feedback

Cited by 80 publications

References 34 publications

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

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

Modelling Temporal Dynamics and Repeated Behaviors for Recommendation

SignRank: A Novel Random Walking Based Ranking Algorithm in Signed Networks

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