Latent Factor Transition for Dynamic Collaborative Filtering

Zhang, Chengyi; Wang, Ke; Yu, Hongkun; Sun, Jun; Lim, Ee Peng

doi:10.1137/1.9781611973440.52

Cited by 49 publications

(38 citation statements)

References 16 publications

(25 reference statements)

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“…Each user u is associated with a sequence of some items from I, S u = (S u 1 , · · · , S u |S u | ), where S u i ∈ I. The index t for S u t denotes the order in which an action occurs in the sequence S u , not the absolute timestamp as in temporal recommendation like [14,31,34]. Given all users' sequences S u , the goal is to recommend each user a list of items that maximize her/his future needs, by considering both general preferences and sequential patterns.…”

Section: Top-n Sequential Recommendationmentioning

confidence: 99%

Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding

Tang

Wang

2018

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

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1,278

898

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Top-N sequential recommendation models each user as a sequence of items interacted in the past and aims to predict top-N ranked items that a user will likely interact in a "near future". The order of interaction implies that sequential patterns play an important role where more recent items in a sequence have a larger impact on the next item. In this paper, we propose a Convolutional Sequence Embedding Recommendation Model (Caser) as a solution to address this requirement. The idea is to embed a sequence of recent items into an "image" in the time and latent spaces and learn sequential patterns as local features of the image using convolutional lters. This approach provides a uni ed and exible network structure for capturing both general preferences and sequential patterns. The experiments on public data sets demonstrated that Caser consistently outperforms state-of-the-art sequential recommendation methods on a variety of common evaluation metrics.

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Section: Top-n Sequential Recommendationmentioning

confidence: 99%

Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding

Tang

Wang

2018

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

Self Cite

1,278

898

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“…two important lines of work that seek to mine users' interaction histories: temporal recommendation and sequential recommendation. Temporal recommendation [17,29,31,35] focuses on modeling absolute timestamps to capture the temporal dynamics of users and items. For example, the popularity of an item might change during different time slots, or users' average ratings might increase or decrease over time.…”

mentioning

confidence: 99%

Time Interval Aware Self-Attention for Sequential Recommendation

Wang

McAuley

2020

Proceedings of the 13th International Conference on Web Search and Data Mining

395

245

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Sequential recommender systems seek to exploit the order of users' interactions, in order to predict their next action based on the context of what they have done recently. Traditionally, Markov Chains (MCs), and more recently Recurrent Neural Networks (RNNs) and Self Attention (SA) have proliferated due to their ability to capture the dynamics of sequential patterns. However a simplifying assumption made by most of these models is to regard interaction histories as ordered sequences, without regard for the time intervals between each interaction (i.e., they model the time-order but not the actual timestamp). In this paper, we seek to explicitly model the timestamps of interactions within a sequential modeling framework to explore the influence of different time intervals on next item prediction. We propose TiSASRec (Time Interval aware Self-attention based sequential recommendation), which models both the absolute positions of items as well as the time intervals between them in a sequence. Extensive empirical studies show the features of TiSASRec under different settings and compare the performance of self-attention with different positional encodings. Furthermore, experimental results show that our method outperforms various state-of-the-art sequential models on both sparse and dense datasets and different evaluation metrics. CCS CONCEPTS• Information systems → Retrieval models and ranking.

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“…Kannan [20] proposed a bounded matrix factorization method which imposed a lower and an upper bound on every estimated missing element of the rating matrix. The matrix factorization is quite a flexible method, that it allows incorporation of additional information, such as time factor [24,9,49,36], geographical information [32,50,13,33] and social information [40,8,51,46]. For instance, Koren [24] investigated the temporal dynamics of customer preferences and modeled the temporal dynamics along the whole time period.…”

Section: Single-criteria Recommender Systemsmentioning

confidence: 99%

Preference modeling by exploiting latent components of ratings

et al. 2018

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Understanding user preference is essential to the optimization of recommender systems. As a feedback of user's taste, rating scores can directly reflect the preference of a given user to a given product. Uncovering the latent components of user ratings is thus of significant importance for learning user interests. In this paper, a new recommendation approach, called LCR, was proposed by investigating the latent components of user ratings. The basic idea is to decompose an existing rating into several components via a cost-sensitive learning strategy. Specifically, each rating is assigned to several latent factor models and each model is updated according to its predictive errors. Afterwards, these accumulated predictive errors of models are utilized to decompose a rating into several components, each of which is treated as an independent part to retrain the latent factor models. Finally, all latent factor models are combined linearly to estimate predictive ratings for users. In contrast to existing methods, LCR provides an intuitive preference modeling strategy via multiple component analysis at an individual perspective. Meanwhile, it is verified by the experimental results on several benchmark datasets that the proposed method is superior to the state-of-art methods in terms of recommendation accuracy.

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Latent Factor Transition for Dynamic Collaborative Filtering

Cited by 49 publications

References 16 publications

Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding

Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding

Time Interval Aware Self-Attention for Sequential Recommendation

Preference modeling by exploiting latent components of ratings

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