Collaborative topic regression for online recommender systems: an online and Bayesian approach

Liu, Chenghao; Jin, Tao; Hoi, Steven C. H.; Zhao, Peilin; Sun, Jun

doi:10.1007/s10994-016-5599-z

Cited by 37 publications

(19 citation statements)

References 20 publications

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“…Social Recommendation. The data sparsity and cold start problem are two important reasons deteriorating the performance of traditional recommender systems (Liu et al 2017).…”

Section: Related Workmentioning

confidence: 99%

Discrete Social Recommendation

Liu

Wang

et al. 2019

AAAI

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Social recommendation, which aims at improving the performance of traditional recommender systems by considering social information, has attracted broad range of interests. As one of the most widely used methods, matrix factorization typically uses continuous vectors to represent user/item latent features. However, the large volume of user/item latent features results in expensive storage and computation cost, particularly on terminal user devices where the computation resource to operate model is very limited. Thus when taking extra social information into account, precisely extracting K most relevant items for a given user from massive candidates tends to consume even more time and memory, which imposes formidable challenges for efficient and accurate recommendations. A promising way is to simply binarize the latent features (obtained in the training phase) and then compute the relevance score through Hamming distance. However, such a two-stage hashing based learning procedure is not capable of preserving the original data geometry in the real-value space and may result in a severe quantization loss. To address these issues, this work proposes a novel discrete social recommendation (DSR) method which learns binary codes in a unified framework for users and items, considering social information. We further put the balanced and uncorrelated constraints on the objective to ensure the learned binary codes can be informative yet compact, and finally develop an efficient optimization algorithm to estimate the model parameters. Extensive experiments on three real-world datasets demonstrate that DSR runs nearly 5 times faster and consumes only with 1/37 of its real-value competitor’s memory usage at the cost of almost no loss in accuracy.

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“…Social Recommendation. The data sparsity and cold start problem are two important reasons deteriorating the performance of traditional recommender systems (Liu et al 2017).…”

Section: Related Workmentioning

confidence: 99%

Discrete Social Recommendation

Liu

Wang

et al. 2019

AAAI

Self Cite

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“…Specifically, the 1628 items in the dataset were clustered under the circumstances of 20 nearest neighbors. We analyzed the impact of different clusters (20,30,40) on the search efficiency, the results are shown in Figure 5. Y-coordinate denotes the ratio of searched nearest neighbors of items, and X-coordinate denotes the percentage of items that have been searched.…”

Section: Effectiveness Evaluationmentioning

confidence: 99%

“…On the other hand, many studies have proven that online recommendation systems using hybrid recommendation approaches can achieve enhanced effects. Hybrid approaches mainly include collaborative filtering, demographic filtering [14], location information [15,16], clustering [17,18], content filtering [19] and Bayesian networks [20].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Two-Phase Recommendation for Group-Buying E-commerce Applications

Bai

et al. 2019

Applied Sciences

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The last two decades have witnessed an explosive growth of e-commerce applications. Existing online recommendation systems for e-commerce applications, particularly group-buying applications, suffer from scalability and data sparsity problems when confronted with exponentially increasing large-scale data. This leads to a poor recommendation effect of traditional collaborative filtering (CF) methods in group-buying applications. In order to address this challenge, this paper proposes a hybrid two-phase recommendation (HTPR) method which consists of offline preparation and online recommendation, combining clustering and collaborative filtering techniques. The user-item category tendency matrix is constructed after clustering items, and then users are clustered to facilitate personalized recommendation where items are generated by collaborative filtering technology. In addition, a parallelized strategy was developed to optimize the recommendation process. Extensive experiments on a real-world dataset were conducted by comparing HTPR with other three recommendation methods: traditional CF, user-clustering based CF, and item-clustering based CF. The experimental results show that the proposed HTPR method is effective and can improve the accuracy of online recommendation systems for group-buying applications.

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“…The benchmark datasets include: CiteULike (Wang and Blei 2011), MovPlot1M and MovPlot10M (Liu et al 2017). Ci-teULike contains users' bookmarks of scientific articles and paper abstracts.…”

Section: Datasetsmentioning

confidence: 99%

“…Firstly, most existing methods fail to simultaneously capture topic semantics and word order information, since they belong to different semantic hierarchies. For topic-aware encoders, the content features are extracted by models such as Latent Dirichlet Allocation and autoencoder (Liu et al 2017;Gopalan, Charlin, and Blei 2014;Wang, Wang, and Yeung 2015). Since these models are built upon the "bagof-words" assumption, they are not sensitive to the order of words.…”

Section: Introductionmentioning

confidence: 99%

CAMO: A Collaborative Ranking Method for Content Based Recommendation

Wang

Zhou

Chen

et al. 2019

AAAI

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In real-world recommendation tasks, feedback data are usually sparse. Therefore, a recommender's performance is often determined by how much information that it can extract from textual contents. However, current methods do not make full use of the semantic information. They encode the textual contents either by "bag-of-words" technique or Recurrent Neural Network (RNN). The former neglects the order of words while the latter ignores the fact that textual contents can contain multiple topics. Besides, there exists a dilemma in designing a recommender. On the one hand, we shall use a sophisticated model to exploit every drop of information in item contents; on the other hand, we shall adopt a simple model to prevent itself from over-fitting when facing the sparse feedbacks. To fill the gaps, we propose a recommender named CAMO 1 . CAMO employs a multi-layer content encoder for simultaneously capturing the semantic information of multitopic and word order. Moreover, CAMO makes use of adversarial training to prevent the complex encoder from overfitting. Extensive empirical studies show that CAMO outperforms state-of-the-art methods in predicting users' preferences.

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Collaborative topic regression for online recommender systems: an online and Bayesian approach

Cited by 37 publications

References 20 publications

Discrete Social Recommendation

Discrete Social Recommendation

A Hybrid Two-Phase Recommendation for Group-Buying E-commerce Applications

CAMO: A Collaborative Ranking Method for Content Based Recommendation

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