Collaborative Variational Autoencoder for Recommender Systems

Li, Xiaopeng; She, James

doi:10.1145/3097983.3098077

Cited by 356 publications

(261 citation statements)

References 14 publications

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“…The CVAE method [23], presented at KDD '18, is a hybrid technique that considers both content as well as rating information. The model learns deep latent representations from content data in an unsupervised manner and also learns implicit relationships between items and users from both content and ratings.…”

Section: Collaborative Variational Autoencoder (Cvae)mentioning

confidence: 99%

Are we really making much progress? A worrying analysis of recent neural recommendation approaches

Dacrema

Cremonesi

Jannach

2019

Proceedings of the 13th ACM Conference on Recommender Systems

413

166

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Deep learning techniques have become the method of choice for researchers working on algorithmic aspects of recommender systems. With the strongly increased interest in machine learning in general, it has, as a result, become difficult to keep track of what represents the state-of-the-art at the moment, e.g., for top-n recommendation tasks. At the same time, several recent publications point out problems in today's research practice in applied machine learning, e.g., in terms of the reproducibility of the results or the choice of the baselines when proposing new models.In this work, we report the results of a systematic analysis of algorithmic proposals for top-n recommendation tasks. Specifically, we considered 18 algorithms that were presented at top-level research conferences in the last years. Only 7 of them could be reproduced with reasonable effort. For these methods, it however turned out that 6 of them can often be outperformed with comparably simple heuristic methods, e.g., based on nearest-neighbor or graph-based techniques. The remaining one clearly outperformed the baselines but did not consistently outperform a well-tuned nonneural linear ranking method. Overall, our work sheds light on a number of potential problems in today's machine learning scholarship and calls for improved scientific practices in this area.

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Section: Collaborative Variational Autoencoder (Cvae)mentioning

confidence: 99%

Are we really making much progress? A worrying analysis of recent neural recommendation approaches

Dacrema

Cremonesi

Jannach

2019

Proceedings of the 13th ACM Conference on Recommender Systems

413

166

View full text Add to dashboard Cite

show abstract

“…The predictive factor D is tested from [20,50,100] and set as 100 for all datasets. In our model, many components use dropout and L2 regularization.…”

Section: Reproducibilitymentioning

confidence: 99%

A Hierarchical Self-Attentive Model for Recommending User-Generated Item Lists

Wang

Niu

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

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User-generated item lists are a popular feature of many different platforms. Examples include lists of books on Goodreads, playlists on Spotify and YouTube, collections of images on Pinterest, and lists of answers on question-answer sites like Zhihu. Recommending item lists is critical for increasing user engagement and connecting users to new items, but many approaches are designed for the item-based recommendation, without careful consideration of the complex relationships between items and lists. Hence, in this paper, we propose a novel user-generated list recommendation model called AttList. Two unique features of AttList are careful modeling of (i) hierarchical user preference, which aggregates items to characterize the list that they belong to, and then aggregates these lists to estimate the user preference, naturally fitting into the hierarchical structure of item lists; and (ii) item and list consistency, through a novel self-attentive aggregation layer designed for capturing the consistency of neighboring items and lists to better model user preference. Through experiments over three real-world datasets reflecting different kinds of user-generated item lists, we find that AttList results in significant improvements in NDCG, Precision@k, and Recall@k versus a suite of state-ofthe-art baselines. Furthermore, all code and data are available at https://github.com/heyunh2015/AttList.

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“…Then the persona embeddings can be directly incorporated into the tips generation component as context information. Specifically, we employ variational auto-encoders (VAEs) [12] (which show strong capability in modeling latent random variables [20,22]) to conduct persona modeling, and we regard the latent variables of VAEs as the persona embeddings. In the context of user behaviour analysis of online retailing, another indispensable party is the product item.…”

Section: Introductionmentioning

confidence: 99%

Persona-Aware Tips Generation?

Wang

Lam

2019

The World Wide Web Conference

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Tips, as a compacted and concise form of reviews, were paid less attention by researchers. In this paper, we investigate the task of tips generation by considering the "persona" information which captures the intrinsic language style of the users or the different characteristics of the product items. In order to exploit the persona information, we propose a framework based on adversarial variational auto-encoders (aVAE) for persona modeling from the historical tips and reviews of users and items. The latent variables from aVAE are regarded as persona embeddings. Besides representing persona using the latent embeddings, we design a persona memory for storing the persona related words for users and items. Pointer Network is used to retrieve persona wordings from the memory when generating tips. Moreover, the persona embeddings are used as latent factors by a rating prediction component to predict the sentiment of a user over an item. Finally, the persona embeddings and the sentiment information are incorporated into a recurrent neural networks based tips generation component. Extensive experimental results are reported and discussed to elaborate the peculiarities of our framework.

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Collaborative Variational Autoencoder for Recommender Systems

Cited by 356 publications

References 14 publications

Are we really making much progress? A worrying analysis of recent neural recommendation approaches

Are we really making much progress? A worrying analysis of recent neural recommendation approaches

A Hierarchical Self-Attentive Model for Recommending User-Generated Item Lists

Persona-Aware Tips Generation?

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